Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

Muhammad Waqar Khan^1,*, Adnan Ahmed Siddiqui¹, Syed Sajjad Hussain Rizvi²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070858 - 09 December 2025

Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >

Wei Liu^1,*, Ruiyang Wang¹, Guangwei Liu²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070328 - 09 December 2025

Abstract Q-learning is a classical reinforcement learning method with broad applicability. It can respond effectively to environmental changes and provide flexible strategies, making it suitable for solving robot path-planning problems. However, Q-learning faces challenges in search and update efficiency. To address these issues, we propose an improved Q-learning (IQL) algorithm. We use an enhanced Ant Colony Optimization (ACO) algorithm to optimize Q-table initialization. We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma. The IQL algorithm is extensively tested in three grid environments of different scales. The results validate the… More >

Junxiang Li^1,2, Zhipeng Dong², Ben Han³, Jianqiao Chen³, Xinxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070816 - 10 November 2025

Abstract Owing to their global search capabilities and gradient-free operation, metaheuristic algorithms are widely applied to a wide range of optimization problems. However, their computational demands become prohibitive when tackling high-dimensional optimization challenges. To effectively address these challenges, this study introduces cooperative metaheuristics integrating dynamic dimension reduction (DR). Building upon particle swarm optimization (PSO) and differential evolution (DE), the proposed cooperative methods C-PSO and C-DE are developed. In the proposed methods, the modified principal components analysis (PCA) is utilized to reduce the dimension of design variables, thereby decreasing computational costs. The dynamic DR strategy implements periodic… More >

Asal Jameel Khudhair^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068553 - 10 November 2025

Abstract Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions… More >

Jing Yang¹, Zhiming Liu^1,*, Xingchao Li², Zhongyao Wang³, Beitong Li¹, Kaiyang Liu¹, Wang Long⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068353 - 10 November 2025

Abstract Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service. However, due to the complexity of fatigue failure mechanisms, achieving accurate multiaxial fatigue life predictions remains challenging. Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions, making it difficult to maintain reliable life prediction results beyond these constraints. This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life, using Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Fully Connected Neural… More >

Ravita Chahar, Ashutosh Kumar Dubey^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-65, 2026, DOI:10.32604/cmc.2025.066990 - 10 November 2025

Abstract Objective: The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods. Conditions such as anxiety, depression, stress, bipolar disorder (BD), and autism spectrum disorder (ASD) frequently arise from the complex interplay of demographic, biological, and socioeconomic factors, resulting in aggravated symptoms. This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions. Methods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025. The potential… More >

Mircea Ţălu^1,2,*

Journal of Cyber Security, Vol.7, pp. 565-587, 2025, DOI:10.32604/jcs.2025.073690 - 11 December 2025

Abstract The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for cryptographic solutions that balance security, performance, and resource efficiency. However, existing studies often focus on isolated algorithmic families, lacking a comprehensive structural and experimental comparison across diverse lightweight cryptographic designs. This study addresses that gap by providing an integrated analysis of modern lightweight cryptographic algorithms spanning six structural classes—Substitution–Permutation Network (SPN), Feistel Network (FN), Generalized Feistel Network (GFN), Addition–Rotation–XOR (ARX), Nonlinear Feedback Shift Register (NLFSR), and Hybrid models—evaluated on resource-constrained IoT platforms. The key contributions include: (i) establishing a unified benchmarking… More >

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964 - 26 November 2025

Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

Ehan Sohn¹, Sangmyung Lee¹, Sunggon Kim¹, Kiwook Sohn¹, Manish Kumar², Yongseok Son^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2761-2785, 2025, DOI:10.32604/cmes.2025.070888 - 26 November 2025

Abstract With the accelerated growth of the Internet of Things (IoT), real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local. Since these devices often handle personal information under limited resources, cryptographic algorithms must be executed efficiently. Their computational characteristics strongly affect system performance, making it necessary to analyze resource impact and predict usage under diverse configurations. In this paper, we analyze the phase-level resource usage of AES variants, ChaCha20, ECC, and RSA on an edge device and develop a prediction model. We apply these algorithms… More >