The extent of coupling and cohesion in an object-oriented system has implications for its external quality. Various static coupling and cohesion metrics have been proposed and used in past empirical investigations; however none of these have taken the run-time properties of a program into account. As program behavior is a function of its operational environment as well as the complexity of the source code, static metrics may fail to quantify all the underlying dimensions of coupling and cohesion. In this paper we proposed a novel Class, Object and Inheritance based Coupling Measure (COICM) to find better OOP Paradigm using JAVA. By this approach we find the better OOP paradigm. Our Algorithm consist of four phases 1) Authentication 2) Select two Object Oriented Programming Files 3) Count no of Classes, Object and Inheritance 4)Based on the analysis provided in the database we deduce that which programming approach is better in the current situation. Our simulation result shows that it is efficient and applicable on the entire platform. The metric values of class and inheritance diagrams have been compared to prove which concept is good to use and beneficial for developers.

OOP, OOA, CBO, Inheritance.

Narendra Pal Singh Rathore, Ravindra Gupta.A Novel Class, Object and Inheritance based Coupling Measure (COICM) to Find Better OOP Paradigm using JAVA. International Journal of Advanced Computer Research. 2011;1(1):1-6.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

dsdsdasdasdasdsdsadasdddsdsadasdasdasd

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023;216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014;4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. Ininternational conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016;20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. Inproceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021;2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. InPune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021;6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019;157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. Inextended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023;216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014;4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. Ininternational conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016;20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. Inproceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021;2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. InPune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021;6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019;157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. Inextended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023;216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014;4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. Ininternational conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016;20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. Inproceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021;2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. InPune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021;6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019;157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. Inextended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023;216:13-20.

Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014;4(9):1432-44.

Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. Ininternational conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016;20(5):1281-90.

Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. Inproceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021;2021:1-3.

Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. InPune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021;6(12):1-12.

Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019;157:110-7.

Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. Inextended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

Mahiddin NA, Sarkar NI. Improving the performance of MANET gateway selection scheme for disaster recovery. In18th international conference on high performance computing and communications; 14th international conference on smart city; 2nd international conference on data science and systems (HPCC/SmartCity/DSS) 2016 (pp. 907-12). IEEE.

Khaliq MN, Ouarda TB, Ondo JC, Gachon P, Bobée B. Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: a review. Journal of Hydrology. 2006;329(3-4):534-52.

Hosking JR. L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1990;52(1):105-24.

Tasker G. Regional frequency analysis: an approach based on L-moments. Journal of the American Statistical Association. 1998;93(443):1233.

Shabri A, Ariff NA. Frequency analysis of maximum daily rainfalls via l-moment approach. Sains Malaysiana. 2009;38(2):149-58.

Hamzah FM, Yusoff SH, Jaafar O. L-moment-based frequency analysis of high-flow at Sungai Langat, Kajang, Selangor, Malaysia. Sains Malaysiana. 2019;48(7):1357-66.

Hassan BG, Ping F. Regional rainfall frequency analysis for the Luanhe Basin–by using L-moments and cluster techniques. APCBEE Procedia. 2012;1:126-35.

Krishna GS, Veerendra G. Flood frequency analysis of Prakasam barrage reservoir Krishna district, Andhra Pradesh using Weibull, Gringorten and L-moments formula. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development. 2015;5(2):57-62.

Hailegeorgis TT, Alfredsen K. Regional flood frequency analysis and prediction in ungauged basins including estimation of major uncertainties for mid-Norway. Journal of Hydrology: Regional Studies. 2017;9:104-26.

Mosaffaie J. Comparison of two methods of regional flood frequency analysis by using L-moments. Water Resources. 2015;42:313-21.

Malekinezhad H, Nachtnebel HP, Klik A. Comparing the index-flood and multiple-regression methods using L-moments. Physics and Chemistry of the Earth, Parts A/B/C. 2011;36(1-4):54-60.

Rutkowska A, Żelazny M, Kohnová S, Łyp M, Banasik K. Regional L-moment-based flood frequency analysis in the upper Vistula River basin, Poland. Geoinformatics and Atmospheric Science. 2018:243-63.

Seckin N, Haktanir T, Yurtal R. Flood frequency analysis of Turkey using L‐moments method. Hydrological Processes. 2011;25(22):3499-505.

Vogel RM, Fennessey NM. L moment diagrams should replace product moment diagrams. Water Resources Research. 1993;29(6):1745-52.

Peel MC, Wang QJ, Vogel RM, Mcmahon TA. The utility of L-moment ratio diagrams for selecting a regional probability distribution. Hydrological Sciences Journal. 2001;46(1):147-55.

Haddad K. Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method: a case study for NSW in Australia. Theoretical and Applied Climatology. 2021;143(3):1261-84.

Ouarda TB, Charron C, Chebana F. Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study. Energy Conversion and Management. 2016;124:247-65.

Hosking JR. Some theory and practical uses of trimmed L-moments. Journal of Statistical Planning and Inference. 2007;137(9):3024-39.

Bobee B, Cavadias G, Ashkar F, Bernier J, Rasmussen P. Towards a systematic approach to comparing distributions used in flood frequency analysis. Journal of Hydrology. 1993;142(1-4):121-36.

Murshed MS, Park BJ, Jeong BY, Park JS. LH-moments of some distributions useful in hydrology. Communications for Statistical Applications and Methods. 2009;16(4):647-58.

Junzhen WA, Songbai SO. Study on application of partial L-moments to flood frequency analysis. Journal of Hydroelectric Engineering. 2015;12(1):1-10.

Mudholkar GS, Hutson AD. LQ-moments: analogs of L-moments. Journal of Statistical Planning and Inference. 1998;71(1-2):191-208.

Sung JH, Kim YO, Jeon JJ. Application of distribution-free nonstationary regional frequency analysis based on L-moments. Theoretical and Applied Climatology. 2018;133:1219-33.

Bahmani R, Eslamian S, Khorsandi M, Hosseinipour EZ. Combination of L-moments method and hydrological model for design flood hydrograph determination. Inworld environmental and water resources congress 2013: showcasing the future 2013 (pp. 3236-46).

Koutsoyiannis D. Knowable moments for high-order stochastic characterization and modelling of hydrological processes. Hydrological Sciences Journal. 2019;64(1):19-33.

Agbonaye AI, Otuaro EA, Christopher O. Comparison of L-moment and method of moments as parameter estimators for identification and choice of the most appropriate rainfall distribution models for design of hydraulic structures. Journal of Civil Engineering. 2022;13(1):33-48.

Vivekanandan N. Intercomparison of probability distributions for selecting a best fit for estimation of rainfall. i-Manager's Journal on Civil Engineering. 2022;12(3):42-7.

Vivekanandan N. Intercomparison of estimators of extreme value family of distributions for rainfall frequency analysis. Mausam. 2022;73(1):59-70.

Sanusi W, Chaerunnisa S, Annas S, Side S, Abdy M. Estimated parameters of rain flow distribution using L-moment method in South Sulawesi, Indonesia. Journal of Applied Mathematics and Computation. 2022;6(1):30-40.

Guayjarernpanishk P, Bussababodhin P, Chiangpradit M. The partial L-moment of the four kappa distribution. Emerging Science Journal. 2023;7(4):1116-25.

Anghel CG, Stanca SC, Ilinca C. Two-parameter probability distributions: methods, techniques and comparative analysis. Water. 2023;15(19):1-35.

Chang CH, Rahmad R, Wu SJ, Hsu CT. Spatial frequency analysis by adopting regional analysis with radar rainfall in Taiwan. Water. 2022;14(17):1-27.

Hinis MA, Geyikli MS. Accuracy evaluation of standardized precipitation index (SPI) estimation under conventional assumption in Yeşilırmak, Kızılırmak, and Konya closed Basins, Turkey. Advances in Meteorology. 2023;2023(1):1-13.

Cao S, Lu H, Peng Y, Ren F. A novel fourth-order L-moment reliability method for L-correlated variables. Applied Mathematical Modelling. 2021;95:806-23.

Guttman NB. The use of L-moments in the determination of regional precipitation climates. Journal of Climate. 1993;6(12):2309-25.

Meshgi A, Khalili D. Comprehensive evaluation of regional flood frequency analysis by L-and LH-moments. I.A re-visit to regional homogeneity. Stochastic Environmental Research and Risk Assessment. 2009;23:119-35.

Fuller WE. Closure to flood flows. Transactions of the American Society of Civil Engineers. 1914;77(1):676-94.

Volpi E. On return period and probability of failure in hydrology. Wiley Interdisciplinary Reviews: Water. 2019;6(3):e1340.

Volpi E, Fiori A, Grimaldi S, Lombardo F, Koutsoyiannis D. One hundred years of return period: strengths and limitations. Water Resources Research. 2015;51(10):8570-85.

Fernández B, Salas JD. Return period and risk of hydrologic events. I: mathematical formulation. Journal of Hydrologic Engineering. 1999;4(4):297-307.

Du T, Xiong L, Xu CY, Gippel CJ, Guo S, Liu P. Return period and risk analysis of nonstationary low-flow series under climate change. Journal of Hydrology. 2015;527:234-50.

Gräler B, Van DBMJ, Vandenberghe S, Petroselli A, Grimaldi S, De BB, et al. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Sciences. 2013;17(4):1281-96.

Volpi E, Fiori A, Grimaldi S, Lombardo F, Koutsoyiannis D. Save hydrological observations! return period estimation without data decimation. Journal of Hydrology. 2019;571:782-92.

Shiau JT. Return period of bivariate distributed extreme hydrological events. Stochastic Environmental Research and Risk Assessment. 2003;17:42-57.

Greenwood JA, Landwehr JM, Matalas NC, Wallis JR. Probability weighted moments: definition and relation to parameters of several distributions expressable in inverse form. Water Resources Research. 1979;15(5):1049-54.

https://dominoweb.draco.res.ibm.com/reports/RC12210.pdf. Accessed 30January2025.

Hosking JR. Approximations for use in constructing L-moment ratio diagrams. IBM Research Division, TJ Watson Research Center; 1991.

Guo SL. A discussion on unbiased plotting positions for the general extreme value distribution. Journal of Hydrology. 1990;121(1-4):33-44.

Cook NJ, Harris RI. The Gringorten estimator revisited. Wind & Structures. 2013;16(4):355-72.

Benjamin JR, Cornell CA. Probability, statistics, and decision for civil engineers. Courier Corporation; 2014.

Mahiddin NA, Sarkar NI. Improving the performance of MANET gateway selection scheme for disaster recovery. In 18th international conference on high performance computing and communications; 14th international conference on smart city; 2nd international conference on data science and systems (HPCC/SmartCity/DSS) 2016 (pp. 907-12). IEEE.

Khaliq MN, Ouarda TB, Ondo JC, Gachon P, Bobée B. Frequency analysis of a sequence of dependent and/or non-stationary hydro-meteorological observations: a review. Journal of Hydrology. 2006; 329(3-4):534-52.

Hosking JR. L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1990; 52(1):105-24.

Tasker G. Regional frequency analysis: an approach based on L-moments. Journal of the American Statistical Association. 1998; 93(443):1233.

Shabri A, Ariff NA. Frequency analysis of maximum daily rainfalls via l-moment approach. Sains Malaysiana. 2009; 38(2):149-58.

Hamzah FM, Yusoff SH, Jaafar O. L-moment-based frequency analysis of high-flow at Sungai Langat, Kajang, Selangor, Malaysia. Sains Malaysiana. 2019; 48(7):1357-66.

Hassan BG, Ping F. Regional rainfall frequency analysis for the Luanhe Basin–by using L-moments and cluster techniques. APCBEE Procedia. 2012; 1:126-35.

Krishna GS, Veerendra G. Flood frequency analysis of Prakasam barrage reservoir Krishna district, Andhra Pradesh using Weibull, Gringorten and L-moments formula. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development. 2015; 5(2):57-62.

Hailegeorgis TT, Alfredsen K. Regional flood frequency analysis and prediction in ungauged basins including estimation of major uncertainties for mid-Norway. Journal of Hydrology: Regional Studies. 2017; 9:104-26.

Mosaffaie J. Comparison of two methods of regional flood frequency analysis by using L-moments. Water Resources. 2015; 42:313-21.

Malekinezhad H, Nachtnebel HP, Klik A. Comparing the index-flood and multiple-regression methods using L-moments. Physics and Chemistry of the Earth, Parts A/B/C. 2011; 36(1-4):54-60.

Rutkowska A, Żelazny M, Kohnová S, Łyp M, Banasik K. Regional L-moment-based flood frequency analysis in the upper Vistula River basin, Poland. Geoinformatics and Atmospheric Science. 2018:243-63.

Seckin N, Haktanir T, Yurtal R. Flood frequency analysis of Turkey using L‐moments method. Hydrological Processes. 2011; 25(22):3499-505.

Vogel RM, Fennessey NM. L moment diagrams should replace product moment diagrams. Water Resources Research. 1993; 29(6):1745-52.

Peel MC, Wang QJ, Vogel RM, Mcmahon TA. The utility of L-moment ratio diagrams for selecting a regional probability distribution. Hydrological Sciences Journal. 2001; 46(1):147-55.

Haddad K. Selection of the best fit probability distributions for temperature data and the use of L-moment ratio diagram method: a case study for NSW in Australia. Theoretical and Applied Climatology. 2021; 143(3):1261-84.

Ouarda TB, Charron C, Chebana F. Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study. Energy Conversion and Management. 2016; 124:247-65.

Hosking JR. Some theory and practical uses of trimmed L-moments. Journal of Statistical Planning and Inference. 2007; 137(9):3024-39.

Bobee B, Cavadias G, Ashkar F, Bernier J, Rasmussen P. Towards a systematic approach to comparing distributions used in flood frequency analysis. Journal of Hydrology. 1993; 142(1-4):121-36.

Murshed MS, Park BJ, Jeong BY, Park JS. LH-moments of some distributions useful in hydrology. Communications for Statistical Applications and Methods. 2009; 16(4):647-58.

Junzhen WA, Songbai SO. Study on application of partial L-moments to flood frequency analysis. Journal of Hydroelectric Engineering. 2015; 12(1):1-10.

Mudholkar GS, Hutson AD. LQ-moments: analogs of L-moments. Journal of Statistical Planning and Inference. 1998; 71(1-2):191-208.

Sung JH, Kim YO, Jeon JJ. Application of distribution-free nonstationary regional frequency analysis based on L-moments. Theoretical and Applied Climatology. 2018; 133:1219-33.

Bahmani R, Eslamian S, Khorsandi M, Hosseinipour EZ. Combination of L-moments method and hydrological model for design flood hydrograph determination. In world environmental and water resources congress 2013: showcasing the future 2013 (pp. 3236-46).

Koutsoyiannis D. Knowable moments for high-order stochastic characterization and modelling of hydrological processes. Hydrological Sciences Journal. 2019; 64(1):19-33.

Agbonaye AI, Otuaro EA, Christopher O. Comparison of L-moment and method of moments as parameter estimators for identification and choice of the most appropriate rainfall distribution models for design of hydraulic structures. Journal of Civil Engineering. 2022; 13(1):33-48.

Vivekanandan N. Intercomparison of probability distributions for selecting a best fit for estimation of rainfall. i-Manager's Journal on Civil Engineering. 2022; 12(3):42-7.

Vivekanandan N. Intercomparison of estimators of extreme value family of distributions for rainfall frequency analysis. Mausam. 2022; 73(1):59-70.

Sanusi W, Chaerunnisa S, Annas S, Side S, Abdy M. Estimated parameters of rain flow distribution using L-moment method in South Sulawesi, Indonesia. Journal of Applied Mathematics and Computation. 2022; 6(1):30-40.

Guayjarernpanishk P, Bussababodhin P, Chiangpradit M. The partial L-moment of the four kappa distribution. Emerging Science Journal. 2023; 7(4):1116-25.

Anghel CG, Stanca SC, Ilinca C. Two-parameter probability distributions: methods, techniques and comparative analysis. Water. 2023; 15(19):1-35.

Chang CH, Rahmad R, Wu SJ, Hsu CT. Spatial frequency analysis by adopting regional analysis with radar rainfall in Taiwan. Water. 2022; 14(17):1-27.

Hinis MA, Geyikli MS. Accuracy evaluation of standardized precipitation index (SPI) estimation under conventional assumption in Yeşilırmak, Kızılırmak, and Konya closed Basins, Turkey. Advances in Meteorology. 2023; 2023(1):1-13.

Cao S, Lu H, Peng Y, Ren F. A novel fourth-order L-moment reliability method for L-correlated variables. Applied Mathematical Modelling. 2021; 95:806-23.

Guttman NB. The use of L-moments in the determination of regional precipitation climates. Journal of Climate. 1993; 6(12):2309-25.

Meshgi A, Khalili D. Comprehensive evaluation of regional flood frequency analysis by L-and LH-moments. I.A re-visit to regional homogeneity. Stochastic Environmental Research and Risk Assessment. 2009; 23:119-35.

Fuller WE. Closure to flood flows. Transactions of the American Society of Civil Engineers. 1914; 77(1):676-94.

Volpi E. On return period and probability of failure in hydrology. Wiley Interdisciplinary Reviews: Water. 2019; 6(3):e1340.

Volpi E, Fiori A, Grimaldi S, Lombardo F, Koutsoyiannis D. One hundred years of return period: strengths and limitations. Water Resources Research. 2015; 51(10):8570-85.

Fernández B, Salas JD. Return period and risk of hydrologic events. I: mathematical formulation. Journal of Hydrologic Engineering. 1999; 4(4):297-307.

Du T, Xiong L, Xu CY, Gippel CJ, Guo S, Liu P. Return period and risk analysis of nonstationary low-flow series under climate change. Journal of Hydrology. 2015; 527:234-50.

Gräler B, Van DBMJ, Vandenberghe S, Petroselli A, Grimaldi S, De BB, et al. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Sciences. 2013; 17(4):1281-96.

Volpi E, Fiori A, Grimaldi S, Lombardo F, Koutsoyiannis D. Save hydrological observations! return period estimation without data decimation. Journal of Hydrology. 2019; 571:782-92.

Shiau JT. Return period of bivariate distributed extreme hydrological events. Stochastic Environmental Research and Risk Assessment. 2003; 17:42-57.

Greenwood JA, Landwehr JM, Matalas NC, Wallis JR. Probability weighted moments: definition and relation to parameters of several distributions expressable in inverse form. Water Resources Research. 1979; 15(5):1049-54.

https://dominoweb.draco.res.ibm.com/reports/RC12210.pdf. Accessed 30 January 2025.

Hosking JR. Approximations for use in constructing L-moment ratio diagrams. IBM Research Division, TJ Watson Research Center; 1991.

Guo SL. A discussion on unbiased plotting positions for the general extreme value distribution. Journal of Hydrology. 1990; 121(1-4):33-44.

Cook NJ, Harris RI. The Gringorten estimator revisited. Wind & Structures. 2013; 16(4):355-72.

Benjamin JR, Cornell CA. Probability, statistics, and decision for civil engineers. Courier Corporation; 2014.

Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. Smart farming: internet of things (IoT)-based sustainable agriculture. Agriculture. 2022; 12(10):1-26.

Sharma S, Verma K, Hardaha P. Implementation of artificial intelligence in agriculture. Journal of Computational and Cognitive Engineering. 2023; 2(2):155-62.

Gulati A, Paroda R, Puri S, Narain D, Ghanwat A. Food system in India challenges, performance and promise. Science and Innovations for Food Systems Transformation. 2023: 813-28.

Akhter R, Sofi SA. Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences. 2022; 34(8):5602-18.

Abiri R, Rizan N, Balasundram SK, Shahbazi AB, Abdul-hamid H. Application of digital technologies for ensuring agricultural productivity. Heliyon. 2023; 9(2023):1-21.

Younes A, Abou EZE, El MO, Abou EDE, Majid ED. The application of machine learning techniques for smart irrigation systems: a systematic literature review. Smart Agricultural Technology. 2024:1-13.

Attri I, Awasthi LK, Sharma TP, Rathee P. A review of deep learning techniques used in agriculture. Ecological Informatics. 2023; 77:102217.

Sudhan RDM, Rani NU. Bird’s eye view of machine learning, deep learning in agriculture. Proceedings of the 2nd Indian international conference on industrial engineering and operations management Warangal, Telangana 2022 (pp.1355-63). IEOM Society International.

Albahar M. A survey on deep learning and its impact on agriculture: challenges and opportunities. Agriculture. 2023; 13(3):1-22.

Bharman P, Saad SA, Khan S, Jahan I, Ray M, Biswas M. Deep learning in agriculture: a review. Asian Journal of Research in Computer Science. 2022; 13(2):28-47.

Madhuri J, Indiramma M. Artificial neural networks based integrated crop recommendation system using soil and climatic parameters. Indian Journal of Science and Technology. 2021; 14(19):1587-97.

Jyothika P, Ramana KV, Narayana CL. Crop recommendation system to maximize crop yield using deep neural network. Journal of Engineering Sciences. 2021; 12(11):119-30.

Mythili K, Rangaraj R. Deep learning with particle swarm based hyper parameter tuning based crop recommendation for better crop yield for precision agriculture. Indian Journal of Science and Technology. 2021; 14(17):1325-37.

Mythili K, Rangaraj R. Crop recommendation for better crop yield for precision agriculture using ant colony optimization with deep learning method. Annals of the Romanian Society for Cell Biology. 2021: 4783-94.

Apat SK, Mishra J, Raju KS, Padhy N. An artificial intelligence-based crop recommendation system using machine learning. Journal of Scientific & Industrial Research. 2023; 82(5):558-67.

Mahale Y, Khan N, Kulkarni K, Wagle SA, Pareek P, Kotecha K, et al. Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique. Discover Sustainability. 2024; 5(1):1-23.

Varshitha DN, Choudhary S. An artificial intelligence solution for crop recommendation. Indonesian Journal of Electrical Engineering and Computer Science. 2022; 25(3):1688-95.

Hasan M, Marjan MA, Uddin MP, Afjal MI, Kardy S, Ma S, et al. Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation. Frontiers in Plant Science. 2023; 14:1-18.

Manjula E, Djodiltachoumy S. Efficient prediction of recommended crop variety through soil nutrients using deep learning algorithm. Journal of Postharvest Technology. 2022; 10(2):66-80.

Reddy DM, Neerugatti UR. A comparative analysis of machine learning models for crop recommendation in India. Revue Dintelligence Artificielle. 2023; 37(4):1081-90.

Anguraj K, Thiyaneswaran B, Megashree G, Shri JP, Navya S, Jayanthi J. Crop recommendation on analyzing soil using machine learning. Turkish Journal of Computer and Mathematics Education. 2021; 12(6):1784-91.

Ali SM, Das B, Kumar D. Machine learning based crop recommendation system for local farmers of Pakistan. Revista Geintec-Gestao Inovacao E Tecnologias. 2021; 11(4):5735-46.

Usha RN, Gowthami G. Smart crop suggester. In advances in computational and bio-engineering: proceeding of the international conference on computational and bio engineering, 2019 (pp. 401-13). Springer International Publishing.

Elbasi E, Zaki C, Topcu AE, Abdelbaki W, Zreikat AI, Cina E, et al. Crop prediction model using machine learning algorithms. Applied Sciences. 2023; 13(16):1-20.

Rawat P, Bajaj M, Vats S, Sharma V. An analysis of crop recommendation systems employing diverse machine learning methodologies. In international conference on device intelligence, computing and communication technologies 2023 (pp. 619-24). IEEE.

Samuel P, Sahithi B, Saheli T, Ramanika D, Kumar NA. Crop price prediction system using machine learning algorithms. Quest Journals Journal of Software Engineering and Simulation. 2020; 6(1):14-20.

Gupta T, Maggu S, Kapoor B. Crop prediction using machine learning. IRE Journals. 2023; 6(9):279-84.

Chandana C, Parthasarathy G. Efficient machine learning regression algorithm using naïve bayes classifier for crop yield prediction and optimal utilization of fertilizer. International Journal of Performability Engineering. 2022; 18(1):47-55.

Bandara P, Weerasooriya T, Ruchirawya T, Nanayakkara W, Dimantha M, Pabasara M. Crop recommendation system. International Journal of Computer Applications. 2020; 975:22-5.

Al-faiz MZ, Ibrahim AA, Hadi SM. The effect of Z-Score standardization (normalization) on binary input due the speed of learning in back-propagation neural network. Iraqi Journal of Information and Communication Technology. 2018; 1(3):42-8.

Sun W, Su F, Wang L. Improving deep neural networks with multi-layer maxout networks and a novel initialization method. Neurocomputing. 2018; 278:34-40.

Cha SH. Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences. 2007; 1(4):300-7.

Chen Z, Chen Y, Wu L, Cheng S, Lin P. Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management. 2019; 198:111793.

Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering. 2020; 8(1):22-34.

Gupta S, Maple C, Crispo B, Raja K, Yautsiukhin A, Martinelli F. A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes. Pattern Recognition. 2023; 139:109453.

Li Y. Research and application of deep learning in image recognition. In 2nd international conference on power, electronics and computer applications 2022 (pp. 994-9). IEEE.

Wang S, Deng G, Hu J. A partial hadamard transform approach to the design of cancelable fingerprint templates containing binary biometric representations. Pattern Recognition. 2017; 61:447-58.

Yang W, Wang S, Hu J, Zheng G, Valli C. A fingerprint and finger-vein based cancelable multi-biometric system. Pattern Recognition. 2018; 78:242-51.

Shahzad M, Wang S, Deng G, Yang W. Alignment-free cancelable fingerprint templates with dual protection. Pattern Recognition. 2021; 111:107735.

Bedari A, Wang S, Yang W. Design of cancelable MCC-based fingerprint templates using dyno-key model. Pattern Recognition. 2021; 119:108074.

Maiorana E. A survey on biometric recognition using wearable devices. Pattern Recognition Letters. 2022; 156:29-37.

Hammadi OI, Abas AD, Ayed KH. Face recognition using deep learning methods a review. International Journal of Engineering & Technology. 2018; 7:6181-8.

Melzi P, Tolosana R, Vera-rodriguez R, Kim M, Rathgeb C, Liu X, et al. FRCSyn-ongoing: nenchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems. Information Fusion. 2024; 107:1-19.

Zulfiqar M, Syed F, Khan MJ, Khurshid K. Deep face recognition for biometric authentication. In international conference on electrical, communication, and computer engineering 2019 (pp. 1-6). IEEE.

Semwal A, Londhe ND. A multi-stream spatio-temporal network based behavioural multiparametric pain assessment system. Biomedical Signal Processing and Control. 2024; 90:105820.

Singhal N, Ganganwar V, Yadav M, Chauhan A, Jakhar M, Sharma K. Comparative study of machine learning and deep learning algorithm for face recognition. Jordanian Journal of Computers and Information Technology. 2021; 7(3):313-25.

Sawat DD, Hegadi RS. Unconstrained face detection: a deep learning and machine learning combined approach. CSI Transactions on ICT. 2017; 5:195-9.

Moghekar R, Ahuja S. Face recognition in unconstrained environment using deep learning. In soft computing for intelligent systems: proceedings of ICSCIS 2020 (pp. 241-53). Springer Singapore.

Adetunji TO. Machine learning algorithms in facial identity verification for computer-based assessments. Acta Electronica Malaysia. 2024; 8(2):54-9.

Qasim KR, Qasim SS. Force field feature extraction using FAST algorithm for face recognition performance. In journal of physics: conference series 2021 (p. 012195). IOP Publishing.

Vakhshiteh F, Nickabadi A, Ramachandra R. Adversarial attacks against face recognition: a comprehensive study. IEEE Access. 2021; 9:92735-56.

Sharma R. Biometric authentication using lightweight convolutional neural network. In international students' conference on electrical, electronics and computer science 2024 (pp. 1-6). IEEE.

Kauba C, Piciucco E, Maiorana E, Gomez-barrero M, Prommegger B, Campisi P, et al. Towards practical cancelable biometrics for finger vein recognition. Information Sciences. 2022; 585:395-417.

Liu Y, Zhang X, Li Y, Zhou J, Li X, Zhao G. Graph-based facial affect analysis: a review. IEEE Transactions on Affective Computing. 2022; 14(4):2657-77.

Ma Q, Li B, Liu G, Li Y, Wang Y, Gu M, et al. Cancelable face template protection based on deep neural network. In 7th international conference on signal and image processing 2022 (pp. 659-64). IEEE.

Helmy M, El-shafai W, El-rabaie ES, El-dokany IM, Abd EFE. A hybrid encryption framework based on Rubik’s cube for cancelable biometric cyber security applications. Optik. 2022; 258:168773.

Acar A. Privacy-aware security applications in the era of internet of things. Electronic Theses and Dissertations, Florida International University. 2020.

Sudhakar T, Gavrilova M. Deep learning for multi-instance biometric privacy. ACM Transactions on Management Information Systems. 2020; 12(1):1-23.

Abdellatef E, Ismail NA, Abd Elrahman SE, Ismail KN, Rihan M, Abd El-Samie FE. Cancelable fusion-based face recognition. Multimedia Tools and Applications. 2019; 78:31557-80.

Almomani I, El-Shafai W, AlKhayer A, Alsumayt A, Aljameel S, Alissa K. Proposed biometric security system based on deep learning and chaos algorithms. Computers, Materials & Continua. 2023; 74(2):3515-37.

Hassaballah M, Aly S. Face recognition: challenges, achievements and future directions. IET Computer Vision. 2015; 9(4):614-26.

Abdellatef E, Ismail NA, Abd Elrahman SE, Ismail KN, Rihan M, Abd El-Samie FE. Cancelable multi-biometric recognition system based on deep learning. The Visual Computer. 2020; 36:1097-109.

Yang TT, Lan CC. Impacts of skin disorders associated with facial discoloration on quality of life: novel insights explaining discordance between life quality scores and willingness to pay. Journal of Cosmetic Dermatology. 2022; 21(7):3053-8.

Shekhar N, Eeshaan R, Tripathy DM, Siddharth M, Bhavni O. Pigmented purpuric dermatoses: a review. Pigment International. 2024; 11(1):1-11.

Nandraj JO, Gajanan KA, Karande KM. A concise review on contemporary and novel treatments addressing the prevention and control of hyperpigmentation. Asian Journal of Pharmaceutical Research and Development. 2024; 12(2):19-27.

Darwish E, Abdelgawad W, Makhlouf M, Abdalla H, Nassar YM, El-tohamy MH, et al. A mobile-based deep learning system for skin disease diagnosis. In intelligent methods, systems, and applications 2024 (pp. 39-44). IEEE.

Raman R, Kumar V, Saini D, Rabadiya D, Patre S, Meenakshi R. Machine learning-driven approaches for dermatological disease diagnosis. In international conference on data science and network security 2024 (pp. 1-5). IEEE.

Malik SG, Jamil SS, Aziz A, Ullah S, Ullah I, Abohashrh M. High-precision skin disease diagnosis through deep learning on dermoscopic images. Bioengineering. 2024; 11(9):1-24.

Pugazhenthi V, Naik SK, Joshi AD, Manerkar SS, Nagvekar VU, Naik KP, et al. Skin disease detection and classification. International Journal of Advanced Engineering Research and Science. 2019; 6(5):396-400.

Wu X, Feng Y, Xu H, Lin Z, Chen T, Li S, et al. CTransCNN: combining transformer and CNN in multilabel medical image classification. Knowledge-Based Systems. 2023; 281:1-15.

Azad R, Kazerouni A, Heidari M, Aghdam EK, Molaei A, Jia Y, et al. Advances in medical image analysis with vision transformers: a comprehensive review. Medical Image Analysis. 2024; 91:103000.

Ilham AA, Achmad A, Rachman MR. Facial skin disorder prediction based on non-visual information using ANN model. In 7th international conference on informatics and computational sciences 2024 (pp. 450-5). IEEE.

Wang Y, Li D, Li L, Sun R, Wang S. A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model. Heliyon. 2024; 10(15):1-11.

Wu ZH, Zhao S, Peng Y, He X, Zhao X, Huang K, et al. Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access. 2019; 7:66505-11.

Pawshe V, Bhagwat A, Yelai S, Irkar S, Kornule P. Face skin disease classification based on images. Journal of Emerging Technologies and Innovative Research. 2020; 7(6): 384-7.

Srinivasu PN, Sivasai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors. 2021; 21(8):1-27.

Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences. 2021; 11(11):1-23.

Dai Y. Building CNN-based models for image aesthetic score prediction using an ensemble. Journal of Imaging. 2023; 9(2):1-14.

Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial intelligence in dermatology image analysis: current developments and future trends. Journal of Clinical Medicine. 2022; 11(22):1-14.

Sazzadul IPM, Mahjabin FS, Bulbul AM, Zihadur RM, Kabir HAB, Shamim KM. Deep learning-based skin disease detection using convolutional neural networks (CNN). In the fourth industrial revolution and beyond: select proceedings of IC4IR+ 2023 (pp. 551-64). Singapore: Springer Nature Singapore.

Dhar P, Guha S. Skin lesion detection using fuzzy approach and classification with CNN. International Journal of Engineering and Manufacturing. 2021; 11(1):11-8.

Thwe PM, Yu MT. Analysis on skin colour model using adaptive threshold values for hand segmentation. International Journal of Image, Graphics and Signal Processing 2019; 11(9):25-33.

Chickaramanna SG, Thippeswamy VS. Identification and classification of prakriti of human using facial features. IAES International Journal of Artificial Intelligence. 2024; 13(2):2093-101.

Dahdouh Y, Boudhiranouar A, Ahmed M. Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma. IAES International Journal of Artificial Intelligence. 2024; 13(1): 1104-11.

Gadag S, Palraj P. Hybrid channel and spatial attention-UNet for skin lesion segmentation. IAES International Journal of Artificial Intelligence (IJ-AI). 2024; 13(1):1077-89.

Khomsi Z, El FM, Bellarbi L. CNN-based approach for non-invasive estimation of breast tumor size and location using thermographic images. International Journal of Online & Biomedical Engineering. 2024; 20(4):160-75.

Makhir A, El YMH, Alaoui LB. Comprehensive cardiac ischemia classification using hybrid CNN-based models. International Journal of Online & Biomedical Engineering. 2024; 20(3):154-65.

Maquen-niño GL, Nuñez-fernandez JG, Taquila-falderon FY, Adrianzén-olano I, De-la-cruz-VdV P, Carrión-barco G. Classification model using transfer learning for the detection of pneumonia in chest X-ray images. International Journal of Online & Biomedical Engineering. 2024; 20(5):150-61.

Benradi H, Bouganssa I, Chater A, Lasfar A. Discriminative approach lung diseases and COVID-19 from chest X-Ray images using convolutional neural networks: a promising approach for accurate diagnosis. International Journal of Online & Biomedical Engineering. 2023; 19(14):131-41.

Allugunti VR. A machine learning model for skin disease classification using convolution neural network. International Journal of Computing, Programming and Database Management. 2022; 3(1):141-7.

Bonechi S, Bianchini M, Bongini P, Ciano G, Giacomini G, Rosai R, et al. Fusion of visual and anamnestic data for the classification of skin lesions with deep learning. In new trends in image analysis and processing–ICIAP Trento, Italy, 2019 (pp. 211-9). Springer International Publishing.

Pacheco AG, Krohling RA. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine. 2020; 116:103545.

Chin YP, Hou ZY, Lee MY, Chu HM, Wang HH, Lin YT, et al. A patient‐oriented, general‐practitioner‐level, deep‐learning‐based cutaneous pigmented lesion risk classifier on a smartphone. British Journal of Dermatology. 2020; 182(6):1498-500.

Sriwong K, Bunrit S, Kerdprasop K, Kerdprasop N. Dermatological classification using deep learning of skin image and patient background knowledge. International Journal of Machine Learning and Computing. 2019; 9(6):862-7.

Nath S, Das GS, Saha S. Deep learning-based common skin disease image classification. Journal of Intelligent & Fuzzy Systems. 2023; 44(5):7483-99.

Ahmed I, Rehan M, Basit A, Tufail M, Hong KS. Neuro-fuzzy and networks-based data driven model for multi-charging scenarios of plug-in-electric vehicles. IEEE Access. 2023; 11:87150-65.

Kumar AN, Chakravarthy M, Kumar MS, Nagaraju M, Ramesha M, Gururaj B, et al. Fuzzy location algorithm for cross-country and evolving faults in EHV transmission line. International Journal of Fuzzy Logic and Intelligent Systems. 2023; 23(2):130-9.

Uddin M, Mo H, Dong D, Elsawah S, Zhu J, Guerrero JM. Microgrids: a review, outstanding issues and future trends. Energy Strategy Reviews. 2023; 49:101127.

Jia Y, Ran G, Gong Y, Wang L, Xu F. Dynamic feedback tracking control for interval type-2 TS fuzzy nonlinear system based on adaptive event-triggered strategy. Neural Processing Letters. 2023; 55(2):1715-40.

Hentout A, Maoudj A, Aouache M. A review of the literature on fuzzy-logic approaches for collision-free path planning of manipulator robots. Artificial Intelligence Review. 2023; 56(4):3369-444.

Sun HY, Han HG, Sun J, Yang HY, Qiao JF. Security control of sampled-data T–S fuzzy systems subject to cyberattacks and successive packet losses. IEEE Transactions on Fuzzy Systems. 2022; 31(4):1178-88.

Xie X, Yang F, Wan L, Xia J, Shi K. Enhanced local stabilization of constrained N-TS fuzzy systems with lighter computational burden. IEEE Transactions on Fuzzy Systems. 2022; 31(3):1064-70.

Dehak A, Nguyen AT, Dequidt A, Vermeiren L, Dambrine M. Reduced-complexity LMI conditions for admissibility analysis and control design of singular nonlinear systems. IEEE Transactions on Fuzzy Systems. 2022; 31(4):1377-90.

Narayanamoorthy S, Ramya L, Gunasekaran A, Kalaiselvan S, Kang D. Selection of suitable biomass conservation process techniques: a versatile approach to normal wiggly interval-valued hesitant fuzzy set using multi-criteria decision making. Complex & Intelligent Systems. 2023; 9(6):6681-95.

Zhang P, Zhang Z, Gong D, Cui X. A novel normal wiggly hesitant fuzzy multi-criteria group decision making method and its application to electric vehicle charging station location. Expert Systems with Applications. 2023; 223:119876.

Singh DJ, Verma NK. Interval type-3 TS fuzzy system for nonlinear aerodynamic modeling. Applied Soft Computing. 2024; 150:111097.

VO TK, Nguyen XD, Ha MT, Le TK. Implement fuzzy-PID controllers for trajectory tracking of an underactuated surface vessel. International Journal of Intelligent Systems and Applications in Engineering. 2023; 11(2):126-32. https://ijisae.org/index.php/IJISAE/article/view/2603

Song YH, Wang GS, Wang PY, Johns AT. Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. IEE Proceedings-Generation, Transmission and Distribution. 1997; 144(4):377-82.

Mabrouk AB, Alanazi A, Bassfar Z, Alanazi D. New hybrid model for nonlinear systems via Takagi-Sugeno fuzzy approach. AIMS Mathematics. 2024; 9(9):23197-220.

Duan Z, Ding F, Liang J, Xiang Z. Observer-based fault detection for continuous–discrete systems in TS fuzzy model. Nonlinear Analysis: Hybrid Systems. 2023; 50:101379.

Sharifian Y, Abdi H. Multi-area economic dispatch problem: methods, uncertainties, and future directions. Renewable and Sustainable Energy Reviews. 2024; 191:114093.

You W, Xie X, Wang H, Xia J, Stojanovic V. Relaxed model predictive control of TS fuzzy systems via a new switching-type homogeneous polynomial technique. IEEE Transactions on Fuzzy Systems. 2024; 32(8):4583-94.

Xu J, Song T, Wang J. Finite-time fuzzy fault-tolerant control for nonlinear flexible spacecraft system with stochastic actuator faults. Mathematics. 2024; 12(4):1-25.

Yan F, Feng S, Liu X, Feng T. Parametric dynamic distributed containment control of continuous-time linear multi-agent systems with specified convergence speed. Sensors. 2023; 23(5):1-17.

Liu G, Sun J, Wang W, Hui N, Zou L, Liu C. H∞ static output feedback control for TS fuzzy systems via lyapunov functions. In 43rd Chinese control conference (CCC) 2024 (pp. 2618-23). IEEE.

Yang H, Zhang S, Yu F. Admissibility analysis and controller design improvement for TS fuzzy descriptor systems. Symmetry. 2024; 16(8):1-16.

Kchaou M, Regaieg MA, Jerbi H, Abbassi R, Stefanoiu D, Popescu D. Admissible control for non-linear singular systems subject to time-varying delay and actuator saturation: an interval type-2 fuzzy approach. Actuators. 2023; 12(30):1-20.

Lu DH, Huang CP. Refined admissible analysis and design conditions for discrete fuzzy singular systems with multiple difference matrices. Applied Artificial Intelligence. 2023; 37(1):1-19.

Li J, Piao Y. Multi-object tracking based on re-identification enhancement and associated correction. Applied Sciences. 2023; 13(17):1-16.

Ravi S, Matheswaran S, Perumal U, Sivakumar S, Palvadi SK. Adaptive trust-based secure and optimal route selection algorithm for MANET using hybrid fuzzy optimization. Peer-to-Peer Networking and Applications. 2023; 16(1):22-34.

Rahman S, Khan I, Dey S, Mallik A. Triple-active bridge-based dynamic power balancing solution for minimizing overdesigning in military aircraft power system. IEEE Transactions on Vehicular Technology. 2023; 73(3):3329-39.

Wu J, He X, Niu Y, Huang T, Yu J. Circuit implementation of proximal projection neural networks for composite optimization problems. IEEE Transactions on Industrial Electronics. 2023; 71(2):1948-57.

Shen M, Wang X, Zhu S, Wu Z, Huang T. Data-driven event-triggered adaptive dynamic programming control for nonlinear systems with input saturation. IEEE Transactions on Cybernetics. 2023; 54(2):1178-88.

Massaoudi M, Abu-rub H, Ghrayeb A. Advancing lithium-ion battery health prognostics with deep learning: a review and case study. IEEE Open Journal of Industry Applications. 2024; 5:43-62.

Coy JJ, Saito M, Yang P, Liu X, Hu Y. A robust ice cloud optical property model for LiDAR-based remote sensing applications. IEEE Geoscience and Remote Sensing Letters. 2023; 21:1-5.

Wang D, Duan X, Yeh SH, Zou J, Song D. Calibration system and algorithm design for a soft hinged micro scanning mirror with a triaxial hall effect sensor. IEEE Robotics and Automation Letters. 2024; 9(3):2447-54.

Lee SC, Hwang HS, Lee KC. Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment. Progress in Orthodontics. 2022; 23(1):1-11.

Wu L, Gao H. Sliding mode control of two-dimensional systems in Roesser model. IET Control Theory & Applications. 2008; 2(4):352-64.

Yang ZJ, Shibuya Y, Qin P. Distributed robust control for synchronised tracking of networked Euler–Lagrange systems. International Journal of Systems Science. 2015; 46(4):720-32.

Yuan YH, Zhang QL, Zhang DQ, Chen BI. Admissible conditions of fuzzy descriptor systems based on fuzzy Lyapunov function approach. International Journal of Information and Systems Sciences. 2008; 4(2):219-32.

Kunya AB, Abubakar AS, Yusuf SS. Review of economic dispatch in multi-area power system: state-of-the-art and future prospective. Electric Power Systems Research. 2023; 217:109089.

Jadoun VK, Prashanth GR, Joshi SS, Narayanan K, Malik H, Márquez FP. Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled whale optimization algorithm. Applied Energy. 2022; 315:1-12.

Maged NA, Hasanien HM, Ebrahim EA, Tostado-véliz M, Turky RA, Jurado F. Optimal real-time implementation of fuzzy logic control strategy for performance enhancement of autonomous microgrids. International Journal of Electrical Power & Energy Systems. 2023; 151:109140.

Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics. 1985; 15(1):116-32.

Kaloop MR, Bardhan A, Kardani N, Samui P, Hu JW, Ramzy A. Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power. Renewable and Sustainable Energy Reviews. 2021; 148:111315.

Hasan F, Kargarian A. Topology-aware learning assisted branch and ramp constraints screening for dynamic economic dispatch. IEEE Transactions on Power Systems. 2022; 37(5):3495-505.

Ramachandran M, Mirjalili S, Ramalingam MM, Gnanakkan CA, Parvathysankar DS, Sundaram A. A ranking-based fuzzy adaptive hybrid crow search algorithm for combined heat and power economic dispatch. Expert Systems with Applications. 2022; 197:116625.

Gajanan LS, Kirar M, Paliwal P, Rajak N. A state-of-the-art review on modern artificial intelligence based techniques for economic load dispatch. In renewable energy and sustainable E-mobility conference 2023 (pp. 1-6). IEEE.

Azeem F, Ahmad A, Gondal TM, Arshad J, Rehman AU, Eldin EM, et al. Load management and optimal sizing of special-purpose microgrids using two stage PSO-fuzzy based hybrid approach. Energies. 2022; 15(17):1-19.

Xu D, Xiao J, Wang X. Environmental economic dispatch of power system based on multi-objective improved hybrid leapfrog algorithm. In 2nd international conference on electrical engineering and control science 2022 (pp. 504-10). IEEE.

Marzbani F, Abdelfatah A. Economic dispatch optimization strategies and problem formulation: a comprehensive review. Energies. 2024; 17(3):1-31.

Nguyen AT, Sentouh C, Popieul JC. Fuzzy steering control for autonomous vehicles under actuator saturation: design and experiments. Journal of the Franklin Institute. 2018; 355(18):9374-95.

Nguyen AT, Campos V, Guerra TM, Pan J, Xie W. Takagi–sugeno fuzzy observer design for nonlinear descriptor systems with unmeasured premise variables and unknown inputs. International Journal of Robust and Nonlinear Control. 2021; 31(17):8353-72.

Chen J, Sun Y, Min H, Sun F, Zhang Y. New results on static output feedback H∞ control for fuzzy singularly perturbed systems: a linear matrix inequality approach. International Journal of Robust and Nonlinear Control. 2013; 23(6):681-94.

Nguyen AT, Coutinho P, Guerra TM, Palhares R, Pan J. Constrained output-feedback control for discrete-time fuzzy systems with local nonlinear models subject to state and input constraints. IEEE Transactions on Cybernetics. 2020; 51(9):4673-84.

Xie XP, Yue D, Park JH. Robust fault estimation design for discrete-time nonlinear systems via a modified fuzzy fault estimation observer. ISA Transactions. 2018; 73:22-30.

Kwon OM, Park MJ, Park JH, Lee SM. Stability and stabilization of TS fuzzy systems with time-varying delays via augmented Lyapunov-Krasovskii functionals. Information Sciences. 2016; 372:1-5.

Wang L, Liu J, Lam HK. New results of observer design for continuous-time fuzzy systems: a switching technique. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2021; 52(9):5702-6.

Gong A, Xie XP, Peng C. Enhanced observer-based state estimation of discrete-time Takagi-Sugeno fuzzy systems via a distinctive multi-instant gain-scheduling law. Journal of the Franklin Institute. 2021; 358(17):9288-306.

Maharana HS, Dash SK. Comparative optimization analysis of ramp rate constriction factor based PSO and electro magnetism based PSO for economic load dispatch in electric power system. In international conference on applied machine learning 2019 (pp. 63-8). IEEE.

Gachhayat SK, Dash SK. Modified sub-gradient based combined objective technique and evolutionary programming approach for economic dispatch involving valve-point loading, enhanced prohibited zones and ramp rate constraints. International Journal of Electrical and Computer Engineering. 2020; 10(5):5048-57.

Chen G, Tan T, Xiang W, Guan Z, Tan H, Yu J, et al. Solving environment economic power dispatch problems by multi-objective modified seagull optimization algorithm with novel constraint treatments. IAENG International Journal of Applied Mathematics. 2023; 53(1):1-17.

Gachhayat SK, Dash SK, Deo BP. Approaches for smart linear regression in a difficult quasi economic dispatch problem. International Journal of Electrical & Computer Engineering. 2023; 13(4):4752-60.

Roman RC, Precup RE, Hedrea EL. Intelligent proportional controller tuned by virtual reference feedback tuning and fictitious reference iterative tuning. Procedia Computer Science. 2023; 221:86-93.

Kaur A, Narang N. Multi-objective generation scheduling of integrated energy system using hybrid optimization technique. Neural Computing and Applications. 2024; 36(3):1215-36.

Daqaq F, Ouassaid M, Ellaia R. A new meta-heuristic programming for multi-objective optimal power flow. Electrical Engineering. 2021; 103(2):1217-37.

Wang Z, Li P, Li Q, Wang Z, Li Z. Motion planning method for car-like autonomous mobile robots in dynamic obstacle environments. IEEE Access. 2023; 11:137387-400.

Velasco-sánchez EP, Bañón MÁ, Candelas FA, Puente ST, Torres F. ViKi-HyCo: a hybrid-control approach for complex car-like maneuvers. IEEE Access. 2024; 12:65428-43.

Carron A, Bodmer S, Vogel L, Zurbrügg R, Helm D, Rickenbach R, et al. Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control. In international conference on robotics and automation 2023 (pp. 1371-8). IEEE.

Pastrana MA, Oliveira LH, Mendes DA, Silva DL, Mendoza-peñaloza J, Muñoz DM. Implementation of a PID controller using online tuning applied to a mobile robot obstacle following/avoidance. In argentine conference on electronics 2024 (pp. 10-5). IEEE.

Leong PY, Ahmad NS. Exploring autonomous load-carrying mobile robots in indoor settings: a comprehensive review. IEEE Access. 2024; 12:131395-417.

Jochman T, Voltr V, Kubáček V, Švec O, Burget P, Hlaváč V. Integrating augmented reality within digital twins for smart robotic manufacturing systems. In 29th international conference on emerging technologies and factory automation 2024 (pp. 1-8). IEEE.

Kang BG, Park HS, Lee JM, Yun JM. Methodology on the cyber-physical system construction for a user-friendly smart clothing manufacturing robot system. In 33rd IEEE international conference on robot and human interactive communication 2024 (pp. 412-6). IEEE.

Nagaya K, Ohhira T, Hashimoto H. Developing power-assisted two-wheeled luggage-carrying robot for stair-lifting using admittance control. In international conference on mechatronics 2023 (pp. 1-6). IEEE.

Gu S, Meng F, Liu B, Chen X, Yu Z, Huang Q. Adaptive interactive control of human and quadruped robot load motion. IEEE/ASME Transactions on Mechatronics. 2024:1-12.

Andronas D, Kampourakis E, Papadopoulos G, Bakopoulou K, Kotsaris PS, Michalos G, et al. Towards seamless collaboration of humans and high-payload robots: an automotive case study. Robotics and Computer-Integrated Manufacturing. 2023; 83:1-17.

Ma J, Lian B, Wang M, Dong G, Li Q, Wu J, et al. Optimal design of a parallel assembling robot with large payload-to-mass ratio. Robotics and Computer-Integrated Manufacturing. 2023; 80:102474.

Xiao L. Precise movement control of agricultural robot car based on PID control algorithm. In 2nd international conference on image processing and computer applications 2024 (pp. 1-6). IEEE.

Georgieva T, Ivanov A, Anchev A. Payload stabilization in multidirectional robot motion by applying trajectory analysis algorithm and feedback encoders. In international conference automatics and informatics 2023 (pp. 179-85). IEEE.

Lee J, Chang PH, Yu B, Jin M. An adaptive PID control for robot manipulators under substantial payload variations. IEEE Access. 2020; 8:162261-70.

Wang Y, Li Z, Wang Y, Yang Y. Design of a position tracking controller for quadrotor uavs with variable mass. In 7th international conference on intelligent robotics and control engineering 2024 (pp. 52-8). IEEE.

Borkar KK, Aljrees T, Pandey SK, Kumar A, Singh MK, Sinha A, et al. Stability analysis and navigational techniques of wheeled mobile robot: a review. Processes. 2023; 11(12):1-36.

Serrano-pérez O, Villarreal-cervantes MG, Rodríguez-molina A, Serrano-pérez J. Offline robust tuning of the motion control for omnidirectional mobile robots. Applied Soft Computing. 2021; 110:107648.

Somefun OA, Akingbade K, Dahunsi F. The dilemma of PID tuning. Annual Reviews in Control. 2021; 52:65-74.

Borase RP, Maghade DK, Sondkar SY, Pawar SN. A review of PID control, tuning methods and applications. International Journal of Dynamics and Control. 2021; 9:818-27.

Abdulnabi AR, Esmaile MF. Automatic mapping and localization in large-scale cyclic using K-nearest neighbours. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(97):1802-11.

Ibrahim MF, Huddin AB, Zaman MH, Hussain A, Anual SN. An enhanced frontier strategy with global search target-assignment approach for autonomous robotic area exploration. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(75):283-91.

Joseph SB, Dada EG, Abidemi A, Oyewola DO, Khammas BM. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. Heliyon. 2022; 8(5):1-29.

Africa AD, Chua JO, Solis JL. PID tuning of speed controller using ziegler-nichols and manual method DC motor. In 15th international conference on humanoid, nanotechnology, information technology, communication and control, environment, and management 2023 (pp. 1-6). IEEE.

Hegedus ET, Birs IR, Ghita M, Ionescu CM, De KR, Muresan CI, et al. Optimal fractional order PID based on a modified Ziegler-Nichols method. In international conference on electrical, computer, communications and mechatronics engineering 2022 (pp. 1-6). IEEE.

Mazlan NN, Thamrin NM, Razak NA. Comparison between Ziegler-Nichols and AMIGO tuning techniques in automated steering control system for autonomous vehicle. In international conference on automatic control and intelligent systems 2020 (pp. 7-12). IEEE.

Utami AR, Yuniar RJ, Giyantara A, Saputra AD. Cohen-coon PID tuning method for self-balancing robot. In international symposium on electronics and smart devices 2022 (pp. 1-5). IEEE.

Manoharan SK, Raghavan D, Megalingam RK, Parakat P, Sudheesh SK. Fine tuning mobility: PID driven velocity control optimization for two wheel differential drive robot. In Parul international conference on engineering and technology 2024 (pp. 1-6). IEEE.

Dib F, Benaya N, Ben MK, Boumhidi I. Comparative study of optimal tuning PID controller for manipulator robot. In the proceedings of the international conference on smart city applications 2022 (pp. 252-61). Cham: Springer International Publishing.

Kenmochi K, Dai Y, Hirakoso N. An optimal fusion of the position control part and the force control part in the bilateral control system. In 12th global conference on consumer electronics 2023 (pp. 545-6). IEEE.

Ramos-velasco LE, Parra-vega V, García-rodríguez R, Vega-navarrete MA, Trejo-ramos C, Olguín-díaz E. Knowledge-based self-tuning of PID control gains for continuum soft robots. Engineering Applications of Artificial Intelligence. 2024; 133:108447.

Mourad A, Youcef Z. Fuzzy-PI controller tuned with ICA: applied to 2 DOF robot control trajectory. In international conference on information, communication and automation technologies 2022 (pp. 1-6). IEEE.

Wahyuni S, Fuad M, Hilda AD, Umam F, Latif M. Enhancing mobile robot stability and roaming capability using fuzzy-PID control method. In 9th information technology international seminar 2023 (pp. 1-6). IEEE.

Kristiyono R, Wiyono W. Autotuning fuzzy PID controller for speed control of BLDC motor. Journal of Robotics and Control. 2021; 2(5):400-7.

Razali MR, Faudzi AA, Shamsudin AU. Position and angular control using fuzzy tuned PID controller for mobile robot path tracking. In 5th international symposium in robotics and manufacturing automation 2022 (pp. 1-6). IEEE.

Yilmaz BM, Tatlicioglu E, Savran A, Alci M. Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators. Applied Soft Computing. 2021; 104:107183.

Ben JC, Seddik H. Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot. Asian Journal of Control. 2021; 23(1):23-41.

Freire EO, Rossomando FG, Soria CM. Self-tuning of a neuro-adaptive PID controller for a SCARA robot based on neural network. IEEE Latin America Transactions. 2018; 16(5):1364-74.

Rodríguez-abreo O, Rodríguez-reséndiz J, Fuentes-silva C, Hernández-alvarado R, Falcón MD. Self-tuning neural network PID with dynamic response control. IEEE Access. 2021; 9:65206-15.

Yildirim S, Savas S, Andruskiene J. Controller gain tuning of a nonholonomic mobile robot via neural network predictor. In 25th international conference electronics 2021 (pp. 1-6). IEEE.

Park J, Kim H, Hwang K, Lim S. Deep reinforcement learning based dynamic proportional-integral (PI) gain auto-tuning method for a robot driver system. IEEE Access. 2022; 10:31043-57.

Zhang L, Hou Z, Wang J, Liu Z, Li W. Robot navigation with reinforcement learned path generation and fine-tuned motion control. IEEE Robotics and Automation Letters. 2023; 8(8):4489-96.

Mate S, Pal P, Jaiswal A, Bhartiya S. Simultaneous tuning of multiple PID controllers for multivariable systems using deep reinforcement learning. Digital Chemical Engineering. 2023; 9:1-12.

Rahayu ES, Maarif A, Cakan A. Particle swarm optimization (PSO) tuning of PID control on DC motor. International Journal of Robotics and Control Systems. 2022; 2(2):435-47.

Campos J, Jaramillo S, Morales L, Camacho O, Chávez D, Pozo D. PSO tuning for fuzzy PD+ I controller applied to a mobile robot trajectory control. In international conference on information systems and computer science 2018 (pp. 62-8). IEEE.

Charkoutsis S, Kara-mohamed M. A particle swarm optimization tuned nonlinear PID controller with improved performance and robustness for first order plus time delay systems. Results in Control and Optimization. 2023; 12:1-18.

Abajo MR, Sierra-garcía JE, Santos M. Evolutive tuning optimization of a PID controller for autonomous path-following robot. In16th international conference on soft computing models in industrial and environmental applications 2022 (pp. 451-60). Springer International Publishing.

Goud H, Sharma PC, Nisar K, Haque MR, Ibrahim AA, Yadav NS, et al. Metaheuristics algorithm for tuning of PID controller of mobile robot system. Computers, Materials & Continua. 2022; 72(2):3481-92.

Suseno EW, Ma'arif A. Tuning of PID controller parameters with genetic algorithm method on DC motor. International Journal of Robotics and Control Systems. 2021; 1(1):41-53.

Valluru SK, Singh M. Optimization strategy of bio-inspired metaheuristic algorithms tuned PID controller for PMBDC actuated robotic manipulator. Procedia Computer Science. 2020; 171:2040-9.

Zidan A, Tappe S, Ortmaier T. Auto-tuning of PID controllers for robotic manipulators using PSO and MOPSO. In informatics in control, automation and robotics: 14th international conference, Madrid, Spain, 2017 (pp. 339-54). Springer International Publishing.

Liu Y, Jiang D, Yun J, Sun Y, Li C, Jiang G, et al. Self-tuning control of manipulator positioning based on fuzzy PID and PSO algorithm. Frontiers in Bioengineering and Biotechnology. 2022; 9:1-36.

Roveda L, Forgione M, Piga D. Robot control parameters auto-tuning in trajectory tracking applications. Control Engineering Practice. 2020; 101:104488.

Faraj MA, Abbood AM. Fractional order PID controller tuned by bat algorithm for robot trajectory control. Indonesian Journal of Electrical Engineering and Computer Science. 2021; 21(1):74-83.

Ghith ES, Tolba FA. Real-time implementation of tuning PID controller based on whale optimization algorithm for micro-robotics system. In 14th international conference on computer and automation engineering 2022 (pp. 103-9). IEEE.

Yao X, Shi X, Zhang X, Mei X. An ant colony optimization parameter tuning method based on uniform design for path planning of mobile robots. In international conference on advances in electrical engineering and computer applications 2022 (pp. 1183-91). IEEE.

Ghith ES, Tolba FA. Tuning PID controllers based on hybrid arithmetic optimization algorithm and artificial gorilla troop optimization for micro-robotics systems. IEEE Access. 2023; 11:27138-54.

Mishra R, Tripathi MK, Sikarwar RS, Singh Y, Tripathi N. Soybean (glycine max l. merrill): a multipurpose legume shaping our world. Plant Cell Biotechnology and Molecular Biology. 2024; 25(3-4):17-37.

Toomer OT, Oviedo EO, Ali M, Patino D, Joseph M, Frinsko M, et al. Current agronomic practices, harvest & post-harvest processing of soybeans (glycine max)-a review. Agronomy. 2023; 13(2):1-14.

Uebersax MA, Cichy KA, Gomez FE, Porch TG, Heitholt J, Osorno JM, et al. Dry beans (phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security-a review. Legume Science. 2023; 5(1):1-13.

Addae-frimpomaah F, Amenorpe G, Denwar NN, Amiteye S, Adazebra GA, Sossah FL, et al. Participatory approach of preferred traits, production constraints and mitigation strategies: implications for soybean breeding in Guinea Savannah zone of Ghana. Heliyon. 2022; 8(5):1-14.

Zhao Y, Liu J, Yang R, Guo T, Zhang J, Li W, et al. A comb-brushing-type green soybean pod harvesting equipment: design and experiment. Plos One. 2023; 18(11):1-28.

Carreira VD, Aleixo EV, Ribeiro NM, Santos JD, Silva RP. A systematic and meta-analytical review of soybean mechanized harvesting in South America. Revista Brasileira De Engenharia Agrícola E Ambiental. 2023; 28(1):1-10.

Staton MJ. Reducing soybean harvest losses. In encyclopedia of digital agricultural technologies 2023 (pp. 1109-19). Cham: Springer International Publishing.

Karunathilake EM, Le AT, Heo S, Chung YS, Mansoor S. The path to smart farming: innovations and opportunities in precision agriculture. Agriculture. 2023; 13(8):1-26.

Wamalwa PW. Optimization of design parameters and performance of a portable common beans (phaseolus vulgaris L) thresher. Doctoral Dissertation, Jomo Kenyatta University of Agriculture and Technology. 2022.

Adekanye TA, Osakpamwan AB, Osaivbie IE. Evaluation of a soybean threshing machine for small scale farmers. Agricultural Engineering International: CIGR Journal. 2016; 18(2):426-34.

Qabaradin A, Tsegaye A. A evaluation of soybean thresher for chickpea crop. Irish Interdisciplinary Journal of Science & Research. 2021; 5(2):101-8.

Kang J, Wang X, Xie F, Luo Y, Li Q, Huang X. Experiments and analysis of the differential threshing cylinder for soybean with different maturities. Transactions of the Chinese Society of Agricultural Engineering. 2023; 39(1):38-49.

Vejasit A, Salokhe V. Studies on machine-crop parameters of an axial flow thresher for threshing soybean. International: the GIGR Journal of Scientific Research and Development. 2004:1-12.

Doddamani SS, Betageri SN. Influence of stages of harvesting and threshing methods on soybean [glycine max (L.) merrill]. The Pharma Innovation Journal. 2021; 10(6): 687-96.

Ni Y, Jin C, Chen M, Qian Z, Yang T, Xu J, et al. Soybean crushing forms by mechanical harvesting and factors affecting the proportions of different forms. Food Science and Technology. 2023; 43:1-10.

Cubukcu P, Şahar AK, Oluk CA, Onat FB. Soybean (glycine max L.) sprouts: an overview. MAS Journal of Applied Sciences. 2023; 8(3):581-90.

Anderson EJ, Ali ML, Beavis WD, Chen P, Clemente TE, Diers BW, et al. Soybean [glycine max (L.) merr.] breeding: history, improvement, production and future opportunities. Advances in Plant Breeding Strategies: Legumes. 2019; 7:431-516.

Amoah AE, Oppong AJ, Appah S, Obeng-akrofi G. Mechanised threshing of pod grains used as food and strategies to optimise the technique: a review. Journal of Advances in Agriculture. 2021; 12:30-43.

Yamba P, Larson EA, Issaka Z, Akayeti A. Design, manufacture and performance evaluation of a soybean paddle thresher with a blower. International Journal of Mechanical Engineering and Applications. 2017; 5(5):253-8.

Nag PK, Gite LP. Human-centered agriculture. Springer Singapore; 2020.

Gautam PV, Mansuri SM, Prakash O, Pramendra, Patel A, Shukla P, et al. Agricultural mechanization for efficient utilization of input resources to improve crop production in arid region. In enhancing resilience of dryland agriculture under changing climate: interdisciplinary and convergence approaches 2023 (pp. 689-716). Singapore: Springer Nature Singapore.

Li Y, Su Z, Liang Z, Li Y. Variable-diameter drum with concentric threshing gap and performance comparison experiment. Applied Sciences. 2020; 10(15):1-17.

Tang Z, Zhang B, Wang M, Zhang H. Damping behaviour of a prestressed composite beam designed for the thresher of a combine harvester. Biosystems Engineering. 2021; 204:130-46.

Warghane RS, Easwara PR. Efficiency enhancement of thresher by auto-controlling the cylinder clearance. International Journal of System Assurance Engineering and Management. 2023; 14(6):2066-79.

Li X, Du Y, Guo J, Mao E. Design, simulation, and test of a new threshing cylinder for high moisture content corn. Applied Sciences. 2020; 10(14):1-20.

Rudoy D, Egyan M, Kulikova N, Chigvintsev V. Review and analysis of technologies for harvesting perennial grain crops. In conference series: earth and environmental science 2021 (pp. 1-12). IOP Publishing.

Delelegn T. Design development and performance evaulation of a common bean (phaseolus vulgaris) thresher. Doctoral Dissertation, Haramaya University. 2022.

Guo J, Du Y, Wu Y, Mao E. Research status and development trend of corn harvester threshing device. In ASABE annual international meeting 2019. American Society of Agricultural and Biological Engineers.

Hunt D, Wilson D. Farm power and machinery management. Waveland Press; 2015.

Dagur R, Singh D, Bhateja S, Rastogi V. Mechanical and material designing of lightweight high endurance multirotor system. Materials Today: Proceedings. 2020; 21:1624-31.

Olusesi OS, Udoye NE. Development and characterization of AA6061 aluminium alloy/clay and rice husk ash composite. Manufacturing Letters. 2021; 29:34-41.

Pereira T, Kennedy JV, Potgieter J. A comparison of traditional manufacturing vs additive manufacturing, the best method for the job. Procedia Manufacturing. 2019; 30:11-8.

Adeyeye O, Sadiku ER, Osholana TS, Reddy AB, Olayinka AO, Ndamase AS, et al. Construction and evaluation of soybeans thresher. African Journal of Agricultural Research. 2019; 14(21):921-7.

Ishola TA, Busari RA, Subair IO, Owoyemi IO, Issa AL. Development of an improved soybean thresher. Journal of Agricultural Engineering and Technology. 2021; 26(1):36-46.

Asodina FA, Adams F, Nimoh F, Asante BO, Mensah A. Performance of smallholder soybean farmers in Ghana; evidence from upper west region of Ghana. Journal of Agriculture and Food Research. 2021; 4:1-7.

Nair S, Suresh A, Mangat A. Gender friendly tools and equipments in farm mechanization. In engendering agricultural development 2022 (pp. 221-38). CRC Press.

Hota S, Mishra JN, Mohanty SK, Khadatkar A. Design and development of pedal operated Ragi thresher for tribal region of Odisha, India. Agricultural Mechanization in Asia, Africa and Latin America. 2017; 48(4):71-5.

Clearinghouse TA. Small-scale farm mechanization catalogue. Clearinghouse Technical Report Series 015. Cotonou, Benin: Technologies for African Agricultural Transformation, Clearinghouse Office, IITA; 2022:1-28.

Harerimana L, Mishra IM, Kumar SP, Parray RA, Lande SD, Bandyopdhayaya K, et al. RSMSOMT response surface methodology optimization of operational and machine parameters of solar powered multicrop thresher on soybean. Agricultural Engineering International: CIGR Journal. 2024; 26(1):115-27.

Yogaraj D, Subesh T, Devaraj A, Chowdary KS, Khakha U, Alone SP, et al. Design and analysis of compact paddy harvester machine. Materials Today: Proceedings. 2022; 62:1430-4.

Lee GH, Moon BE, Basak JK, Kim NE, Paudel B, Jeon SW, et al. Assessment of load on threshing bar during soybean pod threshing. Journal of Biosystems Engineering. 2023; 48(4):478-86.

Harerimana L. Studies on design and development of solar powered multicrop thresher for small holdings. Doctoral Dissertation, Division of Agricultural Engineering ICAR-Indian Agricultural Research Institute, New Delhi. 2022.

Ma L, Xie F, Liu D, Wang X, Zhang Z. An application of artificial neural network for predicting threshing performance in a flexible threshing device. Agriculture. 2023; 13(4):1-15.

Hanna HM, Quick GR. Grain harvesting machinery design, handbook of farm, dairy and food machinery (Kutz M, ed). William Andrew Inc, Delmar, NY; 2007.

Chandra RJ, Masilamani P, Suthakar B, Rajkumar P, Sivakumar SD, Manonmani V. Effect of moisture content on combine harvested seed crop and its quality. Journal of Experimental Agriculture International. 2024; 46(3):114-38.

Mussema R, Diro S, Erko B, Desalegn T, Teshale D, Wake RD, et al. Soybean value chain analysis in Ethiopia: a qualitative study research. Research Report Number-134, EIAR. 2022.

Wang F, Liu Y, Li Y, Ji K. Research and experiment on variable-diameter threshing drum with movable radial plates for combine harvester. Agriculture. 2023; 13(8):1-16.

Marina I, Sujadi H, Indriana KR. Optimizing soybean cultivation efficiency through agricultural technology integration in plant monitoring system. Greenation International Journal of Engineering Science. 2023; 1(2):115-27.

Lal S, Jogdand SV, Naik RK, Koumary NK. Comparative evaluation of performance of traditional method and commercially available pedal operated paddy thresher with the developed machine. Journal of Scientific Research and Reports. 2024; 30(8):647-55.

Prabakaran MP, Kannan GR. Optimization of laser welding process parameters in dissimilar joint of stainless steel AISI316/AISI1018 low carbon steel to attain the maximum level of mechanical properties through PWHT. Optics & Laser Technology. 2019; 112:314-22.

Spotts MF, Shoup TE, Hornberger LE, Kazmer DO. Design of machine elements. Journal of Mechanical Design. 2004; 126(1):201.

https://extension.sdstate.edu/about/our-experts/sdsu-extension. Accessed 26 December 2024.

Martey E. Soil fertility management and economics of soybean in Ghana. Doctoral Dissertation, University of Illinois at Urbana-Champaign. 2019.

Ilori TA, Dauda TO, Adewumi IO. Machine crop parameters’ model of spike-tooth thresher for soybean. World. 2020; 8(3):97-104.

Liu P, Jin C, Liu Z, Zhang GY, Cai ZY, Kang Y, et al. Optimization of field cleaning parameters of soybean combine harvester. Transactions of The Chinese Society of Agricultural Engineering. 2020; 36:35-45.

Lubag M, Bonifacio J, Tan JM, Concepcion R, Mababangloob GR, Galang JG, et al. Diversified impacts of enabling a technology-intensified agricultural supply chain on the quality of life in hinterland communities. Sustainability. 2023; 15(17):1-26.

Akbar MF, Fahria I. Study on identification and projection of food commodity price cycles during the COVID-19 pandemic period as a study of supervision aspects of food product marketing in Bangka Belitung. Society. 2022; 10(1):45-64.

Acciaro M, Bardi A, Cusano MI, Ferrari C, Tei A. Contested port hinterlands: an empirical survey on Adriatic seaports. Case Studies on Transport Policy. 2017; 5(2):342-50.

Humang WP. Performance of access road transportation network from Hinterland to Tanjung Ringgit port, Palopo city. Transportation Research News. 2018; 30(1):35-42.

Sulistyorini R. Multi criteria analysis as a method for selecting alternative road sections in lampung province. Journal of Civil Engineering, University of Lampung. 2010; 14(3):147-56.

Bergqvist R, Macharis C, Meers D, Woxenius J. Making hinterland transport more sustainable a multi actor multi criteria analysis. Research in Transportation Business & Management. 2015; 14:80-9.

Marcelino P, Antunes MD, Fortunato E, Gomes MC. Development of a multi criteria decision analysis model for pavement maintenance at the network level: application of the MACBETH approach. Frontiers in Built Environment. 2019; 5(6):1-10.

Octaviansyah D, Buchari E, Arliansyah J, Nawawi N. Multi-criteria analysis as a method for selecting the best route of Hinterland connections: case study in south Sumatra, Indonesia. The Asian Journal of Shipping and Logistics. 2024; 40(1):22-9.

Kambey SF, Kawet L, Sumarauw JS. Analysis of cabbage supply chain in Rurukan sub-district, Tomohon city. EMBA Journal: Journal of Economic, Management, Business and Accounting Research. 2016; 4(3):303-409.

Lupi M, Pratelli A, Campi F, Ceccotti A, Farina A. The “Island formation” within the Hinterland of a port system: the case of the Padan plain in Italy. Sustainability. 2021; 13(9):1-28.

Dadashpoor H, Arasteh M. Core-port connectivity: towards shaping a national Hinterland in a West Asia country. Transport Policy. 2020; 88:57-68.

Anang A, Jeevan J. The classification of seaport-Hinterland in Johor port and port of Tanjung Pelepas. Advances in Transportation and Logistics Research. 2018; 1:959-74.

Nguyen LC, Thai VV, Nguyen DM, Tran MD. Evaluating the role of dry ports in the port-Hinterland settings: conceptual framework and the case of Vietnam. The Asian Journal of Shipping and Logistics. 2021; 37(4):307-20.

Ortega J, Moslem S, Palaguachi J, Ortega M, Campisi T, Torrisi V. An integrated multi criteria decision making model for evaluating park-and-ride facility location issue: a case study for Cuenca city in Ecuador. Sustainability. 2021; 13(13):1-16.

Sameer YM, Abed AN, Sayl KN. Highway route selection using GIS and analytical hierarchy process case study Ramadi Heet rural highway. In journal of physics: conference series 2021 (pp. 1-14). IOP Publishing.

Behdani B, Wiegmans B, Roso V, Haralambides H. Port-hinterland transport and logistics: emerging trends and frontier research. Maritime Economics & Logistics. 2020; 22:1-25.

Bouchery Y, Woxenius J, Fransoo JC. Identifying the market areas of port-centric logistics and Hinterland intermodal transportation. European Journal of Operational Research. 2020; 285(2):599-611.

Vilke S, Krpan L, Milković M. Application of the multi-criteria analysis in the process of road route evaluation. Tehnički vjesnik. 2018; 25(6):1851-9.

Broniewicz E, Ogrodnik K. Multi-criteria analysis of transport infrastructure projects. Transportation Research part D: Transport and Environment. 2020; 83:1-15.

Zhao H, Yu N, Zhu S. International land-sea trade corridor for sustainable transportation: a review of recent literature. Cleaner Logistics and Supply Chain. 2023; 6:1-11.

Caballini C, Benzi M. Fast corridors: innovative customs processes and technology to increase supply chain competitiveness. the case of IKEA Italy. Transportation Research Interdisciplinary Perspectives. 2023; 21:1-16.

Malisan J, Marpaung E, Hutapea G, Puriningsih FS, Arianto D. Development of short sea shipping in the north coast of Java Island, Indonesia as a potential market. Transportation Research Interdisciplinary Perspectives. 2023; 18:1-10.

Park Y, Dossani R. Port infrastructure and supply chain integration under the belt and road initiative: role of Colombo port in the apparel industry in South Asia. Transportation Research Procedia. 2020; 48:307-26.

De ART, De MMC, Ojiako U, Chipulu M, Marshall A, Dweiri F. A flexible cost model for seaport-hinterland decisions in container shipping. Research in Transportation Business & Management. 2023; 49:101016.

Saeed N, Hoff A, Larsen OI. Analysis of hinterland transport strategies when exporting perishable products. Research in Transportation Business & Management. 2022; 43:1-9.

Shi X, Li H. Developing the port hinterland: different perspectives and their application to Shenzhen port, China. Research in Transportation Business & Management. 2016; 19:42-50.

Raimbault N. From regional planning to port regionalization and urban logistics. the Inland port and the governance of logistics development in the Paris region. Journal of Transport Geography. 2019; 78:205-13.

Frémont A, Franc P. Hinterland transportation in Europe: combined transport versus road transport. Journal of Transport Geography. 2010; 18(4):548-56.

Guo T, Liu P, Wang C, Xie J, Du J, Lim MK. Toward sustainable port-Hinterland transportation: a holistic approach to design modal shift policy mixes. Transportation Research Part A: Policy and Practice. 2023; 174:1-15.

Gao T, Tian J, Huang C, Wu H, Xu X, Liu C. The impact of new western land and sea corridor development on port deep Hinterland transport service and route selection. Ocean & Coastal Management. 2024; 247:106910.

Yu J, Voß S, Cammin P. Cruise passenger-oriented evaluation system for the public transport of Hinterland destinations. Transportation Research Procedia. 2022; 62:615-23.

Urbanyi-popiołek I. Cruise industry in the Baltic sea region, the challenges for ports in the context of sustainable logistics and ecological aspects. Transportation Research Procedia. 2019; 39:544-53.

Garg CP, Kashav V. Assessment of sustainable initiatives in the containerized freight railways of India using fuzzy AHP framework. Transportation Research Procedia. 2020; 48:522-39.

https://babel.bps.go.id/id/publication/2024/02/28/7b80421df62e24a5f2b66c73/provinsi-kepulauan-bangka-belitung-dalam-angka-2024.html. Accessed 20 December 2024.

https://digi-lib.stekom.ac.id/assets/dokumen/ebook/feb_35efe6a47227d6031a75569c2f3f39d44fe2db43_1652079047.pdf. Accessed 20 December 2024.

Saaty TL. Decision making with the analytic hierarchy process. International Journal of Services Sciences. 2008; 1(1):83-98.

Saaty TL, Sodenkamp M. The analytic hierarchy and analytic network measurement processes: the measurement of intangibles: decision making under benefits, opportunities, costs and risks. In Handbook of multicriteria analysis 2010 (pp. 91-166). Berlin, Heidelberg: Springer Berlin Heidelberg.

Tabucanon MT, Lee HM. Multiple criteria evaluation of transportation system improvement projects: the case of Korea. Journal of Advanced Transportation. 1995; 29(1):127-43.

Majstorović A, Jajac N. Maintenance management model for nonurban road network. Infrastructures. 2022; 7(6):1-16.

Alvi IH, Li Q, Hu H, Onyekwena CC, Hou Y, Hakuzweyezu T, et al. A critical review of the advancements in acid-activated metakaolin geopolymers. Construction and Building Materials. 2024; 421:135609.

Bheel N, Chohan IM, Ghoto AA, Abbasi SA, Tag-eldin EM, Almujibah HR, et al. Synergistic effect of recycling waste coconut shell ash, metakaolin, and calcined clay as supplementary cementitious material on hardened properties and embodied carbon of high strength concrete. Case Studies in Construction Materials. 2024; 20:1-16.

Samal AK, Panigrahi M, Ganguly RI, Dash RR. Historical development of construction materials–from stone age to modern age. Development of Geopolymer from Pond Ash‐Thermal Power Plant Waste: Novel Constructional Materials for Civil Engineers. 2023: 1-70.

Ziolkowski P. Computational complexity and its influence on predictive capabilities of machine learning models for concrete mix design. Materials. 2023; 16(17):1-36.

Wu Y, Pieralisi R, Sandoval FG, López-carreño RD, Pujadas P. Optimizing pervious concrete with machine learning: predicting permeability and compressive strength using artificial neural networks. Construction and Building Materials. 2024; 443:1-17.

Mandal S, Shiuly A, Sau D, Mondal AK, Sarkar K. Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation. AI in Civil Engineering. 2024; 3(1):1-24.

Choi JH, Kim D, Ko MS, Lee DE, Wi K, Lee HS. Compressive strength prediction of ternary-blended concrete using deep neural network with tuned hyperparameters. Journal of Building Engineering. 2023; 75:1-24.

Maruthai SM, Ayyadurai A, Muthu D, Palanisami S. Optimizing concrete performance through metakaolin and flyash incorporation: a critical appraisal of regression modeling and design code applicability. Matéria (Rio de Janeiro). 2024; 29:1-22.

Ayyadurai A, Maruthai SM, Muthu D. Effect of fly ash and banana fiber on mechanical properties of concrete pavers. Građevinar. 2024; 76(3):211-22.

Li Q, Ren Z, Su X, Feng Y, Xu T, Zheng Z, et al. Improving sulfate and chloride resistance in eco-friendly marine concrete: alkali-activated slag system with mineral admixtures. Construction and Building Materials. 2024; 411:134333.

Saludung A, Azeyanagi T, Ogawa Y, Kawai K. Mechanical and microstructural evolutions of fly ash/slag-based geopolymer at high temperatures: effect of curing conditions. Ceramics International. 2023; 49(2):2091-101.

Boukhari ME, Merroun O, Maalouf C, Bogard F, Kissi B. Exploring the impact of partial sand replacement with olive waste on mechanical and thermal properties of sustainable concrete. Cleaner Materials. 2023; 9:1-14.

Zhang Y, Liu X, Xu Z, Yuan W, Xu Y, Yao Z, et al. Early-age cracking of fly ash and GGBFS concrete due to shrinkage, creep, and thermal effects: a review. Materials. 2024; 17(10):1-19.

Neguja D, Senthilrajan A. Sustainable self-consolidating green concrete: neural-network and fuzzy clustering techniques for cement replacement. Matéria (Rio de Janeiro). 2024; 29(3):1-20.

Pandit P, Prashanth S, Katpady DN. Durability of alkali-activated fly ash-slag concrete-state of art. Innovative Infrastructure Solutions. 2024; 9(6):1-21.

Ijaz N, Ye WM, Ur RZ, Ijaz Z, Junaid MF. Global insights into micro-macro mechanisms and environmental implications of limestone calcined clay cement (LC3) for sustainable construction applications. Science of the Total Environment. 2024; 907:1-33.

Dan AK, Bhattacharjee D, Ghosh S, Behera SS, Bindhani BK, Das D, et al. Prospective utilization of coal fly ash for making advanced materials. In Clean Coal Technologies: Beneficiation, Utilization, Transport Phenomena and Prospective 2021 (pp. 511-31). Cham: Springer International Publishing.

Terán-cuadrado G, Tahir F, Nurdiawati A, Almarshoud MA, Al-ghamdi SG. Current and potential materials for the low-carbon cement production: life cycle assessment perspective. Journal of Building Engineering. 2024; 96:110528.

Khankhaje E, Kim T, Jang H, Kim CS, Kim J, Rafieizonooz M. A review of utilization of industrial waste materials as cement replacement in pervious concrete: an alternative approach to sustainable pervious concrete production. Heliyon. 2024:1-23.

Sobuz MH, Meraz MM, Safayet MA, Mim NJ, Mehedi MT, Farsangi EN, et al. Performance evaluation of high-performance self-compacting concrete with waste glass aggregate and metakaolin. Journal of Building Engineering. 2023; 67:105976.

Magudeaswaran P, Kumar V, Krishna KV, Nagasaibaba A, Ravinder R. Investigational studies on the impact of supplementary cementitious materials (SCM) for identifying the strength and durability characteristics in self curing concrete. Materials Today: Proceedings. 2023.

Zhong Q, Tian X, Xie G, Luo X, Peng H. Investigation of setting time and microstructural and mechanical properties of MK/GGBFS-blended geopolymer pastes. Materials. 2022; 15(23):1-19.

Li G, Zhou C, Ahmad W, Usanova KI, Karelina M, Mohamed AM, et al. Fly ash application as supplementary cementitious material: a review. Materials. 2022; 15(7):1-23.

Gopalakrishna B, Pasla D. Durability performance of recycled aggregate geopolymer concrete incorporating fly ash and ground granulated blast furnace slag. Journal of Materials in Civil Engineering. 2024; 36(4):04024037.

Boukhelkhal D, Guendouz M, Bourdot A, Cheriet H, Messaoudi K. Elaboration of bio-based building materials made from recycled olive core. MRS Energy & Sustainability. 2021; 8:98-109.

Alghamdi H. A review of cementitious alternatives within the development of environmental sustainability associated with cement replacement. Environmental Science and Pollution Research. 2022; 29(28):42433-51.

Garg R, Garg R, Eddy NO, Khan MA, Khan AH, Alomayri T, et al. Mechanical strength and durability analysis of mortars prepared with fly ash and nano-metakaolin. Case Studies in Construction Materials. 2023; 18:1-15.

Yang H, Zhu J, Tao Y, Wang Z, Zheng Q. Effect of the dry-wet cycle on the performance of marine waste silt solidified by calcium carbide residue and plant ash. Journal of Marine Science and Engineering. 2022; 10(10):1-28.

Akbulut ZF, Yavuz D, Tawfik TA, Smarzewski P, Guler S. Enhancing concrete performance through sustainable utilization of class-C and class-F fly ash: a comprehensive review. Sustainability. 2024; 16(12):1-20.

Liu M, Dai W, Jin W, Li M, Yang X, Han Y, et al. Mix proportion design and carbon emission assessment of high strength geopolymer concrete based on ternary solid waste. Scientific Reports. 2024; 14(1):1-14.

Mi R, Pan G, Liew KM, Kuang T. Utilizing recycled aggregate concrete in sustainable construction for a required compressive strength ratio. Journal of Cleaner Production. 2020; 276:124249.

Balasundaram K, Sharma M. Technology for mercury removal from flue gas of coal based thermal power plants: a comprehensive review. Critical Reviews in Environmental Science and Technology. 2019; 49(18):1700-36.

Liu M, Dai W, Zhong C, Yang X. Study on mechanical properties and microstructure of manufactured sand reactive powder concrete with different curing methods. Materials Letters. 2023; 335:133818.

Gong F, Jiang X, Gamil Y, Iftikhar B, Thomas BS. An overview on spalling behavior, mechanism, residual strength and microstructure of fiber reinforced concrete under high temperatures. Frontiers in Materials. 2023; 10:1-26.

Luhar I, Luhar S. A comprehensive review on fly ash-based geopolymer. Journal of Composites Science. 2022; 6(8):1-59.

Han F, Zhang H, Li Z, Pang Z. Effect of the fineness of limestone powder on the properties of calcium sulfoaluminate cement. Journal of Thermal Analysis and Calorimetry. 2023; 148(10):4033-47.

Ndahirwa D, Zmamou H, Lenormand H, Leblanc N. The role of supplementary cementitious materials in hydration, durability and shrinkage of cement-based materials, their environmental and economic benefits: a review. Cleaner Materials. 2022; 5:1-14.

Ayyadurai A, Saravanan MM, Devi M. Effect on stability of asphalt using COVID-19 single use face mask and saline tube waste. International Journal of Advanced Technology and Engineering Exploration. 2023; 10(103):792-807.

Ansari MA, Shariq M, Mahdi F. Multioptimization of FA-based geopolymer concrete mixes: a synergistic approach using gray relational analysis and principal component analysis. Journal of Structural Design and Construction Practice. 2025; 30(1):04024101.

Obayes O, Gad E, Pokharel T, Lee J, Abdouka K. Evaluation of concrete material properties at early age. CivilEng. 2020; 1(3):326-50.

Zhao J, Trindade AC, Liebscher M, De ASF, Mechtcherine V. A review of the role of elevated temperatures on the mechanical properties of fiber-reinforced geopolymer (FRG) composites. Cement and Concrete Composites. 2023; 137:104885.

ACI Committee 3. Building code requirements for structural concrete (ACI 318-08) and commentary. American Concrete Institute. 2008.

Govardhan C, Gayathri V. Experimental investigation on ternary blended recycled aggregate concrete using glass fibers. Buildings. 2023; 13(8):1-23.

GB 50010-2010. Chinese standard code for design of concrete structures. 2015.

https://www.fib-international.org/publications/fib-bulletins/model-code-2010-first-complete-draft,-vol-1-pdf-detail.html. Accessed 20 December 2024.

Standard I. Plain and reinforced concrete-code of practice. New Delhi: Bureau of Indian Standards. 2000.

Indian Standard IS. 516 (1959) Method of tests for strength of concrete. New Delhi. 2002.

Subramaniam DN, Sathiparan N. Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis. International Journal of Pavement Engineering. 2023; 24(2):2075867.

Alamri M, Ali T, Ahmed H, Qureshi MZ, Elmagarhe A, Khan MA, et al. Enhancing the engineering characteristics of sustainable recycled aggregate concrete using fly ash, metakaolin and silica fume. Heliyon. 2024; 10(7):1-16.

Khan MI, Abbas YM, Abellan-garcia J, Castro-cabeza A. Eco-efficient ultra-high-performance concrete formulation utilizing electric arc furnace slag and recycled glass powder–advanced analytics and lifecycle perspectives. Journal of Materials Research and Technology. 2024; 32:362-77.

Jin R, Chen Q. An investigation of current status of green concrete in the construction industry. In 49th ASC annual international conference proceedings 2013 (pp. 1-8).

Rashid S, Singh M. An investigation on carbon dioxide incorporated sustainable ready-mix concrete using OPC and PPC. Arabian Journal for Science and Engineering. 2023; 48(10):14213-36.

Gao T, Shen L, Shen M, Liu L, Chen F, Gao L. Evolution and projection of CO2 emissions for Chinas cement industry from 1980 to 2020. Renewable and Sustainable Energy Reviews. 2017; 74:522-37.

Sukmak P, Sukmak G, Horpibulsuk S, Setkit M, Kassawat S, Arulrajah A. Palm oil fuel ash-soft soil geopolymer for subgrade applications: strength and microstructural evaluation. Road Materials and Pavement Design. 2019; 20(1):110-31.

Ding GK. Sustainable construction-the role of environmental assessment tools. Journal of Environmental Management. 2008; 86(3):451-64.

Fernando A, Selvaranjan K, Srikanth G, Gamage JC. Development of high strength recycled aggregate concrete-composite effects of fly ash, silica fume and rice husk ash as pozzolans. Materials and Structures. 2022; 55(7):1-22.

Bahoria BV, Parbat DK, Nagarnaik PB. XRD analysis of natural sand, quarry dust, waste plastic (ldpe) to be used as a fine aggregate in concrete. Materials Today: Proceedings. 2018; 5(1):1432-8.

https://environmentclearance.nic.in/writereaddata/SandMiningManagementGuidelines2016.pdf. Accessed 22 December 2024.

Dey S, Kumar VP, Goud KR, Basha SK. State of art review on self compacting concrete using mineral admixtures. Journal of Building Pathology and Rehabilitation. 202; 6(1):18.

Diop B, Mélinge Y, Molez L, Jauberthie R, Bouguerra A. Durability of mortars with natural fillers in aggressive environnement. In structural faults and repair 2008 (pp.1-8). HAL Open Science.

Shokravi H, Mohammadyan-yasouj SE, Koloor SS, Petrů M, Heidarrezaei M. Effect of alumina additives on mechanical and fresh properties of self-compacting concrete: a review. Processes. 2021; 9(3):1-22.

Singh A, Mehta PK, Kumar R. Recycled coarse aggregate and silica fume used in sustainable self-compacting concrete. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(96):1581-96.

Busari AA, Akinmusuru JO, Dahunsi BI. Review of sustainability in self-compacting concrete: the use of waste and mineral additives as supplementary cementitious materials and aggregates. Portugaliae Electrochimica Acta. 2018; 36(3):147-62.

Adebakin IH, Gunasekaran K, Annadurai R. Mechanical properties of self-compacting coconut shell concrete blended with fly ash. Asian Journal of Civil Engineering. 2019; 20:113-24.

Bouzoubaâ N, Lachemi M. Self-compacting concrete incorporating high volumes of class F fly ash: preliminary results. Cement and Concrete Research. 2001; 31(3):413-20.

Yao ZT, Ji XS, Sarker PK, Tang JH, Ge LQ, Xia MS, et al. A comprehensive review on the applications of coal fly ash. Earth-Science Reviews. 2015; 141:105-21.

Hossain SS, Mathur L, Roy PK. Rice husk/rice husk ash as an alternative source of silica in ceramics: a review. Journal of Asian Ceramic Societies. 2018; 6(4):299-313.

Kosior‐kazberuk M, Lelusz M. Strength development of concrete with fly ash addition. Journal of Civil Engineering and Management. 2007; 13(2):115-22.

Tripathi D, Kumar R, Mehta PK. Development of an environmental-friendly durable self-compacting concrete. Environmental Science and Pollution Research. 2022; 29(36):54167-80.

De MPR, Foiato M, Prudêncio JLR. Ecological, fresh state and long-term mechanical properties of high-volume fly ash high-performance self-compacting concrete. Construction and Building Materials. 2019; 203:282-93.

Patil S, Ramesh B, Sathish T, Saravanan A, Almujibah H, Panchal H, et al. Evaluation and optimization of mechanical properties of laterized concrete containing fly ash and steel fiber using Taguchi robust design method. Alexandria Engineering Journal. 2024; 87:682-706.

Rao MD, Dey S, Rao BP. Characterization of fiber reinforced self-compacting concrete by fly ash and cement. Chemistry of Inorganic Materials. 2023; 1:1-14.

Mohammed AM, Asaad DS, Al-hadithi AI. Experimental and statistical evaluation of rheological properties of self-compacting concrete containing fly ash and ground granulated blast furnace slag. Journal of King Saud University-Engineering Sciences. 2022; 34(6):388-97.

Mohamed OA, Najm O, Ahmed E. Alkali-activated slag & fly ash as sustainable alternatives to OPC: sorptivity and strength development characteristics of mortar. Cleaner Materials. 2023; 8:1-21.

Abellan-garcia J, Martinez DM, Khan MI, Abbas YM, Pellicer-martínez F. Environmentally friendly use of rice husk ash and recycled glass waste to produce ultra-high-performance concrete. Journal of Materials Research and Technology. 2023; 25:1869-81.

Ahmadi MA, Alidoust O, Sadrinejad I, Nayeri M. Development of mechanical properties of self compacting concrete contain rice husk ash. International Journal of Computer, Information, and Systems Science, and Engineering. 2007; 1(4):168-71.

Anjos MA, Camões A, Campos P, Azeredo GA, Ferreira RL. Effect of high volume fly ash and metakaolin with and without hydrated lime on the properties of self-compacting concrete. Journal of Building Engineering. 2020; 27:100985.

Tayeh BA, Hakamy AA, Fattouh MS, Mostafa SA. The effect of using nano agriculture wastes on microstructure and electrochemical performance of ultra-high-performance fiber reinforced self-compacting concrete under normal and acceleration conditions. Case Studies in Construction Materials. 2023; 18:1-17.

Mosaberpanah MA, Umar SA. Utilizing rice husk ash as supplement to cementitious materials on performance of ultra high performance concrete–a review. Materials Today Sustainability. 2020; 7:100030.

Venkatanarayanan HK, Rangaraju PR. Material characterization studies on low-and high-carbon rice husk ash and their performance in Portland cement mixtures. Advances in Civil Engineering Materials. 2013; 2(1):266-87.

Jongpradist P, Homtragoon W, Sukkarak R, Kongkitkul W, Jamsawang P. Efficiency of rice husk ash as cementitious material in high‐strength cement‐admixed clay. Advances in Civil Engineering. 2018; 2018(1):1-11.

Rahman ME, Nagaratnam BH, Pakrashi V, Muntohar AS, Sujan D, Chai N, et al. A preliminary study on self compacting concrete using RHA. In proceedings of the 3rd CUTSE international conference 2011 (pp. 495-500). Curtin University.

Nayak DK, Abhilash PP, Singh R, Kumar R, Kumar V. Fly ash for sustainable construction: a review of fly ash concrete and its beneficial use case studies. Cleaner Materials. 2022; 6:1-35.

Lakhani R, Kumar R, Tomar P. Utilization of stone waste in the development of value added products: a state of the art review. Journal of Engineering Science & Technology Review. 2014; 7(3):180-7.

Hamid NJ, Kadir AA, Kamil NA, Hassan MI. Overview on the utilization of quarry dust as a replacement material in construction industry. International Journal of Integrated Engineering. 2018; 10(2):112-7.

Singh SK, Srivastava V, Agarwal VC, Kumar R, Mehta PK. An experimental investigation on stone dust as partial replacement of fine aggregate in concrete. Journal of Academia and Industrial Research. 2014; 3(5):229-32.

Eren Ö, Marar K. Effects of limestone crusher dust and steel fibers on concrete. Construction and Building Materials. 2009; 23(2):981-8.

Bh AR, JE M. Impact of quarry dust and fly ash on the fresh and hardened properties of self compacting concrete. International Research Journal of Engineering and Technology. 2015; 2(8):786-95.

Uygunoğlu T, Topçu İB, Çelik AG. Use of waste marble and recycled aggregates in self-compacting concrete for environmental sustainability. Journal of Cleaner Production. 2014; 84:691-700.

Pathak N, Siddique R. Properties of self-compacting-concrete containing fly ash subjected to elevated temperatures. Construction and Building Materials. 2012; 30:274-80.

Wang HY, Huang WL. Durability of self-consolidating concrete using waste LCD glass. Construction and Building Materials. 2010; 24(6):1008-13.

Singh A, Kumar R, Mehta PK, Tripathi D. Mechanical performance of self-compacting concrete with pozzolanic material. In recent advances in structural engineering: select proceedings of NCRASE 2020 (pp. 11-20). Springer Singapore.

Singh A, Kumar R, Mehta PK, Tripathi D. Properties of binary admixture mixed SCC exposed to sulphate environment. In sustainable building materials and construction: select proceedings of ICSBMC 2022 (pp. 129-36). Singapore: Springer Nature Singapore.

Mojtabavi NM, Ooi CY. A novel scan architecture for low power scan‐based testing. VLSI Design. 2015; 2015(1):1-13.

Trivedi R, Dhariwal S, Kumar A. Comparison of various ATPG techniques to determine optimal BIST. In international conference on intelligent circuits and systems 2018 (pp. 93-8). IEEE.

Borda P, Prajapati P. LOC, lOS and LOES at-speed testing methodologies for automatic test pattern generation using transition delay fault model. International Journal of Research in Engineering and Technology. 2014; 3(3):273-7.

Shantagiri PV, Kapur R. Aggressive exclusion of scan flip-flops from compression architecture for better coverage and reduced TDV: a hybrid approach. Journal of Low Power Electronics and Applications. 2019; 9(2):1-25.

Patel P, Rajawat A, Jain P. Comparative analysis of simulation techniques: scan compression and internal scan. International Journal on Cybernetics & Informatics. 2023; 12:15-22.

Xiang D. Test compression for launch-on-capture transition fault testing. ACM Transactions on Design Automation of Electronic Systems. 2023; 29(1):1-20.

Shukla NK, Mayet AM, Raja MR, Parayangat M, Usman M, Verma R, et al. A unified test data volume compression scheme for circular scan architecture using hosted cuckoo optimization. The Journal of Supercomputing. 2024; 80(5):6411-34.

Pandey K. A critical engineering dissection of LOS and LOC at-speed test approaches. In IEEE international test conference India 2020 (pp. 1-7). IEEE.

Murugan SK, Prabha MM. Launch off shift and capture power reduction in transition fault test based on design for testability methods. International Journal of Research in Computer Applications and Robotics, India. 2015; 3(2):40-9.

Thobhani P, Mehta U. Development of ATPG using basic algorithm processes. International Engineering Journal for Research & Development. 2021; 6:1-8.

Yang KC, Lee MT, Wu CH, Li JC. ATPG and test compression for probabilistic circuits. In international symposium on VLSI design, automation and test (VLSI-DAT) 2019 (pp. 1-4). IEEE.

Chandrashekar C, Neelgar BI. Pattern generation techniques for BIST. International Journal of Research and Analytical Reviews. 2020; 7(3):355-9.

Das KS, Zala A. Optimizing cell-aware ATPG pattern volume to keep test cost competitive. In international conference on electronics, computing and communication technologies 2020 (pp. 1-6). IEEE.

Singh VK, Sarkar T, Pradhan SN. Power-aware testing for maximum fault coverage in analog and digital circuits simultaneously. IETE Technical Review. 2022; 39(6):1395-409.

Kumar YG, Kariyappa BS, Kurian MZ. Design, implementation and performance analysis of test pattern generator for built-in self-test using m-GDI technology. Indian Journal of Science and Technology. 2022; 15(5):221-6.

Liang ZJ, Wu YT, Yang YF, Li JC, Chang N, Kumar A, et al. High-speed, low-storage power and thermal predictions for ATPG test patterns. In international test conference 2023 (pp. 206-15). IEEE.

Ramasamy J, Samiappan D. Design and Analysis of Non-vulnerable PRBS Generation with Internal Shift and XOR of LFSR. In international conference on electrical and electronics engineering 2023 (pp. 573-582). Singapore: Springer Nature Singapore.

Ye CS, Zheng SX, Tsai FJ, Wang C, Lee KJ, Cheng WT, et al. Efficient test compression configuration selection. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2021; 41(7):2323-36.

Tsai FJ, Ye CS, Lee KJ, Zheng SX, Huang Y, Cheng WT, et al. Prediction of test pattern count and test data volume for scan architectures under different input channel configurations. In international test conference 2020 (pp. 1-10). IEEE.

Wang F, Gupta SK. An effective and efficient automatic test pattern generation (ATPG) paradigm for certifying performance of RSFQ circuits. IEEE Transactions on Applied Superconductivity. 2020; 30(5):1-11.

Wang F, Gupta S. Automatic test pattern generation for timing verification and delay testing of RSFQ circuits. In 37th VLSI test symposium 2019 (pp. 1-6). IEEE.

Kung YC, Lee KJ, Reddy SM. Generating single-and double-pattern tests for multiple CMOS fault models in one ATPG run. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 2019; 39(6):1340-5.

Chen KH, Chen CY, Huang JL. Testability measures considering circuit reconvergence to reduce ATPG runtime. In 22nd international symposium on design and diagnostics of electronic circuits & systems 2019 (pp. 1-12). IEEE.

Nekar H, Siddamal SV. Design and implementation of novel scan architecture for test power reduction. Procedia Computer Science. 2020; 171:2556-62.

Suteddy W, Adiwilaga A, Atmanto DA. Fault coverage testing on the ISCAS’89 S1423 sequential circuit using scan based design and synopsis tetramax. Journal of Computer Engineering, Electronics and Information Technology. 2022; 1(2):103-16.

Kongala S, Gupta A, Walia Y, Jain S. Novel methodology to optimize TAT and resource utilization for ATPG simulations for large SoCs. In international test conference 2023 (pp. 60-4). IEEE.

Mohan M, Shrimali T. Hybrid data approach for selecting effective test cases during the regression testing. International Journal on Smart Sensing and Intelligent Systems. 2017; 10(5):1-24.

Dannina SSR, Kumar S. Efficient GDI RAM design and low power clock splitting based ATPG for BIST. International Journal of Advanced Science and Technology. 2019; 28(19):1152-63.

Zhang G, Yuan Y, Liang F, Wei S, Yang CF. Low cost test pattern generation in scan-based BIST schemes. Electronics. 2019; 8(3):1-11.

Srivastava A, Abraham J. Low capture power at-speed test with local hot spot analysis to reduce over-test. In international test conference 2022 (pp. 446-55). IEEE.

Ni T, Wen X, Amrouch H, Zhuo C, Song P. Introduction to the special issue on design for testability and reliability of security-aware hardware. ACM Transactions on Design Automation of Electronic Systems. 2023; 29(1):1-3.

Pomeranz I. Path unselection for path delay fault test generation. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2022; 31(2):267-75.

Li M, Lin Y, Gupta S. Design for testability (DFT) for RSFQ circuits. In 41st VLSI test symposium 2023 (pp. 1-7). IEEE.

Chauhan J, Panchal C, Suthar H. Scan methodology and ATPG DFT techniques at lower technology node. In international conference on computing methodologies and communication 2017 (pp. 508-14). IEEE.

Gopikrishna K, Jindal P. Test coverage analysis of DFT with EDT and without EDT architecture. In 12th international conference on computing communication and networking technologies 2021 (pp. 1-4). IEEE.

Cai S, Zhou Y, Liu P, Yu F, Wang W. A novel test data compression approach based on bit reversion. IEICE Electronics Express. 2017; 14(13):1-11.

Roy S, Stiene B, Millican SK, Agrawal VD. Improved pseudo-random fault coverage through inversions: a study on test point architectures. Journal of Electronic Testing. 2020; 36:123-33.

Javvaji PK, Tragoudas S. Test pattern generation and critical path selection in the presence of statistical delays. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 2019; 28(1):163-73.

Kundu S, Abraham J. Revisiting test compression configuration in context of multi-core testing using packetized scan network. In 37th international conference on VLSI design and 23rd international conference on embedded systems (VLSID) 2024 (pp. 724-9). IEEE.

Talatule SD, Zode P, Zode P. A secure architecture for the design for testability structures. In 19th international symposium on VLSI design and test 2015 (pp. 1-6). IEEE.

Iwata H, Maeda Y, Matsushima J, Laouamri O, Khanna N, Mayer J, et al. A new framework for RTL test points insertion facilitating a “Shift-Left DFT” strategy. In international test conference 2023 (pp. 1-10). IEEE.

Chao Z, Zhang X, Huang J, Ye J, Cai S, Li H, et al. A fast test compaction method for commercial DFT flow using dedicated pure-MaxSAT solver. In 29th Asia and South Pacific design automation conference 2024 (pp. 503-8). IEEE.

Ahmed N, Tehranipoor M, Ravikumar CP. Enhanced launch-off-capture transition fault testing. In international conference on test 2005. IEEE.

Ansari R, Aghdasi P, Shahnazari A. DFT-based finite element analysis of compressive response in armchair phosphorene nanotubes. Journal of Molecular Graphics and Modelling. 2024; 129:108751.

Pomeranz I. Sharing of topped-off compressed test sets among logic blocks. IEEE Access. 2024; 12:49895-903.

Tran HH, Nguyen TT, Ta HN, Pham DP. A metasurface-based MIMO antenna with compact, wideband, and high isolation characteristics for sub-6 GHz 5G applications. IEEE Access. 2023; 11:67737-44.

Güler C, Bayer KSE. A novel high isolation 4-port compact MIMO antenna with DGS for 5G applications. Micromachines. 2023; 14(7):1-17.

Singh A, Kumar A, Kanaujia BK. High gain and enhanced isolation MIMO antenna with FSS and metasurface. Optik. 2023; 286:170982.

Raj T, Mishra R, Kumar P, Kapoor A. Advances in MIMO antenna design for 5G: avcomprehensive review. Sensors. 2023; 23(14):1-34.

Aliqab K, Armghan A, Alsharari M, Aly MH. Highly decoupled and high gain conformal two-port MIMO antenna for V2X communications. Alexandria Engineering Journal. 2023; 74:599-610.

Elalaouy O, El GM, Foshi J. Mutual coupling reduction of a two-port MIMO antenna using defected ground structure. e-Prime-Advances in Electrical Engineering, Electronics and Energy. 2024; 8:100557.

Kiani SH, Munir ME, Savci HS, Rimli H, Alabdulkreem E, Elmannai H, et al. Dual-polarized wideband 5G N77 band slotted MIMO antenna system for next-generation smartphones. IEEE Access. 2024; 12:34467-76.

Luadang B, Janpangngern P, Pookkapund K, Dentri S, Krairiksh M, Phongcharoenpanich C. Broadband unidirectional twin-element MIMO antenna scheme for mid-band 5G and WLAN laptops. Scientific Reports. 2024; 14(1):1-23.

Roges R, Sharma S, Malik PK, Islam T, Asha S, Das S. A compact circularly polarized dual port MIMO antenna with DGS and parasitic patch for modern wireless communication and IOT applications. Physica Scripta. 2024; 99(5):055515.

Rajavel V, Ghoshal D. Enhancement of off-body communications with a low-SAR, high-gain multiband patch antenna designed with a quad-band artificial magnetic conductor. International Journal of Microwave and Wireless Technologies. 2024; 16(2):318-38.

Hamlbar GH, Kazemi R, Fathy AE. Development of a metasurface-based slot antenna for 5G MIMO applications with minimized cross-polarization and stable radiation patterns through mode manipulation. Scientific Reports. 2024; 14(1):1-18.

Islam T, Alsaleem F, Alsunaydih FN, Alhassoon K. Mutual coupling reduction in compact MIMO antenna operating on 28 GHz by using novel decoupling structure. Micromachines. 2023; 14(11):1-12.

Sharma S, Kumar M. Design and analysis of a 4-port MIMO microstrip patch antenna for 5G mid band applications. Progress In Electromagnetics Research. 2023; 129(2022):231-43.

Kempanna SB, Biradar RC, Kumar P, Kumar P, Pathan S, Ali T. Characteristic-mode-analysis-based compact vase-shaped two-element uwb mimo antenna using a unique DGS for wireless communication. Journal of Sensor and Actuator Networks. 2023; 12(3):1-17.

Saad H, Mosleh MF, Abd-alhameed R. Design a dual polarizations MIMO antenna based on decoupling elements for 5G smart-phones. Journal of Techniques. 2023; 5(1):74-85.

Elechi P, John PR. Improved multiband rectangular microstrip patch antenna for 5G application. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 2022; 14(2):7-14.

Sandi E, Diamah A, Al MM. High isolation MIMO antenna for 5G C-band application by using combination of dielectric resonator, electromagnetic bandgap, and defected ground structure. EURASIP Journal on Wireless Communications and Networking. 2022; 2022(1):1-13.

Hasan MM, Islam MT, Samsuzzaman M, Baharuddin MH, Soliman MS, Alzamil A, et al. Gain and isolation enhancement of a wideband MIMO antenna using metasurface for 5G sub-6 GHz communication systems. Scientific Reports. 2022; 12(1):1-17.

Kaushal V, Birwal A, Patel K. Diversity characteristics of four‐element ring slot‐based MIMO antenna for sub‐6‐GHz applications. ETRI Journal. 2023; 45(4):581-93.

Pallavi HV, Jagadeesh AP. Design and analysis of MIMO patch antenna for 5G wireless communication systems. International Journal of Computer Networks and Communications. 2022; 14(4):41-56.

Kanagasabai M, Shanmuganathan S, Alsath MG, Palaniswamy SK. A novel low‐profile 5G MIMO antenna for vehicular communication. International Journal of Antennas and Propagation. 2022; 2022(1):1-12.

Ahmad A, Choi DY, Ullah S. A compact two elements MIMO antenna for 5G communication. Scientific Reports. 2022; 12(1):1-8.

Emara HM, El DSK, Ghouz HH, Sree MA, Fatah SA. Compact high gain microstrip array antenna using DGS structure for 5G applications. Progress in Electromagnetics Research C. 2023; 130:213-25.

Ranjan P, Yadav S, Bage A. Dual band MIMO antenna for LTE, 4G and sub–6 GHz 5G applications. Facta Universitatis, Series: Electronics and Energetics. 2023; 36(1):43-51.

Rana S, Gautam AK, Sharma S, Batra R. A novel compact ultra-wide band MIMO antenna for WLAN 5G & sub-6GHz wireless applications. In international conference on computing, power and communication technologies 2024 (pp. 1159-64). IEEE.

Salim N, Singh MS, Abed AT, Islam MT. 4X4 MIMO slot antenna spanner shaped low mutual coupling for Wi-Fi 6 and 5G communications. Alexandria Engineering Journal. 2023; 78:141-8.

Aung MS, Hla TT. Two-port wideband MIMO antenna for sub-6GHz 5G applications. In IEEE conference on computer applications 2024 (pp. 1-6). IEEE.

Iriqat S, Yenikaya S, Secmen M. Dual-band 2× 1 monopole antenna array and its MIMO configuration for WiMAX, sub-6 GHz, and sub-7 GHz applications. Electronics. 2024; 13(8):1-20.

Ghosh S, Baghel GS, Swati MV. Design of a highly-isolated, high-gain, compact 4-port MIMO antenna loaded with CSRR and DGS for millimeter wave 5G communications. AEU-International Journal of Electronics and Communications. 2023; 169:154721.

Das GS, Chamuah BB, Beria Y, Kalita PP, Buragohain A. Compact four elements SUB-6 GHz MIMO antenna for 5G applications. Materials Today: Proceedings. 2023.

Madhumitha K, Prabhaa BR, Dharshanya P. Two element MIMO antenna design for wireless applications. In 2nd international conference on advancements in electrical, electronics, communication, computing and automation 2023 (pp. 1-5). IEEE.

Khedr AA, Elnaghi BE, Mohamed AM. Design of a compact dual port 2 x 1 ultra-wideband MIMO antenna for radio frequency energy harvesting based on four" a" shaped slots. Progress in Electromagnetics Research M. 2024; 128:41-9.

Jha P, Kumar A, De A. Two-port miniaturized textile antenna for 5G and WLAN applications. International Journal of Microwave and Wireless Technologies. 2023; 15(8):1443-52.

Qin Y, Zhang L, Mao CX, Zhu H. A compact wideband antenna with suppressed mutual coupling for 5G MIMO applications. IEEE Antennas and Wireless Propagation Letters. 2022; 22(4):938-42.

Kumar A, Pattanayak P, Verma RK, Sabat D, Prasad G. Two-element MIMO antenna system for multiband millimeter-wave, 5G mobile communication, Ka-band, and future 6G applications with SAR analysis. AEU-International Journal of Electronics and Communications. 2023; 171:154876.

Gollamudi NK, Narayana YV, Prasad AM. Compact and asymmetric fed modified hexagonal shaped multiple-input multiple-output (MIMO) antenna for 5G sub: 6 GHz (N77/N78 & N79) and WLAN applications. Analog Integrated Circuits and Signal Processing. 2023; 114(1):103-12.

Singh AK, Pandey A, Mishra PK, Yadav RS. A miniaturized 2× 2 double flare horn shaped mimo antenna with enhanced isolation for k and ka band applications. Progress In Electromagnetics Research M. 2022; 111:159-71.

Kulkarni J, Gangwar RK, Anguera J. Broadband and compact circularly polarized MIMO antenna with concentric rings and oval slots for 5G application. IEEE Access. 2022; 10:29925-36.

Raheel K, Ahmad AW, Khan S, Shah SA, Shah IA, Dalarsson M. Design and performance evaluation of orthogonally polarized corporate feed MIMO antenna array for next-generation communication system. IEEE Access. 2024; 12:30382-97.

Ali A, Munir ME, Nasralla MM, Esmail MA, Al-gburi AJ, Bhatti FA. Design process of a compact tri-band MIMO antenna with wideband characteristics for sub-6 GHz, Ku-band, and millimeter-wave applications. Ain Shams Engineering Journal. 2024; 15(3):1-13.

Singh G, Abrol A, Kumar S, Kanaujia BK, Pandey VK. Isolation enhancement in a two-element MIMO antenna using electromagnetic metamaterial. In international conference on device intelligence, computing and communication technologies 2023 (pp. 131-5). IEEE.

https://www.indiatoday.in/india/story/air-pollution-health-risk-india-annual-economic-cost-over-usd-150-billion-1928843-2022-03-24. Accessed 10 January 2023.

Lu F, Xu D, Cheng Y, Dong S, Guo C, Jiang X, et al. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environmental Research. 2015; 136:196-204.

https://www.msn.com/en-in/news/other/summer-action-plan-for-pollution-to-be-submitted-to-delhi-cm-arvind-kejriwal-on-april-24/ar-AA1a7DWL. Accessed 15 February 2023.

www.cpcb.nic.in. Accessed 20 May 2022.

Yin C, Liu K, Zhang Q, Hu K, Yang Z, Yang L, et al. SARIMA-based medium-and long-term load forecasting. Strategic Planning for Energy and the Environment. 2023; 42(2):283-306.

Pant P, Shukla A, Kohl SD, Chow JC, Watson JG, Harrison RM. Characterization of ambient PM2.5 at a pollution hotspot in New Delhi, India and inference of sources. Atmospheric Environment. 2015; 109:178-89.

https://www.outlookindia.com/,www.outlookindia.com/website/story/india-news-delhi-air-quality-will-continue-to-remain-bad-in-coming-winters-5-key-reasons/400980. Accessed 14 March 2021.

Hill W, Lim EL, Weeden CE, Lee C, Augustine M, Chen K, et al. Lung adenocarcinoma promotion by air pollutants. Nature. 2023; 616(7955):159-67.

Pope IIICA, Dockery DW. Health effects of fine particulate air pollution: lines that connect. Journal of the Air & Waste Management Association. 2006; 56(6):709-42.

Singh T, Kaur A, Katyal SK, Walia SK, Dhand G, Sheoran K, et al. Exploring the relationship between air quality index and lung cancer mortality in India: predictive modeling and impact assessment. Scientific Reports. 2023; 13(1):1-12.

https://www.indiaspend.com/pollution/india-has-9-of-worlds-10-most-polluted-cities-but-few-air-quality-monitors-792521. Accessed 14 March 2021.

Selokar A, Ramachandran B, Elangovan KN, Varma BD. PM 2.5 particulate matter and its effects in Delhi/NCR. Materials Today: Proceedings. 2020; 33:4566-72.

Kumar K, Pande BP. Air pollution prediction with machine learning: a case study of Indian cities. International Journal of Environmental Science and Technology. 2023; 20(5):5333-48.

Kothandaraman D, Praveena N, Varadarajkumar K, Madhav RB, Dhabliya D, Satla S, et al. Intelligent forecasting of air quality and pollution prediction using machine learning. Adsorption Science & Technology. 2022; 2022:1-15.

Sharma R, Shilimkar G, Pisal S. Air quality prediction by machine learning. International Journal of Scientific Research in Science and Technology. 2021; 8:486-92.

Desai VP, Kamat RK, Oza KS. Rainfall modeling and prediction using neural networks: a case study of Maharashtra. Disaster Advances. 2022; 15(3):39-43.

Zhu D, Cai C, Yang T, Zhou X. A machine learning approach for air quality prediction: model regularization and optimization. Big Data and Cognitive Computing. 2018; 2(1):1-15.

Sharma E, Deo RC, Prasad R, Parisi AV. A hybrid air quality early-warning framework: an hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms. Science of the Total Environment. 2020; 709:135934.

https://weather.com/en-IN/india/pollution/news/2022-07-07-pollution-in-delhi-patna-5-times-higher-than-cpcb-safe-limits. Accessed 25 December 2022.

Tiwari S, Kumar A, Mantri S, Dey S. Modelling ambient PM2.5 exposure at an ultra-high resolution and associated health burden in megacity Delhi: exposure reduction target for 2030. Environmental Research Letters. 2023; 18(4):1-12.

Chae S, Shin J, Kwon S, Lee S, Kang S, Lee D. PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network. Scientific Reports. 2021; 11(1):1-9.

Subramaniam S, Raju N, Ganesan A, Rajavel N, Chenniappan M, Prakash C, et al. Artificial intelligence technologies for forecasting air pollution and human health: a narrative review. Sustainability. 2022; 14(16):1-36.

Raubitzek S, Corpaci L, Hofer R, Mallinger K. Scaling exponents of time series data: a machine learning approach. Entropy. 2023; 25(12):1-45.

Ansari M, Alam M. An intelligent IoT-cloud-based air pollution forecasting model using univariate time-series analysis. Arabian Journal for Science and Engineering. 2024; 49(3):3135-62.

Zhang H, Srinivasan R. A systematic review of air quality sensors, guidelines, and measurement studies for indoor air quality management. Sustainability. 2020; 12(21):1-38.

Abhilash MS, Thakur A, Gupta D, Sreevidya B. Time series analysis of air pollution in Bengaluru using ARIMA model. In Ambient communications and computer systems 2017 (pp. 413-26). Springer Singapore.

Shah HN, Khan Z, Merchant AA, Moghal M, Shaikh A, Rane P. IOT based air pollution monitoring system. International Journal of Scientific & Engineering Research. 2018; 9(2):62-6.

Arif M, Katafygiotou M, Mazroei A, Kaushik A, Elsarrag E. Impact of indoor environmental quality on occupant well-being and comfort: a review of the literature. International Journal of Sustainable Built Environment. 2016; 5(1):1-11.

Sokhi RS, Moussiopoulos N, Baklanov A, Bartzis J, Coll I, Finardi S, et al. Advances in air quality research–current and emerging challenges. Atmospheric Chemistry and Physics Discussions. 2021; 2021:1-33.

Mane MV, Kumar D, Agarwal K. Detection and prediction of air pollution using machine learning and deep learning techniques. In international conference on computing, communication, and intelligent systems 2022 (pp. 145-50). IEEE.

Liu X, Lu D, Zhang A, Liu Q, Jiang G. Data-driven machine learning in environmental pollution: gains and problems. Environmental Science & Technology. 2022; 56(4):2124-33.

Mahendra HN, Mallikarjunaswamy S, Kumar DM, Kumari S, Kashyap S, Fulwani S, et al. Assessment and prediction of air quality level using ARIMA model: a case study of surat city, Gujarat State, India. Nature Environment & Pollution Technology. 2023; 22(1):199-210.

Molaei SN, Salajegheh A, Khosravi H, Nasiri A, Abadi AR. Prediction of hourly PM10 concentration through a hybrid deep learning-based method. Earth Science Informatics. 2024; 17(1):37-49.

Sarkar P, Saha M. Machine learning-based detection of sudden air pollutant level changes: impacts on public health. International Journal of Information Technology. 2024:1-9.

Mehmood K, Bao Y, Cheng W, Khan MA, Siddique N, Abrar MM, et al. Predicting the quality of air with machine learning approaches: current research priorities and future perspectives. Journal of Cleaner Production. 2022; 379:134656.

Ghazali S, Ismail LH. Air quality prediction using artificial neural network. In proceedings of the international conference on civil environmental engineering sustainability, Johor Bahru, Malaysia 2012 (pp. 1-15).

Maleki H, Sorooshian A, Goudarzi G, Baboli Z, Tahmasebi BY, Rahmati M. Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy. 2019; 21:1341-52.

Natarajan SK, Shanmurthy P, Arockiam D, Balusamy B, Selvarajan S. Optimized machine learning model for air quality index prediction in major cities in India. Scientific Reports. 2024; 14(1):1-18.

Athira V, Geetha P, Vinayakumar R, Soman KP. Deepairnet: applying recurrent networks for air quality prediction. Procedia Computer Science. 2018; 132:1394-403.

Lin YC, Lee SJ, Ouyang CS, Wu CH. Air quality prediction by neuro-fuzzy modeling approach. Applied Soft Computing. 2020; 86:105898.

Bhatt K, Sarangi RK, Kambli M. Comparative analysis of ocean color forecasting models: a case study on Indian southern Peninsula using Sarima and deep learning approaches. International Research Journal of Modernization in Engineering Technology and Science. 2023; 5(11):3167-75.

Parviz L. Comparative evaluation of hybrid sarima and machine learning techniques based on time varying and decomposition of precipitation time series. Journal of Agricultural Science and Technology. 2020; 22(2):563-78.

Huang W, Li T, Liu J, Xie P, Du S, Teng F. An overview of air quality analysis by big data techniques: monitoring, forecasting, and traceability. Information Fusion. 2021; 75:28-40.

Guo Q, He Z, Li S, Li X, Meng J, Hou Z, et al. Air pollution forecasting using artificial and wavelet neural networks with meteorological conditions. Aerosol and Air Quality Research. 2020; 20(6):1429-39.

Yu R, Yang Y, Yang L, Han G, Move OA. RAQ–a random forest approach for predicting air quality in urban sensing systems. Sensors. 2016; 16(1):1-18.

Ukachukwu M, Uzoamaka N, Elochukwu N. Application of machine learning to air pollution studies: a systematic review. Journal of Energy Research and Reviews. 2023; 15(2):1-11.

Terbuch A, O’leary P, Khalili-motlagh-kasmaei N, Auer P, Zöhrer A, Winter V. Detecting anomalous multivariate time-series via hybrid machine learning. IEEE Transactions on Instrumentation and Measurement. 2023; 72:1-11.

Suri RS, Jain AK, Kapoor NR, Kumar A, Arora HC, Kumar K, et al. Air quality prediction-a study using neural network based approach. Journal of Soft Computing in Civil Engineering. 2023; 7(1):93-113.

Mohamad AF, Jasin AM, Asmat A, Canda R, Ismail J, Soom AB. Sales analytics dashboard with ARIMA and SARIMA time series model. In 13th symposium on computer applications & industrial electronics (ISCAIE) 2023 (pp. 106-12). IEEE.

Dubey AK, Kumar A, García-díaz V, Sharma AK, Kanhaiya K. Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments. 2021; 47:101474.

Al-alola SS, Alkadi II, Alogayell HM, Mohamed SA, Ismail IY. Air quality estimation using remote sensing and GIS-spatial technologies along Al-Shamal train pathway, Al-Qurayyat city in Saudi Arabia. Environmental and Sustainability Indicators. 2022; 15:1-10.

Hu D, Li S, Wang M. Object detection in hospital facilities: a comprehensive dataset and performance evaluation. Engineering Applications of Artificial Intelligence. 2023; 123:106223.

Mahaur B, Mishra KK. Small-object detection based on YOLOv5 in autonomous driving systems. Pattern Recognition Letters. 2023; 168:115-22.

Petton E. Object detection: train YOLOv5 on a custom dataset [Internet]. 2023.

Singh G. Train your own YoloV5 object detection model. https://www.analyticsvidhya.com/blog/2021/08/train-your-own-yolov5-object-detection-model/. Accessed 20 January 2025.

Zhu X, Lyu S, Wang X, Zhao Q. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In proceedings of the IEEE/CVF international conference on computer vision 2021 (pp. 2778-88). IEEE.

Kim JH, Kim N, Park YW, Won CS. Object detection and classification based on YOLO-V5 with improved maritime dataset. Journal of Marine Science and Engineering. 2022; 10(3):1-14.

Horvat M, Jelečević L, Gledec G. A comparative study of YOLOv5 models performance for image localization and classification. In central European conference on information and intelligent systems 2022 (pp. 349-56). Faculty of Organization and Informatics Varazdin.

Sirisha U, Praveen SP, Srinivasu PN, Barsocchi P, Bhoi AK. Statistical analysis of design aspects of various YOLO-based deep learning models for object detection. International Journal of Computational Intelligence Systems. 2023; 16(1):1-29.

Majeed F, Khan FZ, Iqbal MJ, Nazir M. Real-time surveillance system based on facial recognition using YOLOv5. In Mohammad Ali Jinnah university international conference on computing 2021 (pp. 1-6). IEEE.

Bhanbhro H, Hooi YK, Hassan Z. Modern approaches towards object detection of complex engineering drawings. In international conference on digital transformation and intelligence 2022 (pp. 1-6). IEEE.

Jia X, Tong Y, Qiao H, Li M, Tong J, Liang B. Fast and accurate object detector for autonomous driving based on improved YOLOv5. Scientific Reports. 2023; 13(1):1-13.

Kumar S, Jailia M, Varshney S, Pathak N, Urooj S, Elmunim NA. Robust vehicle detection based on improved you look only once. Computers, Materials & Continua. 2023; 74(2):3561-77.

Ali ML, Zhang Z. The YOLO framework: a comprehensive review of evolution, applications, and benchmarks in object detection. Computers. 2024; 13(12):1-37.

Iqra, Giri KJ, Javed M. Small object detection in diverse application landscapes: a survey. Multimedia Tools and Applications. 2024; 83:1-36.

Nurminen JK, Rainio K, Numminen JP, Syrjänen T, Paganus N, Honkoila K. Object detection in design diagrams with machine learning. In progress in computer recognition systems 2020 (pp. 27-36). Springer International Publishing.

Pathak AR, Pandey M, Rautaray S. Application of deep learning for object detection. Procedia Computer Science. 2018; 132:1706-17.

Sartipi F. Automatic sorting of recycled aggregate using image processing and object detection. Journal of Construction Materials. 2020; 1(3):15-9.

Shindo T, Watanabe T, Yamada K, Watanabe H. Accuracy improvement of object detection in VVC coded video using YOLO-v7 features. In international conference on artificial intelligence in engineering and technology 2023 (pp. 247-51). IEEE.

Terven J, Córdova-eparza DM, Romero-gonzález JA. A comprehensive review of yolo architectures in computer vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction. 2023; 5(4):1680-716.

Wang CY, Bochkovskiy A, Liao HY. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2023 (pp. 7464-75). IEEE.

Jiang K, Xie T, Yan R, Wen X, Li D, Jiang H, et al. An attention mechanism-improved YOLOv7 object detection algorithm for hemp duck count estimation. Agriculture. 2022; 12(10):1-18.

Zhao H, Zhang H, Zhao Y. Yolov7-sea: object detection of maritime UAV images based on improved yolov7. In proceedings of the IEEE/CVF winter conference on applications of computer vision 2023 (pp. 233-8). IEEE.

Li S, Tao T, Zhang Y, Li M, Qu H. YOLO v7-CS: a YOLO v7-based model for lightweight bayberry target detection count. Agronomy. 2023; 13(12):1-18.

Lai Y, Ma R, Chen Y, Wan T, Jiao R, He H. A pineapple target detection method in a field environment based on improved yolov7. Applied Sciences. 2023; 13(4):1-18.

Xia Y, Nguyen M, Yan WQ. A real-time kiwifruit detection based on improved YOLOv7. In international conference on image and vision computing New Zealand 2022 (pp. 48-61). Cham: Springer Nature Switzerland.

Yang H, Liu Y, Wang S, Qu H, Li N, Wu J, et al. Improved apple fruit target recognition method based on YOLOv7 model. Agriculture. 2023; 13(7):1-21.

Li S, Wang S, Wang P. A small object detection algorithm for traffic signs based on improved YOLOv7. Sensors. 2023; 23(16):1-22.

Liu P, Yin H. Yolov7-peach: an algorithm for immature small yellow peaches detection in complex natural environments. Sensors. 2023; 23(11):1-20.

Tang F, Yang F, Tian X. Long-distance person detection based on YOLOv7. Electronics. 2023; 12(6):1-14.

Kim YE, Huh K, Park YJ, Peck KR, Jung J. Association between vaccination and acute myocardial infarction and ischemic stroke after COVID-19 infection. Jama. 2022; 328(9):887-9.

Rai R, Tripathi V. An overview of breast cancer epidemiology, risk factors, classification, genetics, diagnosis and treatment. Vantage Journal of Thematic Analysis. 2023; 4:45-67.

Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. The British Journal of Radiology. 2022; 95(1130):1-3.

Chamberlin MD. Global oncology: disparities, outcomes and innovations around the globe. An Issue of Hematology/Oncology Clinics of North America, E-Book. Elsevier Health Sciences; 2023.

Nayyar S, Chakole S, Taksande AB, Prasad R, Munjewar PK, Wanjari MB, et al. From awareness to action: a review of efforts to reduce disparities in breast cancer screening. Cureus. 2023; 15(6):1-11.

Sangwan RK, Huda RK, Panigrahi A, Toteja GS, Sharma AK, Thakor M, et al. Strengthening breast cancer screening program through health education of women and capacity building of primary healthcare providers. Frontiers in Public Health. 2023; 11:1-10.

Castorina L, Comis AD, Prestifilippo A, Quartuccio N, Panareo S, Filippi L, et al. Innovations in positron emission tomography and state of the art in the evaluation of breast cancer treatment response. Journal of Clinical Medicine. 2023; 13(1):1-20.

Cecil K, Huppert L, Mukhtar R, Dibble EH, Obrien SR, Ulaner GA, et al. Metabolic positron emission tomography in breast cancer. PET Clinics. 2023; 18(4):473-85.

Fowler AM, Miyake KK, Nakamoto Y. Clinical applications of dedicated breast positron emission tomography. PET Clinics. 2024; 19(1):105-17.

Xu K, Kang H. A review of machine learning approaches for brain positron emission tomography data analysis. Nuclear Medicine and Molecular Imaging. 2024; 58(4):203-12.

Satoh Y, Imokawa T, Fujioka T, Mori M, Yamaga E, Takahashi K, et al. Deep learning for image classification in dedicated breast positron emission tomography (dbPET). Annals of Nuclear Medicine. 2022; 36(4):401-10.

Perron J, Ko JH. Review of quantitative methods for the detection of Alzheimers disease with positron emission tomography. Applied Sciences. 2022; 12(22):1-32.

Balkenende L, Teuwen J, Mann RM. Application of deep learning in breast cancer imaging. In seminars in nuclear medicine 2022 (pp. 584-96). WB Saunders.

Gu B, Yang Z, Du X, Xu X, Ou X, Xia Z, et al. Imaging of tumor stroma using 68Ga-FAPI PET/CT to improve diagnostic accuracy of primary tumors in head and neck cancer of unknown primary: a comparative imaging trial. Journal of Nuclear Medicine. 2024; 65(3):365-71.

Sherman ME, Vierkant RA, Winham SJ, Vachon CM, Carter JM, Pacheco-spann L, et al. Benign breast disease and breast cancer risk in the percutaneous biopsy era. JAMA surgery. 2024; 159(2):193-201.

Inglese M, Ferrante M, Duggento A, Boccato T, Toschi N. Spatiotemporal learning of dynamic positron emission tomography data improves diagnostic accuracy in breast cancer. IEEE Transactions on Radiation and Plasma Medical Sciences. 2023; 7(6):630-7.

Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-centre harmonised multimodal positron emission tomography/computed tomography image radiomic features and machine learning algorithms for non-small cell lung cancer histopathological subtype phenotype decoding. Clinical oncology. 2023; 35(11):713-25.

Qiao X, Jiang C, Li P, Yuan Y, Zeng Q, Bi L, et al. Improving breast tumor segmentation in PET via attentive transformation based normalization. IEEE Journal of Biomedical and Health Informatics. 2022; 26(7):3261-71.

Imokawa T, Satoh Y, Fujioka T, Takahashi K, Mori M, Kubota K, et al. Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images. Breast Cancer. 2023:1-8.

Tsukijima M, Teramoto A, Kojima A, Yamamuro O, Tamaki T, Fujita H. A position-adaptive noise-reduction method using a deep denoising filter bank for dedicated breast positron emission tomography images. Physical and Engineering Sciences in Medicine. 2024; 47(1):73-85.

Sueoka S, Sasada S, Masumoto N, Emi A, Kadoya T, Okada M. Performance of dedicated breast positron emission tomography in the detection of small and low-grade breast cancer. Breast Cancer Research and Treatment. 2021; 187:125-33.

Yurdusev AA, Adem K, Hekim M. Detection and classification of microcalcifications in mammograms images using difference filter and Yolov4 deep learning model. Biomedical Signal Processing and Control. 2023; 80(2):104360.

Nogales A, Perez-lara F, García-tejedor ÁJ. Enhancing breast cancer diagnosis with deep learning and evolutionary algorithms: a comparison of approaches using different thermographic imaging treatments. Multimedia Tools and Applications. 2024; 83(14):42955-71.

Singh L, Alam A. An efficient hybrid methodology for an early detection of breast cancer in digital mammograms. Journal of Ambient Intelligence and Humanized Computing. 2024; 15(1):337-60.

Thakur A, Gupta M, Sinha DK, Mishra KK, Venkatesan VK, Guluwadi S. Transformative breast cancer diagnosis using CNNs with optimized reduceLROnplateau and early stopping enhancements. International Journal of Computational Intelligence Systems. 2024; 17(1):1-18.

Munshi RM, Cascone L, Alturki N, Saidani O, Alshardan A, Umer M. A novel approach for breast cancer detection using optimized ensemble learning framework and XAI. Image and Vision Computing. 2024; 142:104910.

Chugh G, Kumar S, Singh N. TransNet: a comparative study on breast carcinoma diagnosis with classical machine learning and transfer learning paradigm. Multimedia Tools and Applications. 2024; 83(11):33855-77.

Raza A, Ullah N, Khan JA, Assam M, Guzzo A, Aljuaid H. DeepBreastCancerNet: a novel deep learning model for breast cancer detection using ultrasound images. Applied Sciences. 2023; 13(4):1-19.

Sahu A, Das PK, Meher S. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control. 2023; 80(1):104292.

Emam MM, Houssein EH, Samee NA, Alohali MA, Hosney ME. Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved coati optimization algorithm. Expert Systems with Applications. 2024; 255:124581.

Qian L, Bai J, Huang Y, Zeebaree DQ, Saffari A, Zebari DA. Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm. Biomedical Signal Processing and Control. 2024; 87:105492.

Gutierrez C, Owens A, Medeiros L, Dabydeen D, Sritharan N, Phatak P, et al. Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation. Scientific Reports. 2024; 14(1):1-17.

Huang Q, Ding H, Effatparvar M. Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer. Expert Systems with Applications. 2024; 237:121470.

https://www.cancerimagingarchive.net/collection/qin-breast/. Accessed 25 January 2025.

Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. In proceedings of the 1993 SIGMOD international conference on management of data 1993 (pp. 207-16). ACM.

Jashma SPP, Dinesh AU, Reddy NS. Mining frequent itemsets from transaction databases using hybrid switching framework. Multimedia Tools and Applications. 2023; 82(18):27571-91.

Islam MS, Kar PC, Samiullah M, Ahmed CF, Leung CK. Discovering probabilistically weighted sequential patterns in uncertain databases. Applied Intelligence. 2023; 53(6):6525-53.

Huang G, Gan W, Yu PS. TaSPM: targeted sequential pattern mining. ACM Transactions on Knowledge Discovery from Data. 2024; 18(5):1-8.

Fournier-viger P, Faghihi U, Nkambou R, Nguifo EM. CMRules: mining sequential rules common to several sequences. Knowledge-Based Systems. 2012; 25(1):63-76.

Zhao Q, Bhowmick SS. Association rule mining: a survey. Nanyang Technological University, Singapore. 2003; 135:1-20.

Sun L, Cheng R, Cheung DW, Cheng J. Mining uncertain data with probabilistic guarantees. In proceedings of the 16th SIGKDD international conference on knowledge discovery and data mining 2010 (pp. 273-82). ACM.

Bernecker T, Kriegel HP, Renz M, Verhein F, Züfle A. Probabilistic frequent pattern growth for itemset mining in uncertain databases. In international conference on scientific and statistical database management 2012 (pp. 38-55). Berlin, Heidelberg: Springer Berlin Heidelberg.

Leemans SJ, Van ZSJ, Lu X. Partial-order-based process mining: a survey and outlook. Knowledge and Information Systems. 2023; 65(1):1-29.

Fister JI, Fister I, Fister D, Podgorelec V, Salcedo-sanz S. A comprehensive review of visualization methods for association rule mining: taxonomy, challenges, open problems and future ideas. Expert Systems with Applications. 2023; 233:120901.

Wael M, Kassem G. A systematic literature review toward standardization of business rules discovery in the context of process mining. In international conference on technological advancement in embedded and mobile systems 2024 (pp. 33-42). Springer, Cham.

Wang J, Wang C, Huang J, Gao M, Zhou A. Uncertainty-aware self-training for low-resource neural sequence labeling. In proceedings of the AAAI conference on artificial intelligence 2023 (pp. 13682-90). AAAI.

Chen CM, Zhang Z, Ming-tai WJ, Lakshmanna K. High utility periodic frequent pattern mining in multiple sequences. CMES-Computer Modeling in Engineering & Sciences. 2023; 137(1):733-59.

Gao D, Zhu Y, Soares CG. Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate gaussian process. Reliability Engineering & System Safety. 2023; 230:108963.

Yeshchenko A, Mendling J. A survey of approaches for event sequence analysis and visualization. Information Systems. 2024; 120:102283.

Zhang Y, Paquette L. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations. In educational data science: essentials, approaches, and tendencies: proactive education based on empirical big data evidence 2023 (pp. 219-54). Singapore: Springer Nature Singapore.

Tong Y, Chen L, Cheng Y, Yu PS. Mining frequent itemsets over uncertain databases. Proceedings of the VLDB Endowment. 2012; 5(11):1650-61.

Ahmed AU, Ahmed CF, Samiullah M, Adnan N, Leung CK. Mining interesting patterns from uncertain databases. Information Sciences. 2016; 354:60-85.

Bernecker T, Cheng R, Cheung DW, Kriegel HP, Lee SD, Renz M, et al. Model-based probabilistic frequent itemset mining. Knowledge and Information Systems. 2013; 37:181-217.

Huang G, Gan W, Weng J, Yu PS. US-Rule: discovering utility-driven sequential rules. ACM Transactions on Knowledge Discovery from Data. 2023; 17(1):1-22.

Wu Y, Zhao X, Li Y, Guo L, Zhu X, Fournier-viger P, et al. OPR-miner: order-preserving rule mining for time series. IEEE Transactions on Knowledge and Data Engineering. 2023; 35(11):11722-35.

Zhang C, Lyu M, Gan W, Yu PS. Totally-ordered sequential rules for utility maximization. ACM Transactions on Knowledge Discovery from Data. 2024; 18(4):1-23.

Fournier-viger P, Gueniche T, Zida S, Tseng VS. ERMiner: sequential rule mining using equivalence classes. In advances in intelligent data analysis XIII: 13th international symposium, Leuven, Belgium, 2014 (pp. 108-19). Springer International Publishing.

Subrahmanian VS, Pulice C, Brown JF, Bonen-clark J, Subrahmanian VS, Pulice C, et al. Temporal probabilistic rules and policy computation algorithms. A Machine Learning Based Model of Boko Haram. 2021:43-52.

Zhang L, Yang G, Li X. Mining sequential patterns of PM2.5 pollution between 338 cities in China. Journal of Environmental Management. 2020; 262:110341.

Shaheen M, Abdullah U. CARM: context based association rule mining for conventional data. Computers, Materials & Continua. 2021; 68(3):3305-22.

Le T, Vo B, Huynh VN, Nguyen NT, Baik SW. Mining top-k frequent patterns from uncertain databases. Applied Intelligence. 2020; 50:1487-97.

Islam MA, Rafi MR, Azad AA, Ovi JA. Weighted frequent sequential pattern mining. Applied Intelligence. 2022; 52(1):254-81.

Leung CK. Mining uncertain data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2011; 1(4):316-29.

Modi G, Bansal S, Patidar MA. A survey on sequential rule mining techniques. International Journal for Technological Research in Engineering. 2018; 6(3):4825-8.

Diaz-garcia JA, Ruiz MD, Martin-bautista MJ. A survey on the use of association rules mining techniques in textual social media. Artificial Intelligence Review. 2023; 56(2):1175-200.

Aguiar G, Krawczyk B, Cano A. A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. Machine Learning. 2024; 113(7):4165-243.

Khan S, Shaheen M. From data mining to wisdom mining. Journal of Information Science. 2023; 49(4):952-75.

Zhao X, Zhou X, Li G. Automatic database knob tuning: a survey. IEEE Transactions on Knowledge and Data Engineering. 2023; 35(12):12470-90.

Quvvatov B. SQL databases and big data analytics: navigating the data management landscape. Development of Pedagogical Technologies in Modern Sciences. 2024; 3(1):117-24.

Bu C, Zheng X, Zhao X, Xu T, Bai X, Jia Y, et al. GenBase: a nucleotide sequence database. Genomics, Proteomics & Bioinformatics. 2024; 22(3):1-6.

Petrey D, Zhao H, Trudeau SJ, Murray D, Honig B. PrePPI: a structure informed proteome-wide database of protein–protein interactions. Journal of Molecular Biology. 2023; 435(14):168052.

Zhao Z, Yan D, Ng W. Mining probabilistically frequent sequential patterns in large uncertain databases. IEEE Transactions on Knowledge and Data Engineering. 2013; 26(5):1171-84.

Fournier-viger P, Nkambou R, Tseng VS. RuleGrowth: mining sequential rules common to several sequences by pattern-growth. In proceedings of the symposium on applied computing 2011 (pp. 956-61). ACM.

Sen P, Deshpande A, Getoor L. Representing tuple and attribute uncertainty in probabilistic databases. In seventh international conference on data mining workshops 2007 (pp. 507-12). IEEE.

Yingtaweesittikul H, Wu J, Mongia A, Peres R, Ko K, Nagarajan N, et al. CREAMMIST: an integrative probabilistic database for cancer drug response prediction. Nucleic Acids Research. 2023; 51(D1): D1242-8.

Civelli S, Forestieri E, Secondini M. Practical implementation of sequence selection for nonlinear probabilistic shaping. In optical fiber communications conference and exhibition 2023 (pp. 1-3). IEEE.

Marchet C, Limasset A. Scalable sequence database search using partitioned aggregated bloom comb trees. Bioinformatics. 2023; 39(Supplement-1):252-9.

Seddiki I, Nouioua F, Barkat A. Extracting sequential frequent itemsets from probabilistic sequences database. International Journal of Information Technology. 2023; 15(5):2509-15.

Chui CK, Kao B, Hung E. Mining frequent itemsets from uncertain data. In advances in knowledge discovery and data mining: 11th Pacific-Asia conference, PAKDD 2007, Nanjing, China, 2007 (pp. 47-58). Springer Berlin Heidelberg.

https://archive.ics.uci.edu/dataset/73/mushroom. Accessed 21 January 2025.

Woodhouse C. The astrophotography manual image processing fundamentals. Routledge; 2024.

Zhao Y, Jia W, Chen Y, Wang R. Fast blind decontouring network. IEEE Transactions on Circuits and Systems for Video Technology. 2022; 33(2):478-90.

Wang F, Chen F, Tang J, Huang M. Generic skeleton object detection framework with gradient maps. In proceedings of the 15th international conference on digital image processing 2023 (pp. 1-8). ACM.

Demir Y, Kaplan NH. Low-light image enhancement based on sharpening-smoothing image filter. Digital Signal Processing. 2023; 138:104054.

Farahani SS, Reshadinezhad MR, Fatemieh SE. New design for error-resilient approximate multipliers used in image processing in CNTFET technology. The Journal of Supercomputing. 2024; 80(3):3694-712.

Weli MM, Abdullah OM. Hybrid smoothing and sharpening filters using the spatial domain: literature review. International Research Journal of Innovations in Engineering and Technology. 2024; 8(2):51-60.

Li J. A review of fingerprint image enhancement based on Gabor filter. In international conference on image, vision and intelligent systems 2022 (pp. 519-25). Singapore: Springer Nature Singapore.

Wang H. Application of non-local mean image denoising algorithm based on machine learning technology in visual communication design. Journal of Intelligent & Fuzzy Systems. 2023; 45(6):10213-25.

Qiao Z, Wen X, Zhou X, Qin F, Liu S, Gao B, et al. Adaptive iterative guided filtering for suppressing background noise in ptychographical imaging. Optics and Lasers in Engineering. 2023; 160:107233.

Sun Z, Angelis G, Meikle S, Calamante F. MRI tractography-guided PET image reconstruction regularisation using connectome-based nonlocal means filtering. Physics in Medicine & Biology. 2023; 68(13):1-14.

He L, Xie Y, Xie S, Jiang Z, Chen Z. Iterative self-guided image filtering. IEEE Transactions on Circuits and Systems for Video Technology. 2024; 34(8):7537-49.

Liu X, Wu Z, Wang X. The validity analysis of the non-local mean filter and a derived novel denoising method. Virtual Reality & Intelligent Hardware. 2023; 5(4):338-50.

Rekha H, Samundiswary P. Image denoising using fast non-local means filter and multi-thresholding with harmony search algorithm for WSN. International Journal of Advanced Intelligence Paradigms. 2023; 24(1-2):92-109.

Seo KH, Kang SH, Shim J, Lee Y. Optimization of smoothing factor for fast non-local means algorithm in high pitch based low-dose computed tomography images with tin-filter. Radiation Physics and Chemistry. 2023; 206:110762.

Sun Z, Meikle S, Calamante F. CONN-NLM: a novel CONNectome-based non-local means filter for PET-MRI denoising. Frontiers in Neuroscience. 2022; 16:1-14.

Yang K, Chen C, Hu X, Yu H. Denoising algorithm based on multi-feature non-local mean filtering for Monte Carlo rendered images. Journal of System Simulation. 2022; 34(6):1259-66.

Thakur N, Khan NU, Sharma SD. A two phase ultrasound image de-speckling framework by nonlocal means on anisotropic diffused image data. Informatica. 2023; 47(2):221-33.

Muniraj M, Dhandapani V. Underwater image enhancement by modified color correction and adaptive look-up-table with edge-preserving filter. Signal Processing: Image Communication. 2023; 113:116939.

Bu P, Wang H, Yang T, Zhao H. Linear time manageable edge-aware filtering on complementary tree structures. Computers & Graphics. 2024; 118:133-45.

Zhang X, Zhao W, Zhang W, Peng J, Fan J. Guided filter network for semantic image segmentation. IEEE Transactions on Image Processing. 2022; 31:2695-709.

Mishiba K. Fast guided median filter. IEEE Transactions on Image Processing. 2023; 32:737-49.

Teng M, Dali Y, Lingyan H. Fabric defect detection based on improved guided filter. Wool Textile Journal. 2017; 45(11):70-3.

Zuo Y, Xie J, Wang H, Fang Y, Liu D, Wen W. Gradient-guided single image super-resolution based on joint trilateral feature filtering. IEEE Transactions on Circuits and Systems for Video Technology. 2022; 33(2):505-20.

Xinyuan MI, Zhang Y, Zhang J. Spatial fusion enhancement of thermal infrared images based on multi-resolution analysis and low-rank guided filter. National Remote Sensing Bulletin. 2021; 25(11):2255-69.

Li Z, Zheng J, Senthilnath J. Simultaneous smoothing and sharpening using IWGIF. In international conference on image processing 2022 (pp. 861-5). IEEE.

Yang Y, Xiong Y, Cao Y, Zeng L, Zhao Y, Zhan Y. Fast bilateral filter with spatial subsampling. Multimedia Systems. 2023; 29(1):435-46.

Gonzales AL, Ramos AL, Lacson JM, Go KS, Furigay RB. Optimizing image and signal processing through the application of various filtering techniques: a comparative study. In novel & intelligent digital systems conferences 2023 (pp. 151-70). Cham: Springer Nature Switzerland.

Shehin AU, Sankar D. Adaptive bilateral filtering detection using frequency residuals for digital image forensics. In 29th international conference on systems, signals and image processing 2022 (pp. 1-6). IEEE.

Khetkeeree S, Thanakitivirul P. Hybrid filtering for image sharpening and smoothing simultaneously. In 35th international technical conference on circuits/systems, computers and communications 2020 (pp. 367-71). IEEE.

Lv H, Shan P, Shi H, Zhao L. An adaptive bilateral filtering method based on improved convolution kernel used for infrared image enhancement. Signal, Image and Video Processing. 2022; 16(8):2231-7.

He K, Sun J, Tang X. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012; 35(6):1397-409.

Liu H, Wang R, Xia Y, Zhang X. Improved cost computation and adaptive shape guided filter for local stereo matching of low texture stereo images. Applied Sciences. 2020; 10(5):1-17.

Toet A. Alternating guided image filtering. Peer J Computer Science. 2016; 2: 1-18.

Yang WJ, Tsai ZS, Chung PC, Cheng YT. An adaptive cost aggregation method based on bilateral filter and canny edge detector with segmented area for stereo matching. In international workshop on advanced image technology 2019 (pp. 288-93). SPIE.

Hamzah RA, Ibrahim H, Hassan AH. Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. Journal of Visual Communication and Image Representation. 2017; 42:145-60.

Wang G, Liu Y, Xiong W, Li Y. An improved non-local means filter for color image denoising. Optik. 2018; 173:157-73.

Webber AG. The USC-SIPI image database: version 6. USC-SIPI Report. 2018; 432: 1-24.

Jelani AR, Ahmad MR, Azaman MI, Gono Y, Mohamed Z, Sukawai S, et al. Development and evaluation of a new generation oil palm motorised cutter. Journal of Oil Palm Research. 2018; 30(2):276-88.

Oyedeji A, Umar A, Kuburi L, Apeh I. Trend of harvesting of oil palm fruit; the mechanisms, and challenges. International Journal of Scientific Research and Engineering Development. 2020; 3(3):1053-63.

Sowat SN, Ismail WI, Mahadi MR, Bejo SK, Kassim MS. Trend in the development of oil palm fruit harvesting technologies in Malaysia. Jurnal Teknologi (Sciences & Engineering). 2018; 80(2):83-91.

Abangba AF, Adekunle KC, Ayirebi AA, Ofori YO. Performance evaluation, machine parameters and ergonomic aspects of palm fruit harvesters. Journal of Advanced Research in Applied Mechanics. 2024; 122(1):14-31.

Mohamaddan S, Rahman MA, Andrew_munot M, Tanjong SJ, Deros BM, Dawal SM, et al. Investigation of oil palm harvesting tools design and technique on work-related musculoskeletal disorders of the upper body. International Journal of Industrial Ergonomics. 2021; 86:103226.

Ruiz ÁE, Banguera J, Pérez TW, Hernández HJ, Arévalo J, Mosquera MM. Technical and economic assessment of two harvesting tools for young elaeis oleifera x E. guineensis oil palms. Agronomia Colombiana. 2020; 38(3):418-28.

Gathala MK, Laing AM, Tiwari TP, Timsina J, Rola-rubzen F, Islam S, et al. Improving smallholder farmers’ gross margins and labor-use efficiency across a range of cropping systems in the eastern Gangetic plains. World Development. 2021; 138:105266.

Chiriacò MV, Bellotta M, Jusić J, Perugini L. Palm oil’s contribution to the United Nations sustainable development goals: outcomes of a review of socio-economic aspects. Environmental Research Letters. 2022; 17(6):1-22.

Nair KP. Tree crops. Harvesting Cash from the World’s Important Cash Crops, 1st ed.; Springer Nature: Cham, Switzerland. 2021: 249-85.

Ramasubramanian B, Sundarrajan S, Rao RP, Reddy MV, Chellappan V, Ramakrishna S. Novel low-carbon energy solutions for powering emerging wearables, smart textiles, and medical devices. Energy & Environmental Science. 2022; 15(12):4928-81.

Karunathilake EM, Le AT, Heo S, Chung YS, Mansoor S. The path to smart farming: innovations and opportunities in precision agriculture. Agriculture. 2023; 13(8):1-26.

Xue H, Gong H, Yamauchi Y, Sasaki T, Ma R. Photo-enhanced rechargeable high-energy-density metal batteries for solar energy conversion and storage. Nano Research Energy. 2022; 1(1):1-21.

Pakeerathan K. Smart agriculture: special challenges and strategies for island states. In smart agriculture for developing nations: status, perspectives and challenges 2023 (pp. 251-8). Singapore: Springer Nature Singapore.

Sharma A, Sharma A, Tselykh A, Bozhenyuk A, Choudhury T, Alomar MA, et al. Artificial intelligence and internet of things oriented sustainable precision farming: towards modern agriculture. Open Life Sciences. 2023; 18(1):20220713.

Kahar P, Rachmadona N, Pangestu R, Palar R, Adi DT, Juanssilfero AB, et al. An integrated biorefinery strategy for the utilization of palm-oil wastes. Bioresource Technology. 2022; 344:126266.

Deb N, Alam MZ, Rahman T, Al-khatib MA, Jami MS, Mansor MF. Acid–base pretreatment and enzymatic hydrolysis of palm oil mill effluent in a single reactor system for production of fermentable sugars. International Journal of Polymer Science. 2023; 2023(1):1-15.

Binti SA, Binti JSH, Binti RSN, Binti AH. Redefining biofuels: investigating oil palm biomass as a promising cellulose feedstock for nitrocellulose-based propellant production. Defence Technology. 2024; 37:111-32.

Baur P, Iles A. Replacing humans with machines: a historical look at technology politics in California agriculture. Agriculture and Human Values. 2023; 40(1):113-40.

Szász L, Demeter K, Rácz BG, Losonci D. Industry 4.0: a review and analysis of contingency and performance effects. Journal of Manufacturing Technology Management. 2021; 32(3):667-94.

Felipe CM, Leidner DE, Roldán JL, Leal‐rodríguez AL. Impact of IS capabilities on firm performance: the roles of organizational agility and industry technology intensity. Decision Sciences. 2020; 51(3):575-619.

Lee AT, Mcgregor G, Coetzee A. Correlates of yield, fecundity and survival of a wild harvested cyclopia intermedia (honeybush) population. Agroecology and Sustainable Food Systems. 2023; 47(5):646-67.

Lodolini EM, Polverigiani S, Giorgi V, Famiani F, Neri D. Time and type of pruning affect tree growth and yield in high-density olive orchards. Scientia Horticulturae. 2023; 311:111831.

Ghaziani S, Dehbozorgi G, Bakhshoodeh M, Doluschitz R. Unraveling on-farm wheat loss in fars province, Iran: a qualitative analysis and exploration of potential solutions with emphasis on agricultural cooperatives. Sustainability. 2023; 15(16):1-24.

Afsah‐hejri L, Homayouni T, Toudeshki A, Ehsani R, Ferguson L, Castro‐garcía S. Mechanical harvesting of selected temperate and tropical fruit and nut trees. Horticultural Reviews. 2022; 49:171-242.

Li M, Nangong Z. Precision trunk injection technology for treatment of huanglongbing (HLB)-affected citrus trees-a review. Journal of Plant Diseases and Protection. 2022; 129(1):15-34.

Yezekyan T, Marinello F, Armentano G, Trestini S, Sartori L. Modelling of harvesting machines’ technical parameters and prices. Agriculture. 2020; 10(6):1-12.

Pulingam T, Lakshmanan M, Chuah JA, Surendran A, Zainab-l I, Foroozandeh P, et al. Oil palm trunk waste: environmental impacts and management strategies. Industrial Crops and Products. 2022; 189:115827.

Foong SY, Chan YH, Lock SS, Chin BL, Yiin CL, Cheah KW, et al. Microwave processing of oil palm wastes for bioenergy production and circular economy: recent advancements, challenges, and future prospects. Bioresource Technology. 2023; 369:128478.

Roles J, Yarnold J, Hussey K, Hankamer B. Techno-economic evaluation of microalgae high-density liquid fuel production at 12 international locations. Biotechnology for Biofuels. 2021; 14(1):1-19.

Gai R, Chen N, Yuan H. A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Computing and Applications. 2023; 35(19):13895-906.

Akinyi D. Cost-benefit analysis of prioritized climate-smart agricultural practices and innovations among smallholder farmers a case of selected value-chains in Sub-Saharan Africa. Doctoral Dissertation, Egerton University. 2022.

Nichenametla PKCK. A multi-criteria assessment of the impact of previous land use and current management practices on the performance of oil palm on smallholders plots in the southern Thailand. Masters Thesis, Norwegian University of Life Sciences. 2023.

Anwar A, Murugan AS, Recchia A, Kim E, Urbanic J. Investigating musculoskeletal risks in manual mushroom harvesting: an ergonomic field study in Canadian farms. Social Sciences & Humanities Open. 2024; 10:1-7.

Seo M, Kim H, Jung W. Ergonomic improvements to agricultural harvest baskets to reduce the risk of musculoskeletal disorders among farmers. International Journal of Environmental Research and Public Health. 2022; 19(17):1-12.

Chikelu PO. Model design and development of a telescopic palm fruit harvester. Modern Mechanical Engineering. 2023; 13(1):1-20.

Ahmada MR, Radzia MK, Ramlia AS, Bakria MA, Hafizi MI, Azamana AI, et al. Evaluation and comparison of the ergonomics, performance and economics of battery-powered and engine-powered palm oil harvesting tools: Cantas Elektro. Jurnal Kejuruteraan. 2023; 35(4):811-21.

Anuar MM, Jaafar SB. Evaluation on TVET assessment in oil palm operation focuses on mechanized FFB harvesting and collecting. Advances in Agricultural and Food Research Journal. 2022; 3(1):1-10.

Akhtar J, Amin NS. A review on operating parameters for optimum liquid oil yield in biomass pyrolysis. Renewable and Sustainable Energy Reviews. 2012; 16(7):5101-9.

Jung GB, Su A, Tu CH, Weng FB. Effect of operating parameters on the DMFC performance. Journal of Fuel Cell Science and Technology. 2005; 2(2):81-5.

Bertola NJ, Bayane I, Brühwiler E. Cost-benefit evaluation of a monitoring system for structural identification of existing bridges. In bridge safety, maintenance, management, life-cycle, resilience and sustainability 2022 (pp. 394-400). CRC Press.

Escallón-barrios M, Castillo-gomez D, Leal J, Montenegro C, Medaglia AL. Improving harvesting operations in an oil palm plantation. Annals of Operations Research. 2022; 314(2):411-49.

Lehmann EL, Romano JP. Testing statistical hypotheses. New York: Springer; 1986.

Jelani AR, Hitam A, Jamak J, Noor M, Gono Y, Ariffin O. Cantas TM–a tool for the efficient harvesting of oil palm fresh fruit bunches. Journal of Oil Palm Research. 2008; 20:548-58.

Khor JF, Ling L, Yusop Z, Tan WL, Ling JL, Soo EZ. Impact of El Niño on oil palm yield in Malaysia. Agronomy. 2021; 11(11):1-22.

Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science. 2021; 7:1-24.

Ismail A, Ahmad SM, Sharudin Z. Labour productivity in the Malaysian oil palm plantation sector. Oil Palm Industry Economic Journal. 2015; 15(2):1-10.

Çengel YA. Green thermodynamics. International Journal of Energy Research. 2007; 31(12):1088-104.

Bejan A. Advanced engineering thermodynamics. John Wiley & Sons; 2016.

Carlson JM, Zanobetti A, De CSE, Poblacion AP, Fabian PM, Carnes F, et al. Critical windows of susceptibility for the effects of prenatal exposure to heat and heat variability on gestational growth. Environmental Research. 2023; 216:114607.

Levin HM, Mcewan PJ. Cost-effectiveness analysis: methods and applications. Sage; 2001.

Neumann PJ, Sanders GD. Cost-effectiveness analysis 2.0. New England Journal of Medicine. 2017; 376(3):203-5.

Thaddeus DJ, Bakri MA, Khalid MR, Ahmad MR, Azaman MI, Mustaffa NK, et al. An overview of the benefits and advantages of implementing mechanisation in the oil palm plantation: a look at fresh fruit bunch (FFB) evacuation. Advances in Agricultural and Food Research Journal. 2023; 4(2):1-13.

Du F, Li D, Sa X, Li C, Yu Y, Li C, et al. Overview of friction and wear performance of sliding bearings. Coatings. 2022; 12(9):1-15.

Korotkov A, Korotkova L, Vidin D. Study of the quality of plain bearings, used in mining machinery. In E3S web of conferences 2023 (pp. 1-10). EDP Sciences.

Zhang Z, Gkartzou E, Jestin S, Semitekolos D, Pappas PN, Li X, et al. 3D printing processability of a thermally conductive compound based on carbon nanofiller-modified thermoplastic polyamide 12. Polymers. 2022; 14(3):1-16.

Lu J. Polymer materials in daily life: classification, applications, and future prospects. In E3S web of conferences 2023 (pp. 1-5). EDP Sciences.

Rajendran P, Ganesan A. Experimental investigation on friction behavior of selective laser sintering processed parts. 3D Printing and Additive Manufacturing. 2024; 11(3):e1186-95.

Mehmet I, Dogan G, Chitariu DF, Dumitraș C, Negoescu F. Research on advances in roller bearing manufacturing. In conference series: materials science and engineering 2021 (pp. 1-6). IOP Publishing.

Sanchez GD, Leventini S, Martini A. Effect of temperature and surface roughness on the tribological behavior of electric motor greases for hybrid bearing materials. Lubricants. 2021; 9(6):1-16.

Smith TM, Kantzos CA, Zarkevich NA, Harder BJ, Heczko M, Gradl PR, et al. A 3D printable alloy designed for extreme environments. Nature. 2023; 617(7961):513-8.

Li D, Yang X, Wu Y, Cheng J, Wang S, Wan Z, et al. Theoretical analysis and experimental research of surface texture hydrodynamic lubrication. Chinese Journal of Mechanical Engineering. 2022; 35(1):1-15.

Kadda M, Nadia B. Elastic behavior of the plain journal bearing coated with a textured surface and a non-textured surface: plain journal bearing at textured surface behavior. International Journal of Surface Engineering and Interdisciplinary Materials Science. 2020; 8(1):55-77.

Guo Z, Huang Q, Xie X, Yuan C. Effects of spherical-platform texture parameters on the tribological performance of water-lubricated bearings. Wear. 2021; 477:203863.

Vidyasagar KC, Pandey RK, Kalyanasundaram D. An exploration of frictional and vibrational behaviors of textured deep groove ball bearing in the vicinity of requisite minimum load. Friction. 2021; 9:1749-65.

Yang Z, Xiong T, Du F, Li B. Topology optimization of stiffener layout design for box type load-bearing component under thermo-mechanical coupling. CMES-Computer Modeling in Engineering & Sciences. 2023; 135(2):1701-18.

Shan W, Chen Y, Wang X, Yu C, Wu K, Han Z. Nonlinear dynamic characteristics of deep groove ball bearings with an improved contact model. Machines. 2023; 11(3):1-25.

Zhang P, Wei L, Feng X, Feng F. Friction characteristics of the spiral groove mechanical seal. In journal of physics: conference series 2020 (pp. 1-8). IOP Publishing.

Liang C, Cai Z, Wu H, Xiao J, Zhang Y, Ma Z. Chloride transport and induced steel corrosion in recycled aggregate concrete: a review. Construction and Building Materials. 2021; 282:122547.

Chowdhury D, Batham A, Sehgal U, Ghosh C, Bhattacharya B, Datta S. Analysing the frictional properties of micro dimpled surface created by milling machine under lubricated condition. Tribology International. 2020; 146:106260.

Norani MN, Abdullah MI, Abdollah MF, Amiruddin H, Ramli FR, Tamaldin N. Tribological analysis of a 3D-printed internal triangular flip ABS pin during running-in stage. Jurnal Tribologi. 2020; 27:42-56.

Gupta N, Tandon N, Pandey RK, Vidyasagar KC, Kalyanasundaram D. Tribological and vibration studies of textured spur gear pairs under fully flooded and starved lubrication conditions. Tribology Transactions. 2020; 63(6):1103-20.

Esangbedo MO, Abifarin JK. Determination and managerial implications of machine conditions for high-grade industrial polycaprolactam (nylon 6). Scientific Reports. 2023; 13(1):1-8.

Clavería I, Gimeno S, Miguel I, Mendoza G, Lostalé A, Fernández Á, et al. Tribological performance of nylon composites with nanoadditives for self-lubrication purposes. Polymers. 2020; 12(10):1-22.

Gadelmoula A, Aldahash SA. Effect of reinforcement with short carbon fibers on the friction and wear resistance of additively manufactured PA12. Polymers. 2023; 15(15):1-14.

Alo OA, Otunniyi IO, Mauchline D. Correlation of reuse extent with degradation degree of PA 12 powder during laser powder bed fusion and mechanical behavior of sintered parts. Polymer Engineering & Science. 2023; 63(1):126-38.

Sanders B, Cant E, Kelly CA, Jenkins M. The effect of powder re-use on the coalescence behaviour and isothermal crystallisation kinetics of polyamide 12 within powder bed fusion. Polymers. 2024; 16(5):1-22.

Lee KP, Kajtaz M. Experimental characterisation and finite element modelling of polyamide-12 fabricated via multi jet fusion. Polymers. 2022; 14(23):1-9.

Rosso S, Meneghello R, Biasetto L, Grigolato L, Concheri G, Savio G. In-depth comparison of polyamide 12 parts manufactured by multi jet fusion and selective laser sintering. Additive Manufacturing. 2020; 36:1-13.

Sibisi TH, Shongwe MB, Tshabalala LC, Mathoho I. LAM additive manufacturing: a fundamental review on mechanical properties, common defects, dominant processing variables, and its applications. The International Journal of Advanced Manufacturing Technology. 2023; 128(7):2847-61.

Enzi A, Mynderse JA. Design and experimental validation of a small-scale prototype selective laser sintering system. SN Applied Sciences. 2019; 1(12):1-10.

Yan B, Zhang X, Zhu Z. The influence of bearing ring inclination on precision ball bearing contact and heat generation performance. Lubricants. 2022; 10(9):1-14.

Saldívar MC, Tay E, Isaakidou A, Moosabeiki V, Fratila-apachitei LE, Doubrovski EL, et al. Bioinspired rational design of bi-material 3D printed soft-hard interfaces. Nature Communications. 2023; 14(1):1-11.

Ngatiman NA, Nuawi MZ, Putra A, Qamber IS, Sutikno T, Jopri MH. Spark plug failure detection using Z-freq and machine learning. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2021; 19(6):2020-9.

Burda EA, Zusman GV, Kudryavtseva IS, Naumenko AP. An overview of vibration analysis techniques for the fault diagnostics of rolling bearings in machinery. Shock and Vibration. 2022; 2022(1):1-14.

Brockett C, Carbone S, Jennings L, Fisher J. Influence of material and geometry on the wear of fixed bearing total knee replacements. In Orthopaedic Proceedings 2014 (p. 7). Bone & Joint.

Morano C, Alfano M, Pagnotta L. Effect of strain rates and heat exposure on polyamide (PA12) processed via selective laser sintering. Materials. 2023; 16(13):1-13.

Singh J, Singh R, Sharma S. Effect of processing parameters on mechanical properties of FDM filament prepared on single screw extruder. Materials Today: Proceedings. 2022; 50:886-92.

Schneider J, Kumar S. Multiscale characterization and constitutive parameters identification of polyamide (PA12) processed via selective laser sintering. Polymer Testing. 2020; 86:106357.

Popova E, Popov VL. The legacy of Coulomb and generalized laws of friction. Proceedings in Applied Mathematics and Mechanics. 2021; 20(1):1-2.

Khafidh M, Schipper DJ, Masen MA, Vleugels N, Dierkes WK, Noordermeer JW. Validity of Amontons’ law for run-in short-cut aramid fiber reinforced elastomers: the effect of epoxy coated fibers. Friction. 2020; 8:613-25.

Yu J, Ge J, Yu H, Ye L. Improved bioproduction of the nylon 12 monomer by combining the directed evolution of P450 and enhancing heme synthesis. Molecules. 2023; 28(4):1-19.

Lates MT, Gavrila CC. Frictional study of the polyamide/rubber contact materials. In conference series: materials science and engineering 2020 (pp. 1-6). IOP Publishing.

Erinle TJ, Ojaomo KE, Adeoye OS. Fused deposition modelling (FDM) and selective laser sintering (SLS) as indisputable machines in additive manufacturing: a review. Scisynopsis international conference on 3D printing & additive manufacturing. 2023 (pp.1-9).

Dejene ND, Lemu HG. Current status and challenges of powder bed fusion-based metal additive manufacturing: literature review. Metals. 2023; 13(2):1-22.

Li M, Xu Y, Fang J. Orthotropic mechanical properties of PLA materials fabricated by fused deposition modeling. Thin-Walled Structures. 2024; 199:1-15.

Zisopol DG, Tănase M, Portoacă AI. Innovative strategies for technical-economical optimization of FDM production. Polymers. 2023; 15(18):1-22.

Abdulridha HH, Abbas TF, Bedan AS. Predicting mechanical strength and optimized parameters in FDM-printed polylactic acid parts via artificial neural networks and desirability analysis. Management Systems in Production Engineering. 2024; 32(3):428-37.

Mensahn ES, Wada SA, Lugeiyamu L. Structural evaluation of conventional and modified flexible pavement performance. Arid Zone Journal of Engineering, Technology and Environment. 2021; 17(4):535-46.

Al-taher MG, Sawan AM, Solyman ME, El-sharkawi Attia MI, Ibrahim MF. Evaluating the durability of asphalt mixtures for flexible pavement using different techniques: a review. International Journal of Pavement Research and Technology. 2024:1-27.

Kou B, Cao J, Huang W, Ma T, Shi Z. Rutting prediction model of asphalt pavement based on RIOHTrack full-scale ring road. Measurement. 2025; 242:115915.

Alamnie MM. Laboratory-based study of viscoelastic, viscoplastic and fatigue damage of asphalt concrete. Doctoral Thesis, University of Agder. 2024.

Patel VJ, Juremalani J, Kumavat HR. Incorporation of high volume ground granulated slag from blast furnaces in pavement quality concrete. Engineering, Technology & Applied Science Research. 2024; 14(4):14888-93.

Zhang AA, Shang J, Li B, Hui B, Gong H, Li L, et al. Intelligent pavement condition survey: overview of current researches and practices. Journal of Road Engineering. 2024; 4(3): 257-81.

Li Q, Xiao DX, Wang KC, Hall KD, Qiu Y. Mechanistic-empirical pavement design guide (MEPDG): a bird’s-eye view. Journal of Modern Transportation. 2011: 114-33.

Gong H, Sun Y, Hu W, Huang B. Neural networks for fatigue cracking prediction using outputs from pavement mechanistic-empirical design. International Journal of Pavement Engineering. 2021; 22(2):162-72.

Kang J. Pavement performance prediction using machine learning and instrumentation in smart pavement. Master's Thesis, University of Waterloo. 2022.

Liu Y, You Z, Li L, Wang W. Review on advances in modeling and simulation of stone-based paving materials. Construction and Building Materials. 2013; 43:408-17.

Deng Y, Shi X. Short-term predictions of asphalt pavement rutting using deep-learning models. Journal of Transportation Engineering, Part B: Pavements. 2024; 150(2):04024004.

Bayat R, Talatahari S, Gandomi AH, Habibi M, Aminnejad B. Artificial neural networks for flexible pavement. Information. 2023; 14(2):1-23.

Yang X, Guan J, Ding L, You Z, Lee VC, Hasan MR, et al. Research and applications of artificial neural network in pavement engineering: a state-of-the-art review. Journal of Traffic and Transportation Engineering (English Edition). 2021; 8(6):1000-21.

Mikels N, Kassem E, Muftah A, Sufian AA. The Use of Artificial Intelligence in Pavement Engineering. Pacific Northwest Transportation Consortium (PacTrans) (UTC); 2023.

Kotb MM. Prediction of distresses in pavement networks: a machine learning approach. Master's Thesis, The American University in Cairo (Egypt). 2024.

Karadag H, Firat S, Isik NS, Yilmaz G. Determination of permanent deformation of flexible pavements using finite element model. Građevinar. 2022; 74(6):471-80.

Pandey AK, Mathur D. Finite element analysis and optimization of flexible pavement. International Journal of Civil Engineering Applications Research. 2023; 4(1):6-25.

Deng Y, Zhang Y, Shi X, Hou S, Lytton RL. Stress–strain dependent rutting prediction models for multi-layer structures of asphalt mixtures. International Journal of Pavement Engineering. 2022; 23(8):2728-45.

Liu L, Hao P. ABAQUS program-based numerical analysis on U-shaped cracking of asphalt pavement. In international conference on remote sensing, environment and transportation engineering 2011 (pp. 2861-4). IEEE.

Arabani M, Jamshidi R, Sadeghnejad M. Using of 2D finite element modeling to predict the glasphalt mixture rutting behavior. Construction and Building Materials. 2014; 68:183-91.

Al-khateeb LA, Saoud A, Al-msouti MF. Rutting prediction of flexible pavements using finite element modeling. Jordan Journal of Civil Engineering. 2011; 5(2):173-90.

Imaninasab R, Bakhshi B, Shirini B. Rutting performance of rubberized porous asphalt using finite element method (FEM). Construction and Building Materials. 2016; 106:382-91.

Alnaqbi AJ, Zeiada W, Al-khateeb G, Abttan A, Abuzwidah M. Predictive models for flexible pavement fatigue cracking based on machine learning. Transportation Engineering. 2024; 16:1-24.

Cano-ortiz S, Pascual-muñoz P, Castro-fresno D. Machine learning algorithms for monitoring pavement performance. Automation in Construction. 2022; 139:1-16.

Cheng C, Wang L, Zhou X, Wang X. Predicting rutting development using machine learning methods based on RIOCHTrack data. Applied Sciences. 2024; 14(8):1-21.

Alwan DS, Joni HH, Hilal MM. Deformation behavior of flexible pavements by finite element simulation. Periodicals of Engineering and Natural Sciences (PEN). 2022; 10(6):142-50.

Kumar P, Kiran KBV, Manjunatha S, Subramanya KG. Finite element modeling by ABAQUS for rutting in flexible pavement. Civil Engineering and Architecture. 2024; 12(3):1576-84.

Kothai R, Prabakaran N, Murthy YS, Cenkeramaddi LR, Kakani V. Pavement distress detection, classification and analysis using machine learning algorithms: a survey. IEEE Access. 2024; 12:126943-60.

Deng Y, Shi X. Modeling the rutting performance of asphalt pavements: a review. Journal of Infrastructure Preservation and Resilience. 2023; 4(1):1-21.

Alnaqbi AJ, Zeiada W, Al-khateeb GG, Hamad K, Barakat S. Creating rutting prediction models through machine learning techniques utilizing the long-term pavement performance database. Sustainability. 2023; 15(18):1-31.

Hu X, Ishaq A, Khattak A, Chen F. Assessment of factors affecting pavement rutting in Pakistan using finite element method and machine learning models. Sustainability. 2024; 16(6):1-24.

Li L, Huang X, Han D, Dong M, Zhu D. Investigation of rutting behavior of asphalt pavement in long and steep section of mountainous highway with overloading. Construction and Building Materials. 2015; 93:635-43.

Patel A, Singh V, Shanker R. FEM based parametric analysis for investigating effect of wheel base characteristics and axle configurations on flexural stresses in rigid pavements. Materials Today: Proceedings. 2022; 65:1280-9.

Akram HA, Hilal MM, Fattah MY. Numerical simulation of the effect of repeated load and waste polypropylene on the behavior of asphalt layers. In conference series: earth and environmental science 2022 (pp. 1-13). IOP Publishing.

Li H, Guo R, Wang W. A multiphase hybrid-stress finite element method for macroscopic and microscopic modeling of composites: an element with multiple interfaces. Applied Mathematical Modelling. 2023; 116:147-67.

Congress IR. IRC: 37: 2018-guidelines for the design of flexible pavements. IRC, New Delhi, India. 2018.

Kumar A, Sinha S. Support vector machine-based prediction of unconfined compressive strength of multi-walled carbon nanotube doped soil-fly ash mixes. Multiscale and Multidisciplinary Modeling, Experiments and Design. 2024; 7(6):5365-86.

Kashyap R, Chauhan VB, Kumar A, Jaiswal S. Machine learning-based stability assessment of unlined circular tunnels under surcharge loading. Asian Journal of Civil Engineering. 2024; 25(3):2553-66.

Kumar A, Sinha S, Saurav S. Random forest, CART, and MLR-based predictive model for unconfined compressive strength of cement reinforced clayey soil: a comparative analysis. Asian Journal of Civil Engineering. 2024; 25(2):2307-23.

Kumar A, Sinha S, Pandey D, Maurya MC, Chauhan VB. Advanced regression models for assessing the strength of multi-walled carbon nanotube-modified high-volume fly ash concrete. Asian Journal of Civil Engineering. 2024; 25(2):2247-68.

Kumar A, Sinha S, Saurav S, Chauhan VB. Prediction of unconfined compressive strength of cement–fly ash stabilized soil using support vector machines. Asian Journal of Civil Engineering. 2024; 25(2):1149-61.

Jin R, Chen Q. An investigation of current status of green concrete in the construction industry. In 49th ASC annual international conference proceedings 2013 (pp. 1-8).

Rashid S, Singh M. An investigation on carbon dioxide incorporated sustainable ready-mix concrete using OPC and PPC. Arabian Journal for Science and Engineering. 2023; 48(10):14213-36.

Gao T, Shen L, Shen M, Liu L, Chen F, Gao L. Evolution and projection of CO2 emissions for Chinas cement industry from 1980 to 2020. Renewable and Sustainable Energy Reviews. 2017; 74:522-37.

Sukmak P, Sukmak G, Horpibulsuk S, Setkit M, Kassawat S, Arulrajah A. Palm oil fuel ash-soft soil geopolymer for subgrade applications: strength and microstructural evaluation. Road Materials and Pavement Design. 2019; 20(1):110-31.

Ding GK. Sustainable construction-the role of environmental assessment tools. Journal of Environmental Management. 2008; 86(3):451-64.

Fernando A, Selvaranjan K, Srikanth G, Gamage JC. Development of high strength recycled aggregate concrete-composite effects of fly ash, silica fume and rice husk ash as pozzolans. Materials and Structures. 2022; 55(7):1-22.

Bahoria BV, Parbat DK, Nagarnaik PB. XRD analysis of natural sand, quarry dust, waste plastic (ldpe) to be used as a fine aggregate in concrete. Materials Today: Proceedings. 2018; 5(1):1432-8.

https://environmentclearance.nic.in/writereaddata/SandMiningManagementGuidelines2016.pdf. Accessed 22 December 2024.

Dey S, Kumar VP, Goud KR, Basha SK. State of art review on self compacting concrete using mineral admixtures. Journal of Building Pathology and Rehabilitation. 202; 6(1):18.

Diop B, Mélinge Y, Molez L, Jauberthie R, Bouguerra A. Durability of mortars with natural fillers in aggressive environnement. In structural faults and repair 2008 (pp.1-8). HAL Open Science.

Shokravi H, Mohammadyan-yasouj SE, Koloor SS, Petrů M, Heidarrezaei M. Effect of alumina additives on mechanical and fresh properties of self-compacting concrete: a review. Processes. 2021; 9(3):1-22.

Singh A, Mehta PK, Kumar R. Recycled coarse aggregate and silica fume used in sustainable self-compacting concrete. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(96):1581-96.

Busari AA, Akinmusuru JO, Dahunsi BI. Review of sustainability in self-compacting concrete: the use of waste and mineral additives as supplementary cementitious materials and aggregates. Portugaliae Electrochimica Acta. 2018; 36(3):147-62.

Adebakin IH, Gunasekaran K, Annadurai R. Mechanical properties of self-compacting coconut shell concrete blended with fly ash. Asian Journal of Civil Engineering. 2019; 20:113-24.

Bouzoubaâ N, Lachemi M. Self-compacting concrete incorporating high volumes of class F fly ash: preliminary results. Cement and Concrete Research. 2001; 31(3):413-20.

Yao ZT, Ji XS, Sarker PK, Tang JH, Ge LQ, Xia MS, et al. A comprehensive review on the applications of coal fly ash. Earth-Science Reviews. 2015; 141:105-21.

Hossain SS, Mathur L, Roy PK. Rice husk/rice husk ash as an alternative source of silica in ceramics: a review. Journal of Asian Ceramic Societies. 2018; 6(4):299-313.

Kosior‐kazberuk M, Lelusz M. Strength development of concrete with fly ash addition. Journal of Civil Engineering and Management. 2007; 13(2):115-22.

Tripathi D, Kumar R, Mehta PK. Development of an environmental-friendly durable self-compacting concrete. Environmental Science and Pollution Research. 2022; 29(36):54167-80.

De MPR, Foiato M, Prudêncio JLR. Ecological, fresh state and long-term mechanical properties of high-volume fly ash high-performance self-compacting concrete. Construction and Building Materials. 2019; 203:282-93.

Patil S, Ramesh B, Sathish T, Saravanan A, Almujibah H, Panchal H, et al. Evaluation and optimization of mechanical properties of laterized concrete containing fly ash and steel fiber using Taguchi robust design method. Alexandria Engineering Journal. 2024; 87:682-706.

Rao MD, Dey S, Rao BP. Characterization of fiber reinforced self-compacting concrete by fly ash and cement. Chemistry of Inorganic Materials. 2023; 1:1-14.

Mohammed AM, Asaad DS, Al-hadithi AI. Experimental and statistical evaluation of rheological properties of self-compacting concrete containing fly ash and ground granulated blast furnace slag. Journal of King Saud University-Engineering Sciences. 2022; 34(6):388-97.

Mohamed OA, Najm O, Ahmed E. Alkali-activated slag & fly ash as sustainable alternatives to OPC: sorptivity and strength development characteristics of mortar. Cleaner Materials. 2023; 8:1-21.

Abellan-garcia J, Martinez DM, Khan MI, Abbas YM, Pellicer-martínez F. Environmentally friendly use of rice husk ash and recycled glass waste to produce ultra-high-performance concrete. Journal of Materials Research and Technology. 2023; 25:1869-81.

Ahmadi MA, Alidoust O, Sadrinejad I, Nayeri M. Development of mechanical properties of self compacting concrete contain rice husk ash. International Journal of Computer, Information, and Systems Science, and Engineering. 2007; 1(4):168-71.

Anjos MA, Camões A, Campos P, Azeredo GA, Ferreira RL. Effect of high volume fly ash and metakaolin with and without hydrated lime on the properties of self-compacting concrete. Journal of Building Engineering. 2020; 27:100985.

Tayeh BA, Hakamy AA, Fattouh MS, Mostafa SA. The effect of using nano agriculture wastes on microstructure and electrochemical performance of ultra-high-performance fiber reinforced self-compacting concrete under normal and acceleration conditions. Case Studies in Construction Materials. 2023; 18:1-17.

Mosaberpanah MA, Umar SA. Utilizing rice husk ash as supplement to cementitious materials on performance of ultra high performance concrete–a review. Materials Today Sustainability. 2020; 7:100030.

Venkatanarayanan HK, Rangaraju PR. Material characterization studies on low-and high-carbon rice husk ash and their performance in Portland cement mixtures. Advances in Civil Engineering Materials. 2013; 2(1):266-87.

Jongpradist P, Homtragoon W, Sukkarak R, Kongkitkul W, Jamsawang P. Efficiency of rice husk ash as cementitious material in high‐strength cement‐admixed clay. Advances in Civil Engineering. 2018; 2018(1):1-11.

Rahman ME, Nagaratnam BH, Pakrashi V, Muntohar AS, Sujan D, Chai N, et al. A preliminary study on self compacting concrete using RHA. In proceedings of the 3rd CUTSE international conference 2011 (pp. 495-500). Curtin University.

Nayak DK, Abhilash PP, Singh R, Kumar R, Kumar V. Fly ash for sustainable construction: a review of fly ash concrete and its beneficial use case studies. Cleaner Materials. 2022; 6:1-35.

Lakhani R, Kumar R, Tomar P. Utilization of stone waste in the development of value added products: a state of the art review. Journal of Engineering Science & Technology Review. 2014; 7(3):180-7.

Hamid NJ, Kadir AA, Kamil NA, Hassan MI. Overview on the utilization of quarry dust as a replacement material in construction industry. International Journal of Integrated Engineering. 2018; 10(2):112-7.

Singh SK, Srivastava V, Agarwal VC, Kumar R, Mehta PK. An experimental investigation on stone dust as partial replacement of fine aggregate in concrete. Journal of Academia and Industrial Research. 2014; 3(5):229-32.

Eren Ö, Marar K. Effects of limestone crusher dust and steel fibers on concrete. Construction and Building Materials. 2009; 23(2):981-8.

Bh AR, JE M. Impact of quarry dust and fly ash on the fresh and hardened properties of self compacting concrete. International Research Journal of Engineering and Technology. 2015; 2(8):786-95.

Uygunoğlu T, Topçu İB, Çelik AG. Use of waste marble and recycled aggregates in self-compacting concrete for environmental sustainability. Journal of Cleaner Production. 2014; 84:691-700.

Pathak N, Siddique R. Properties of self-compacting-concrete containing fly ash subjected to elevated temperatures. Construction and Building Materials. 2012; 30:274-80.

Wang HY, Huang WL. Durability of self-consolidating concrete using waste LCD glass. Construction and Building Materials. 2010; 24(6):1008-13.

Singh A, Kumar R, Mehta PK, Tripathi D. Mechanical performance of self-compacting concrete with pozzolanic material. In recent advances in structural engineering: select proceedings of NCRASE 2020 (pp. 11-20). Springer Singapore.

Singh A, Kumar R, Mehta PK, Tripathi D. Properties of binary admixture mixed SCC exposed to sulphate environment. In sustainable building materials and construction: select proceedings of ICSBMC 2022 (pp. 129-36). Singapore: Springer Nature Singapore.

Jayasinghe PA, Derrible S, Kattan L. Interdependencies between urban transport, water, and solid waste infrastructure systems. Infrastructures. 2023; 8(4):1-16.

Orkpeh AK, Adedire FM. African urban peripheries and informal development: a review of challenges and sustainable approaches to inclusive cities. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography. 2024; 78(1):40-53.

Tariq A, Mushtaq A. Untreated wastewater reasons and causes: a review of most affected areas and cities. International Journal of Chemical and Biochemical Sciences. 2023; 23(1):121-43.

Bancalari A. The unintended consequences of infrastructure development. Review of Economics and Statistics. 2024:1-44.

Celik T, Budayan C. How the residents are affected from construction operations conducted in residential areas. Procedia Engineering. 2016; 161:394-8.

Matthews JC, Allouche EN, Sterling RL. Social cost impact assessment of pipeline infrastructure projects. Environmental Impact Assessment Review. 2015; 50:196-202.

Çelik T, Kamali S, Arayici Y. Social cost in construction projects. Environmental Impact Assessment Review. 2017; 64:77-86.

Apeldoorn S. Comparing the costs–trenchless versus traditional methods. International Society for Trenchless Technology Conference, Sidney 2010 (pp.1-8). Australasian Society for Trenchless Technology.

Abuhmra D. Residential infrastructure optimization: foul sewer network construction approaches and relative efficiencies. Thesis of Science in Engineering Management, Qatur University. 2024:11-53.

Chan HW. Impacts of construction materials and site activities to the neighbourhood environment. Doctoral Dissertation, Faculty of Engineering and Science, Universiti Tunku Abdul Rahman. 2023.

Chadalawada R. Innovative trenchless technologies for installing underground fiber optic cables are improving efficiency while minimizing environmental impact. European Journal of Advances in Engineering and Technology. 2024; 11(10):85-98.

Onu MA, Ayeleru OO, Oboirien B, Olubambi PA. Challenges of wastewater generation and management in sub-Saharan Africa: a review. Environmental Challenges. 2023; 11:1-21.

Mishra S, Kumar R, Kumar M. Use of treated sewage or wastewater as an irrigation water for agricultural purposes-environmental, health, and economic impacts. Total Environment Research Themes. 2023; 6:1-11.

Li X, Zhu Y, Zhang Z. An LCA-based environmental impact assessment model for construction processes. Building and Environment. 2010; 45(3):766-75.

Teo MM, Loosemore M. Community‐based protest against construction projects: a case study of movement continuity. Construction Management and Economics. 2011; 29(2):131-44.

Najafi M. Trenchless technology piping: installation and inspection. McGraw Hill Professional; 2010.

Danku JC, Adjei-kumi T, Baiden BK, Agyekum K. An exploratory study into social cost considerations in Ghanaian construction industry. Journal of Building Construction and Planning Research. 2020; 8(1):14-29.

Gilchrist A, Allouche EN. Quantification of social costs associated with construction projects: state-of-the-art review. Tunnelling and Underground Space Technology. 2005; 20(1):89-104.

Xueqing W, Bingsheng L, Allouche EN, Xiaoyan L. Practical bid evaluation method considering social costs in urban infrastructure projects. In 4th international conference on management of innovation and technology 2008 (pp. 617-22). IEEE.

Liu B, Huo T, Wang X, Shen Q, Chen Y. The decision model of the intuitionistic fuzzy group bid evaluation for urban infrastructure projects considering social costs. Canadian Journal of Civil Engineering. 2013; 40(3):263-73.

Yuan QM, Cui DJ, Jiang W. Study on evaluation methods of the social cost of green building projects. Advances in Industrial Engineering, Information and Water Resources. WIT Press, Southampton. 2013.

Wang YM, Yang JB, Xu DL. Environmental impact assessment using the evidential reasoning approach. European Journal of Operational Research. 2006; 174(3):1885-913.

Yu WD, Lo SS. Time‐dependent construction social costs model. Construction Management and Economics. 2005; 23(3):327-37.

Ferguson A. Qualitative evaluation of transportation construction related social costs and their impacts on the local community. Thesis for the Degree of Master of Science in Civil Engineering, University of Texas at Arlington, TX, 2012.

Celik T. Developing a building construction associated social cost estimation system for Turkish construction industry. PhD Thesis, University of Salford, United Kingdom. 2014.

Werey C, Larabi Z, Rozan A. Addressing socio-economic and environmental impacts in sewer networks' rehabilitation decision making tools. In DIME workshop: environmental innovation in infrastructure sectors 2009 (pp. 1-13). HAL Open Science.

Çelik T, Arayici Y, Budayan C. Assessing the social cost of housing projects on the built environment: analysis and monetization of the adverse impacts incurred on the neighbouring communities. Environmental Impact Assessment Review. 2019; 77:1-10.

Budayan C, Çelik T. Determination of important building construction adverse impacts creating nuisances in residential areas on neighbouring community. Teknik Dergi. 2021; 32(2):10611-28.

Nunes VC. Development of a decision support model for the social costs of pipelines renovation projects. Master's Thesis, Civil Engineering and Management, University of Twente. 2017.

Blair J, Czaja RF, Blair EA. Designing surveys: a guide to decisions and procedures. Sage Publications; 2013.

Alkharusi H. A descriptive analysis and interpretation of data from likert scales in educational and psychological research. Indian Journal of Psychology and Education. 2022; 12(2):13-6.

Wolf EJ, Harrington KM, Clark SL, Miller MW. Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety. Educational and Psychological Measurement. 2013; 73(6):913-34.

Kline RB. Principles and practice of structural equation modeling. Guilford Publications; 2023.

Al-balawi AAA. Integration of exploratory and confirmatory working analysis as ways to verify the working structure of the World Health Organization's abbreviated quality-of-life measure. Scientific Journal of the Faculty of Education - Assiut University. 2022; 38(7.2):1-30.https://mfes.journals.ekb.eg/article_268300.html?lang=en

https://cosit.gov.iq/ar/1204-2018-7. Accessed 10-December-2023.

https://uokerbala.edu.iq/wp-content/uploads/2021/11/Rp_The-impact-of-residential-fission-in-the-efficiency-of-infrastructure-services-for-the-holy-city-of-Karbala-A-number-of-residential-neighborhood.pdf. Accessed 10-December-2024.

Vyas T, Varia HR. Predicting traffic induced noise using artificial neural network and multiple linear regression approach. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(92):1009-27.

Joreskog K, Sorbom D. Structural equation modelling: guidelines for determining model fit. NY: University Press of America; 1993.

Majeed SA, Saleh LA, Aswed GK. Modeling the water quality index and climate variables using an artificial neural network and non-linear regression. International Journal of Engineering & Technology. 2018; 7:1346-50.

Zhaurova M, Soukka R, Horttanainen M. Multi-criteria evaluation of CO2 utilization options for cement plants using the example of Finland. International Journal of Greenhouse Gas Control. 2021; 112:1-11.

Shakor P, Hasan S, Awuzie BO, Singh AK, Rauniyar A, Karakouzian M. Evaluating the potential of geopolymer concrete as a sustainable alternative for thin white-topping pavement. Frontiers in Materials. 2023; 10:1-14.

Junaid MT, Kayali O, Khennane A, Black J. A mix design procedure for low calcium alkali activated fly ash-based concretes. Construction and Building Materials. 2015; 79:301-10.

Li N, Shi C, Zhang Z, Wang H, Liu Y. A review on mixture design methods for geopolymer concrete. Composites Part B: Engineering. 2019; 178:107490.

Pavithra P, Reddy MS, Dinakar P, Rao BH, Satpathy BK, Mohanty AN. A mix design procedure for geopolymer concrete with fly ash. Journal of Cleaner Production. 2016; 133:117-25.

Serag FA, Sofi WH, Taha AZ, El-yamani MA, Tawfik TA. Mix design proposed for geopolymer concrete mixtures based on ground granulated blast furnace slag. Australian Journal of Civil Engineering. 2020; 18(2):205-18.

Anuradha R, Sreevidya V, Venkatasubramani R, Rangan BV. Modified guidelines for geopolymer concrete mix design using Indian standard. Asian Journal of Civil Engineering. 2011:353-64.

Fernández-jiménez A, Palomo Á, Sobrados I, Sanz J. The role played by the reactive alumina content in the alkaline activation of fly ashes. Microporous and Mesoporous Materials. 2006; 91(1-3):111-9.

Fernández-jiménez A, Palomo A. Mid-infrared spectroscopic studies of alkali-activated fly ash structure. Microporous and Mesoporous Materials. 2005; 86(1-3):207-14.

Kovalchuk G, Fernández-jiménez A, Palomo A. Alkali-activated fly ash: effect of thermal curing conditions on mechanical and microstructural development-part II. Fuel. 2007; 86(3):315-22.

Deb PS, Nath P, Sarker PK. The effects of ground granulated blast-furnace slag blending with fly ash and activator content on the workability and strength properties of geopolymer concrete cured at ambient temperature. Materials & Design (1980-2015). 2014; 62:32-9.

Rautaray SK, Bera DK, Rath AK. The effects of ground granulated blast-furnace slag blending with fly ash based self compacting geo-polymer concrete on the workability and strength properties at ambient curing. In recent developments in sustainable infrastructure 2022 (pp. 567-79). Singapore: Springer Nature Singapore.

Kannangara T, Guerrieri M, Fragomeni S, Joseph P. Effects of initial surface evaporation on the performance of fly ash-based geopolymer paste at elevated temperatures. Applied Sciences. 2021; 12(1):1-18.

Humad A. Shrinkage and related properties of alkali-activated binders based on high MgO blast furnace slag. Doctoral Dissertation, Luleå University of Technology. 2019.

Wu R, Gu Q, Gao X, Huang J, Guo Y, Zhang H. Effect of curing conditions on the alkali-activated blends: microstructure, performance and economic assessment. Journal of Cleaner Production. 2024; 445:141344.

Lee S, Van RA, Chon CM. Benefits of sealed-curing on compressive strength of fly ash-based geopolymers. Materials. 2016; 9(7):1-11.

Davidovits J. Chemistry of geopolymeric systems, terminology. In geopolymer 1999 (pp. 9-39).

ASTM International. ASTM C618-22, Standard specification for coal fly ash and raw or calcined natural pozzolan for use in concrete. 2022.

Collins F, Sanjayan JG. Microcracking and strength development of alkali activated slag concrete. Cement and Concrete Composites. 2001; 23(4-5):345-52.

Nasir M, Mahmood AH, Bahraq AA. History, recent progress, and future challenges of alkali-activated binders–an overview. Construction and Building Materials. 2024; 426:1-20.

El-hassan H, Ismail N, Al HS, Alshehhi A, Al AN. Effect of GGBS and curing temperature on microstructure characteristics of lightweight geopolymer concrete. In MATEC web of conferences 2017 (pp. 1-10). EDP Sciences.

Wang H, Wu H, Xing Z, Wang R, Dai S. The effect of various Si/Al, Na/Al molar ratios and free water on micromorphology and macro-strength of metakaolin-based geopolymer. Materials. 2021; 14(14):1-16.

Vijai K, Kumutha R, Vishnuram BG. Effect of types of curing on strength of geopolymer concrete. International Journal of the Physical Sciences. 2010; 5(9):1419-23.

Palomo A, Grutzeck MW, Blanco MT. Alkali-activated fly ashes: a cement for the future. Cement and Concrete Research. 1999; 29(8):1323-9.

Perera DS, Uchida O, Vance ER, Finnie KS. Influence of curing schedule on the integrity of geopolymers. Journal of Materials Science. 2007; 42:3099-106.

Rafeet A, Vinai R, Soutsos M, Sha W. Effects of slag substitution on physical and mechanical properties of fly ash-based alkali activated binders (AABs). Cement and Concrete Research. 2019; 122:118-35.

Abhilash P, Sashidhar C, Reddy IR. Strength properties of fly ash and GGBS based geo-polymer concrete. International Journal of Chemical Tech Research. 2016; 9(3):350-6.

Tamilarasan A, Suganya OM. Effect of varying molarity and curing conditions on the mechanical and microstructural characteristics of alkali activated GGBS binder. Materials Research Express. 2023; 10(9):1-18.

Van JJG, Van DJS, Lukey GC. The effect of composition and temperature on the properties of fly ash-and kaolinite-based geopolymers. Chemical Engineering Journal. 2002; 89(1-3):63-73.

Steins P, Poulesquen A, Diat O, Frizon F. Structural evolution during geopolymerization from an early age to consolidated material. Langmuir. 2012; 28(22):8502-10.

Lizcano M, Gonzalez A, Basu S, Lozano K, Radovic M. Effects of water content and chemical composition on structural properties of alkaline activated metakaolin‐based geopolymers. Journal of the American Ceramic Society. 2012; 95(7):2169-77.

Mohamed OA. Effects of the curing regime, acid exposure, alkaline activator dosage, and precursor content on the strength development of mortar with alkali-activated slag and fly ash binder: a critical review. Polymers. 2023; 15(5):1-26.

Ghosh K, Ghosh P. Alkali-activated fly ash blast furnace slag composites. Routledge Taylor and Francis Group, CRC Press; 2021.

Helmy AI. Intermittent curing of fly ash geopolymer mortar. Construction and Building Materials. 2016; 110:54-64.

ASTM C 171 standard specification for sheet materials for curing concrete sheet materials for curing concrete. 2020.https://store.astm.org/c0171-20.html.

IS 519-1959: Method of tests for strength of concrete. Bureau of Indian Standards. https://law.resource.org/pub/in/bis/S03/is.516.1959.pdf

IS: 516-1959. Methods of tests for strength of concrete. Bureau of Indian Standards. 1959:1-30.

ASTM C1585-04. Test method for measurement of rate of absorption of water by hydraulic-cement concretes. ASTM. 2004.

Zuhua Z, Xiao Y, Huajun Z, Yue C. Role of water in the synthesis of calcined kaolin-based geopolymer. Applied Clay Science. 2009; 43(2):218-23.

Zhao J, Wang A, Lyu B, Liu K, Chu Y, Ma R, et al. Proportioning factors of alkali-activated materials and interaction relationship revealed by response surface modeling. Materials. 2023; 16(5):1-21.

Duxson P, Provis JL, Lukey GC, Mallicoat SW, Kriven WM, Van DJS. Understanding the relationship between geopolymer composition, microstructure and mechanical properties. Colloids and Surfaces A: Physicochemical and Engineering Aspects. 2005; 269(1-3):47-58.

Duxson P, Fernández-jiménez A, Provis JL, Lukey GC, Palomo A, Van DJS. Geopolymer technology: the current state of the art. Journal of Materials Science. 2007; 42:2917-33.

Ma J, Dehn F. Shrinkage and creep behavior of an alkali‐activated slag concrete. Structural Concrete. 2017; 18(5):801-10.

Nguyen TN, Phung QT, Jacques D, Elsen J, Pontikes Y. Microstructure and transport properties of metakaolin-based geopolymers subjected to accelerated leaching. Construction and Building Materials. 2024; 426:136225.

Collier NC, Sharp JH, Milestone NB, Hill J, Godfrey IH. The influence of water removal techniques on the composition and microstructure of hardened cement pastes. Cement and Concrete Research. 2008; 38(6):737-44.

Criado M, Fernández-jiménez A, Palomo A. Alkali activation of fly ash. part III: effect of curing conditions on reaction and its graphical description. Fuel. 2010; 89(11):3185-92.

Iqbal J, Rashid Z. Effect of DGs on power quality of distribution system: an analytical review. Electrical, Control and Communication Engineering. 2023; 19(1):10-6.

Mahato JP, Poudel YK, Mandal RK, Chapagain MR. Power loss minimization and voltage profile improvement of radial distribution network through the installation of capacitor and distributed generation (DG). Archives of Advanced Engineering Science. 2024:1-9.

Riaz MU, Malik SA, Daraz A, Alrajhi H, Alahmadi AN, Afzal AR. Advanced energy management in a sustainable integrated hybrid power network using a computational intelligence control strategy. Energies. 2024; 17(20):1-53.

Prasad KR, Kollu R, Ramkumar A, Ramesh A. A multi-objective strategy for optimal DG and capacitors placement to improve technical, economic, and environmental benefits. International Journal of Electrical Power & Energy Systems. 2025; 165:1-15.

Hassan Q, Hsu CY, Mounich K, Algburi S, Jaszczur M, Telba AA, et al. Enhancing smart grid integrated renewable distributed generation capacities: implications for sustainable energy transformation. Sustainable Energy Technologies and Assessments. 2024; 66:103793.

Rafi V, Dhal PK, Rajesh M, Srinivasan DR, Chandrashekhar M, Reddy NM. Optimal placement of time-varying distributed generators by using crow search and black widow-hybrid optimization. Measurement: Sensors. 2023; 30:1-10.

Moses IA, Kiprono LL, Talai SM. Optimal placement and sizing of distributed generation (DG) units in electrical power distribution networks. International Journal of Electrical and Electronics Engineering Studies. 2023; 9:66-124.

Iftikhar MZ, Imran K, Akbar MI, Ghafoor S. Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique. Renewable Energy Focus. 2024; 49:100550.

Khorashadizade M, Abbasi E, Fazeli SA. Improved salp swarm optimization algorithm based on a robust search strategy and a novel local search algorithm for feature selection problems. Chemometrics and Intelligent Laboratory Systems. 2025; 258:1-16.

Cortez HL, Broma JC, Magwili GV. Optimal placement and sizing of hybrid solar-wind distributed generation in distribution network using particle swarm optimization algorithm. In international conference on electrical, computer and energy technologies 2022 (pp. 1-6). IEEE.

Gümüş TE, Emiroglu S, Yalcin MA. Optimal DG allocation and sizing in distribution systems with thevenin based impedance stability index. International Journal of Electrical Power & Energy Systems. 2023; 144:108555.

Djidimbélé R, Ngoussandou BP, Kidmo DK, Bajaj M, Raidandi D. Optimal sizing of hybrid systems for power loss reduction and voltage improvement using PSO algorithm: case study of guissia rural grid. Energy Reports. 2022; 8:86-95.

Reddy GH, Koundinya AN, Gope S, Singh KM. Optimal sizing and allocation of DG and FACTS device in the distribution system using fractional lévy flight bat algorithm. IFAC-PapersOnLine. 2022; 55(1):168-73.

Raj AF, Saravanan AG. An optimization approach for optimal location & size of DSTATCOM and DG. Applied Energy. 2023; 336:120797.

Subbaramaiah K, Sujatha P. Optimal DG unit placement in distribution networks by multi-objective whale optimization algorithm & its techno-economic analysis. Electric Power Systems Research. 2023; 214:108869.

Ali MH, Kamel S, Hassan MH, Tostado-véliz M, Zawbaa HM. An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks. Energy Reports. 2022; 8:582-604.

Lakum A, Mahajan V. A novel approach for optimal placement and sizing of active power filters in radial distribution system with nonlinear distributed generation using adaptive grey wolf optimizer. Engineering Science and Technology, an International Journal. 2021; 24(4):911-24.

Salgotra R, Singh U, Singh S, Singh G, Mittal N. Self-adaptive salp swarm algorithm for engineering optimization problems. Applied Mathematical Modelling. 2021; 89:188-207.

Kansal V, Dhillon JS. Emended salp swarm algorithm for multiobjective electric power dispatch problem. Applied Soft Computing. 2020; 90:106172.

Yang B, Wu S, Huang J, Guo Z, Wang J, Zhang Z, et al. Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions. Energy Conversion and Management. 2023; 292:117410.

El-fergany AA, Hasanien HM. Salp swarm optimizer to solve optimal power flow comprising voltage stability analysis. Neural Computing and Applications. 2020; 32:5267-83.

Yehia M, Allam D, Zobaa AF. A novel hybrid fuzzy-metaheuristic strategy for estimation of optimal size and location of the distributed generators. Energy Reports. 2022; 8:12408-25.

Faris H, Mirjalili S, Aljarah I, Mafarja M, Heidari AA. Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Nature-inspired Optimizers: Theories, Literature Reviews and Applications. 2020:185-99.

Chaudhary V, Dubey HM, Pandit M, Bansal JC. Multi-area economic dispatch with stochastic wind power using salp swarm algorithm. Array. 2020; 8:1-13.

Slowik A, Kwasnicka H. Evolutionary algorithms and their applications to engineering problems. Neural Computing and Applications. 2020; 32:12363-79.

Mitra U, Arya A, Gupta S. A wind diesel hybrid system simulation for power generation using PMSG. In international conference on intelligent controller and computing for smart power 2022 (pp. 1-6). IEEE.