Somayah Albaradei^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2937-2970, 2025, DOI:10.32604/cmes.2025.072584 - 23 December 2025

Abstract Cancer deaths and new cases worldwide are projected to rise by 47% by 2040, with transitioning countries experiencing an even higher increase of up to 95%. Tumor severity is profoundly influenced by the timing, accuracy, and stage of diagnosis, which directly impacts clinical decision-making. Various biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, contribute to cancer development. The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers. This integrative approach supports the notion that cancer is fundamentally driven by such alterations, enabling the discovery of molecular signatures… More > Graphic Abstract

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 >

Alex Segura^*, Joaquim Meléndez

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 649-672, 2025, DOI:10.32604/cmes.2025.070954 - 30 October 2025

Abstract The increasing use of renewable energy sources, combined with the increase in electricity demand, has highlighted the importance of energy flexibility management in electrical grids. Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation, ensuring the stability and efficiency of the grid. Thus, Local Flexibility Markets (LFMs) are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level, where the Distribution System Operators (DSOs) are the flexibility-demanding actors, and prosumers are the flexibility providers. This paper investigates the… More >

Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, Suhaila Abuowaida⁶, Naceur Chihaoui⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004 - 30 October 2025

Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >

Shuxun Li^1,2, Xinhao Liu^1,2,*, Yu Zhang^1,2, Yu Zhao^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2495-2526, 2025, DOI:10.32604/fdmp.2025.068674 - 30 October 2025

Abstract Hydraulic control valves, positioned at the terminus of pipe networks, are critical for regulating flow and pressure, thereby ensuring the operational safety and efficiency of pipeline systems. However, conventional valve designs often struggle to maintain effective regulation across a wide range of system pressures. To address this limitation, this study introduces a novel Pilot hydraulic valves specifically engineered for enhanced dynamic performance and precise regulation under variable pressure conditions. Building upon prior experimental findings, the proposed design integrates a high-fidelity simulation framework and a surrogate model-based optimization strategy. The study begins by formulating a comprehensive… More >

Duong Tien Dung^1,2,3, Ha Hai Nam⁴, Nguyen Long Giang³, Luong Thi Hong Lan^5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5341-5357, 2025, DOI:10.32604/cmc.2025.068299 - 23 October 2025

Abstract Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data, guided by active learning, to enhance classification accuracy, particularly in complex and ambiguous datasets. Although several active semi-supervised fuzzy clustering methods have been developed previously, they typically face significant limitations, including high computational complexity, sensitivity to initial cluster centroids, and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters. This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify, analyze, and correct misclassified boundary elements. By strategically utilizing labeled data through active learning, our 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 >

Abrar M. Alajlan^1,*, Marwah M. Almasri²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5237-5262, 2025, DOI:10.32604/cmc.2025.066736 - 23 October 2025

Abstract Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats. Attackers can non-invasively manipulate sensors and spoof controllers, which in turn increases the autonomy of the system. Even though the focus on protecting against sensor attacks increases, there is still uncertainty about the optimal timing for attack detection. Existing systems often struggle to manage the trade-off between latency and false alarm rate, leading to inefficiencies in real-time anomaly detection. This paper presents a framework designed to monitor, predict, and control dynamic systems with a particular emphasis on detecting and adapting to… More >