Ahmad Mwfaq Bataineh^1,2,*, Ahmad Sufril Azlan Mohamed¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 93-124, 2025, DOI:10.32604/cmc.2025.064250 - 09 June 2025

Abstract Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assessment (REBA) pose estimation by leveraging progress in three-dimensional human modeling. This survey provides an extensive overview of recent advancements, particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks. By reviewing literature from 2016 to 2024, this study offers a current and comprehensive analysis of techniques, existing challenges, and emerging trends in three-dimensional human pose estimation. In contrast to traditional reviews organized by learning paradigms, this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder (MSD)… More >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3553-3569, 2023, DOI:10.32604/cmc.2023.037028 - 31 March 2023

Abstract Recently, deep learning has achieved remarkable results in fields that require human cognitive ability, learning ability, and reasoning ability. Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity. Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process. However, it takes a lot of time and effort for researchers to use the existing activation function in their research. Therefore, in this paper, we propose a universal activation function (UA) so… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Mostafa Abotaleb⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042 - 31 October 2022

Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2ⁿ possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Fadwa Alrowais^1,*, Sunil Kumar³, Abdelhameed Ibrahim⁴, Abdelaziz A. Abdelhamid^5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039 - 31 October 2022

Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2ⁿ potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead… More >