Ginanjar Suwasono Adi¹, Samsul Huda^2,*, Griffani Megiyanto Rahmatullah³, Dodit Suprianto¹, Dinda Qurrota Aini Al-Sefy³, Ivon Sandya Sari Putri⁴, Lalu Tri Wijaya Nata Kusuma⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068619 - 10 November 2025

Abstract In the competitive retail industry of the digital era, data-driven insights into gender-specific customer behavior are essential. They support the optimization of store performance, layout design, product placement, and targeted marketing. However, existing computer vision solutions often rely on facial recognition to gather such insights, raising significant privacy and ethical concerns. To address these issues, this paper presents a privacy-preserving customer analytics system through two key strategies. First, we deploy a deep learning framework using YOLOv9s, trained on the RCA-TVGender dataset. Cameras are positioned perpendicular to observation areas to reduce facial visibility while maintaining accurate More >

Thomas Hanne^1,*, Mohammad Jahani Moghaddam²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4203-4236, 2025, DOI:10.32604/cmc.2025.068087 - 23 October 2025

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their More >

J. S. V. Siva Kumar¹, Mahmad Mustafa², Sk. M. Unnisha Begum³, Badugu Suresh⁴, Rajanand Patnaik Narasipuram^5,*

Energy Engineering, Vol.122, No.10, pp. 3891-3904, 2025, DOI:10.32604/ee.2025.070052 - 30 September 2025

Abstract Electric vehicle (EV) monitoring systems commonly depend on IoT-based sensor measurements to track key performance parameters such as vehicle speed, state of charge (SoC), battery temperature, power consumption, motor RPM, and regenerative braking. While these systems enable real-time data acquisition, they are often hindered by sensor noise, communication delays, and measurement uncertainties, which compromise their reliability for critical decision-making. To overcome these limitations, this study introduces a comparative framework that integrates reference signals, a digital twin model emulating ideal system behavior, and real-time IoT measurements. The digital twin provides a predictive and noise-resilient representation of More >

Abdulaziz Alshahrani^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4067-4090, 2025, DOI:10.32604/cmc.2025.065211 - 23 September 2025

Abstract High dropout rates in short-term job skills training programs hinder workforce development. This study applies machine learning to predict program completion while addressing class imbalance challenges. A dataset of 6548 records with 24 demographic, educational, program-specific, and employment-related features was analyzed. Data preprocessing involved cleaning, encoding categorical variables, and balancing the dataset using the Synthetic Minority Oversampling Technique (SMOTE), as only 15.9% of participants were dropouts. six machine learning models—Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and XGBoost—were evaluated on both balanced and unbalanced datasets using an 80-20 train-test split. Performance More >

Josh McNett¹, Jackie McNett^2,*

Journal of Cyber Security, Vol.7, pp. 279-306, 2025, DOI:10.32604/jcs.2025.067514 - 25 July 2025

Abstract This review explores the implementation and effectiveness of continuous evaluation programs in managing and mitigating insider threats within organizations. Continuous evaluation programs involve the ongoing assessment of individuals’ suitability for access to sensitive information and resources by monitoring their behavior, access patterns, and other indicators in real-time. The review was conducted using a comprehensive search across various academic and professional databases, including IEEE Xplore, SpringerLink, and Google Scholar and papers were selected from a time span of 2015–2023. The review outlines the importance of defining the scope and objectives of such programs, which should include… More >

Jing Qu¹, Yanmei Li^1,2, Changrong Liu¹, Wen Wang¹, Weiping Fu^1,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3787-3810, 2025, DOI:10.32604/cmc.2025.065895 - 03 July 2025

Abstract Despite the gradual transformation of traditional manufacturing by the Human-Robot Collaboration Assembly (HRCA), challenges remain in the robot’s ability to understand and predict human assembly intentions. This study aims to enhance the robot’s comprehension and prediction capabilities of operator assembly intentions by capturing and analyzing operator behavior and movements. We propose a video feature extraction method based on the Temporal Shift Module Network (TSM-ResNet50) to extract spatiotemporal features from assembly videos and differentiate various assembly actions using feature differences between video frames. Furthermore, we construct an action recognition and segmentation model based on the Refined-Multi-Scale… More >

Wonbyung Lee, Jang Hyun Kim^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

Thien-Tan Cao, Huu-Thanh Duong, Viet-Tuan Le, Hau Nguyen Trung, Vinh Truong Hoang, Kiet Tran-Trung^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4373-4391, 2025, DOI:10.32604/cmc.2025.061583 - 19 May 2025

Abstract Kinship verification is a key biometric recognition task that determines biological relationships based on physical features. Traditional methods predominantly use facial recognition, leveraging established techniques and extensive datasets. However, recent research has highlighted ear recognition as a promising alternative, offering advantages in robustness against variations in facial expressions, aging, and occlusions. Despite its potential, a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively. To address this challenge, we introduce the EarKinshipVN dataset, a novel and extensive collection of ear images designed specifically for kinship… More >

Yuxin Zhao, Zhanxiang Tan, Yuying Li, Kaiyue Zhang, Lili Guo^*, Xiaogai Hou^*

Phyton-International Journal of Experimental Botany, Vol.94, No.4, pp. 1159-1176, 2025, DOI:10.32604/phyton.2025.063737 - 30 April 2025

Abstract Receptor-like cytoplasmic kinases (RLCKs) play a crucial role in the physiological processes of plant growth and development and stress response. To elucidate the characteristics and functions of the PBL gene family in tree peonies, the whole genome identification of PBL family members in tree peonies was conducted using a bioinformatics approach based on the published Arabidopsis thaliana PBL protein sequence. A total of 51 PoPBL members were identified, which were distributed unevenly on five chromosomes in the tree peony. PoPBL proteins were localized in the nucleus, cytoplasm, chloroplasts, and mitochondria, with most members of the same clade… More >

Saleh Albahli^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1095-1128, 2025, DOI:10.32604/cmes.2025.058821 - 11 April 2025

Abstract Predicting hospital readmission and length of stay (LOS) for diabetic patients is critical for improving healthcare quality, optimizing resource utilization, and reducing costs. This study leverages machine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade. A comprehensive preprocessing pipeline, including feature selection, data transformation, and class balancing, was implemented to ensure data quality and enhance model performance. Exploratory analysis revealed key patterns, such as the influence of age and the number of diagnoses on readmission rates, guiding the More >