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, DOI:10.32604/cmes.2025.070964

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 >

Adel A.Abou El-Ela¹, Ragab A. El-Sehiemy^2,3,4,*, Abdallah Nazih¹, Asmaa A. Mubarak⁵, Eman S. Ali¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069507

Abstract Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic (PV) systems integration associated with varying loading and climate conditions. This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation, with a focus on harmonic mitigation, voltage regulation, power factor correction, and optimization techniques. The impact of passive and active filters, custom power devices such as dynamic voltage restorers (DVRs) and static synchronous compensators (STATCOMs), and grid modernization technologies on power quality is examined. Additionally, this paper… More >

Kinzah Noor¹, Agbotiname Lucky Imoize^2,*, Michael Adedosu Adelabu³, Cheng-Chi Lee^4,5,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073200

Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract

Tae-hyeon Yun¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072357

Abstract The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency… More >

Sandeerah Choudhary¹, Qaisar Abbas², Tallha Akram^3,*, Irshad Qureshi⁴, Mutlaq B. Aldajani², Hammad Salahuddin¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072200

Abstract The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete (RAC) as an eco-friendly alternative to conventional concrete. However, predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters. This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks (FFNN), Random Forest (RF), and XGBoost. A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:… More >

Yiqiao Gong^1,2, Chunlai Wu¹, Wenfeng Zheng^1,*, Siyu Lu³, Guangyu Xu⁴, Lijuan Zhang⁵, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072136

Abstract Recent Super-Resolution (SR) algorithms often suffer from excessive model complexity, high computational costs, and limited flexibility across varying image scales. To address these challenges, we propose DDNet, a dynamic and lightweight SR framework designed for arbitrary scaling factors. DDNet integrates a residual learning structure with an Adaptively fusion Feature Block (AFB) and a scale-aware upsampling module, effectively reducing parameter overhead while preserving reconstruction quality. Additionally, we introduce DDNetGAN, an enhanced variant that leverages a relativistic Generative Adversarial Network (GAN) to further improve texture realism. To validate the proposed models, we conduct extensive training using the More >

Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071512

Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

Seunghan Kim^1,#, Changhoon Lim^2,#, Gwonsang Ryu³, Hyunil Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071190

Abstract Federated Learning enables privacy-preserving training of Transformer-based language models, but remains vulnerable to backdoor attacks that compromise model reliability. This paper presents a comparative analysis of defense strategies against both classical and advanced backdoor attacks, evaluated across autoencoding and autoregressive models. Unlike prior studies, this work provides the first systematic comparison of perturbation-based, screening-based, and hybrid defenses in Transformer-based FL environments. Our results show that screening-based defenses consistently outperform perturbation-based ones, effectively neutralizing most attacks across architectures. However, this robustness comes with significant computational overhead, revealing a clear trade-off between security and efficiency. By explicitly More >

Mona Almofarreh¹, Amnah Alshahrani², Nouf Helal Alharbi³, Ahmed Omer Ahmed⁴, Hussain Alshahrani⁵, Abdulrahman Alzahrani^6,*, Mohammed Mujib Alshahrani⁷, Asma A. Alhashmi⁸

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070545

Abstract Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools. This study indicates that zero-day attacks have a significant impact on computer security. A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks, as the signatures of zero-day attacks are usually not previously accessible. A machine learning (ML)-based detection algorithm is proficient in capturing statistical features of attacks and, therefore, optimistic for zero-day attack detection. ML and deep learning (DL) are employed for designing intrusion detection systems. The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS… More >

Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070426

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >