Exploring the Future of Technology: AI, Security, and Digital Innovation
Tech Science Press
TSP publishes online periodicals and books, also hosts academical conferences.
In today’s era of rapid technological advancement, artificial intelligence and cutting-edge technologies are transforming industries at an unprecedented pace. From intelligent automation in the automotive sector to advanced cybersecurity measures, from anomaly detection in energy systems to digital modeling in materials science, groundbreaking research is shaping the future.
This edition of CMC - Computers, Materials & Continua (Vol. 82, No. 3, 2025) features eight impactful articles covering AI, optimization algorithms, privacy protection, anomaly detection, network intrusion detection, multi-view clustering, user behavior prediction, and digital twin technology. Dive into these cutting-edge studies and explore the limitless possibilities of technological innovation!
?? Read the latest articles here: https://www.techscience.com/cmc/v82n3
Featured articles：
1.Artificial Intelligence Revolutionising the Automotive Sector: A Comprehensive Review of Current Insights, Challenges, and Future Scope
Citation: M. N. Hossain, M. A. Rahim, M. M. Rahman, and D. Ramasamy, “Artificial Intelligence Revolutionising the Automotive Sector: A Comprehensive Review of Current Insights, Challenges, and Future Scope,” Comput. Mater. Contin., vol. 82, no. 3, pp. 3643–3692, 2025. https://doi.org/10.32604/cmc.2025.061749
2.Employing a Diversity Control Approach to Optimize Self-Organizing Particle Swarm Optimization Algorithms
Citation: S. Hsiao and W. Sung, “Employing a Diversity Control Approach to Optimize Self-Organizing Particle Swarm Optimization Algorithms,” Comput. Mater. Contin., vol. 82, no. 3, pp. 3891–3905, 2025. https://doi.org/10.32604/cmc.2025.060056
3.Differential Privacy Federated Learning Based on Adaptive Adjustment
Citation: Y. Cheng, W. Li, S. Qin, and T. Tu, “Differential Privacy Federated Learning Based on Adaptive Adjustment,”?Comput. Mater. Contin., vol. 82, no. 3, pp. 4777–4795, 2025.?https://doi.org/10.32604/cmc.2025.060380
4.Hybrid Memory-Enhanced Autoencoder with Adversarial Training for Anomaly Detection in Virtual Power Plants
Y. Liu, C. Pan, Y. Oh, and C. G. Lim, “Hybrid Memory-Enhanced Autoencoder with Adversarial Training for Anomaly Detection in Virtual Power Plants,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4593–4629, 2025. https://doi.org/10.32604/cmc.2025.061196
5.Diff-IDS: A Network Intrusion Detection Model Based on Diffusion Model for Imbalanced Data Samples
Citation: Y. Yang, X. Tang, Z. Liu, J. Cheng, H. Fang, and C. Zhang, “Diff-IDS: A Network Intrusion Detection Model Based on Diffusion Model for Imbalanced Data Samples,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4389–4408, 2025. https://doi.org/10.32604/cmc.2025.060357
6.Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering
Citation: K. Zhou, Y. Bai, Y. Hu, and B. Wang, “Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering,” Comput. Mater. Contin., vol. 82, no. 3, pp. 3873–3890, 2025. https://doi.org/10.32604/cmc.2025.060918
7. Learning Temporal User Features for Repost Prediction with Large Language Models
Citation: W. Sun and X. F. Liu, “Learning Temporal User Features for Repost Prediction with Large Language Models,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4117–4136, 2025. https://doi.org/10.32604/cmc.2025.059528
8.Digital Twin-Driven Modeling and Application of High-Temperature Biaxial Materials Testing Apparatus
Citation: X. Gao, P. Liu, A. Zhao, G. Huang, J. Zhang, and L. Zhou, “Digital Twin-Driven Modeling and Application of High-Temperature Biaxial Materials Testing Apparatus,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4137–4159, 2025. https://doi.org/10.32604/cmc.2025.060194
About the journal
Computers, Materials & Continua publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials. Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.
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