Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025

Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

Myoung-oh Choi¹, Mincheol Shin¹, Hyonjun Kang¹, Ka Lok Man², Mucheol Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3191-3212, 2025, DOI:10.32604/cmes.2025.068723 - 30 September 2025

Abstract The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification. To address this challenge, we propose Vulnerability2Vec, a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience. Vulnerability2Vec converts Common Vulnerabilities and Exposures (CVE) text explanations to semantic graphs, where nodes represent CVE IDs and key terms (nouns, verbs, and adjectives), and edges capture co-occurrence relationships. Then, it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram More >

Mian Muhammad Kamal^1,*, Syed Zain Ul Abideen², M. A. Al-Khasawneh^3,4, Alaa M. Momani⁴, Hala Mostafa⁵, Mohammed Salem Atoum⁶, Saeed Ullah⁷, Jamil Abedalrahim Jamil Alsayaydeh^8,*, Mohd Faizal Bin Yusof⁹, Suhaila Binti Mohd Najib⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3053-3083, 2025, DOI:10.32604/cmes.2025.067430 - 30 September 2025

Abstract Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue. However, low resolution, occlusion, and background interference make small object detection a complex and demanding task. One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities. This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations, such as Hyperspectral–Multispectral (HS-MS), Hyperspectral–Synthetic Aperture Radar (HS-SAR), and HS-SAR–Digital Surface Model (HS-SAR-DSM). The detection process is done by the proposed Jaccard Deep Q-Net (JDQN), which More >

Xiao-Xiao Luo, Bi Peng, Jian-Hua Wang, Guang-Yuan Hu, Xiang-Lin Yuan, Guo-Xian Long^*

Oncology Research, Vol.33, No.10, pp. 2903-2921, 2025, DOI:10.32604/or.2025.066308 - 26 September 2025

Abstract Objective: The World Health Organization (WHO) grading based on histopathology cannot always accurately predict tumor behavior of meningiomas. To overcome the limitations of the WHO grading, the study aims to propose a novel oxidative stress-based molecular classification for WHO grade 2/3 meningiomas. Methods: Differentially expressed oxidative stress-related genes were analyzed between 86 WHO grade 1 (low grade) meningiomas and 99 grade 2/3 (high grade) meningiomas. An oxidative stress-based molecular classification was developed in high-grade meningiomas through consensus clustering analysis. Immune microenvironment features, responses to immunotherapy and chemotherapy, and targeted drugs were evaluated. Three machine learning… More >

Hoang-Dieu Vu^1,2, Duc-Nghia Tran³, Quang-Tu Pham¹, Ngoc-Linh Nguyen^4,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2819-2835, 2025, DOI:10.32604/cmc.2025.068355 - 23 September 2025

Abstract This study presents CGB-Net, a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer, with direct applicability to gastroesophageal reflux disease (GERD) monitoring. Unlike conventional approaches limited to four basic postures, CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions, providing enhanced resolution for personalized health assessment. The architecture introduces a unique integration of three complementary components: 1D Convolutional Neural Networks (1D-CNN) for efficient local spatial feature extraction, Gated Recurrent Units (GRU) to capture short-term temporal dependencies with reduced computational complexity, and Bidirectional Long Short-Term Memory… More >

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3945-3963, 2025, DOI:10.32604/cmc.2025.067538 - 23 September 2025

Abstract Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The More >

Sarab Almuhaideb^1,*, Najwa Altwaijry¹, Isra Al-Turaiki¹, Ahmad Raza Khan², Hamza Ali Rizvi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3095-3128, 2025, DOI:10.32604/cmc.2025.067390 - 23 September 2025

Abstract Many bioinformatics applications require determining the class of a newly sequenced Deoxyribonucleic acid (DNA) sequence, making DNA sequence classification an integral step in performing bioinformatics analysis, where large biomedical datasets are transformed into valuable knowledge. Existing methods rely on a feature extraction step and suffer from high computational time requirements. In contrast, newer approaches leveraging deep learning have shown significant promise in enhancing accuracy and efficiency. In this paper, we investigate the performance of various deep learning architectures: Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (CNN-LSTM), CNN-Bidirectional Long Short-Term Memory (CNN-BiLSTM), Residual Network (ResNet), and… More >

BeomJun Jo¹, SeongKi Kim^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2701-2714, 2025, DOI:10.32604/cmc.2025.067280 - 23 September 2025

Abstract The rapid evolution of virtual reality (VR) and augmented reality (AR) technologies has significantly transformed human-computer interaction, with applications spanning entertainment, education, healthcare, industry, and remote collaboration. A central challenge in these immersive systems lies in enabling intuitive, efficient, and natural interactions. Hand gesture recognition offers a compelling solution by leveraging the expressiveness of human hands to facilitate seamless control without relying on traditional input devices such as controllers or keyboards, which can limit immersion. However, achieving robust gesture recognition requires overcoming challenges related to accurate hand tracking, complex environmental conditions, and minimizing system latency.… More >

Batyrkhan Omarov^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2195-2253, 2025, DOI:10.32604/cmc.2025.064799 - 23 September 2025

Abstract Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability More >

Musulmon Lolaev¹, Anand Paul^2,*, Jeonghong Kim¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4155-4171, 2025, DOI:10.32604/cmc.2025.064758 - 23 September 2025

Abstract Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights… More >