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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
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
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 >

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861

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Zhenhui Cai, Kaiqing Zhou^*, Zhouhua Liao
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2255-2303, 2025, DOI:10.32604/cmc.2025.067994 - 23 September 2025
Abstract The YOLO (You Only Look Once) series, a leading single-stage object detection framework, has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance. Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection, lesion segmentation, and microscopic image analysis, thereby accelerating the development of clinical decision support systems. This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024. It compares YOLO’s performance with other models (e.g., Faster R-CNN, RetinaNet) in medical contexts, summarizes standard evaluation metrics (e.g.,… More >

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876

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Enas W. Abood¹, Ali A. Yassin^2,*, Zaid Ameen Abduljabbar^2,3,4,*, Vincent Omollo Nyangaresi^5,6, Iman Qays Abduljaleel², Abdulla J. Y. Aldarwish², Husam A. Neamah^7,8
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2305-2345, 2025, DOI:10.32604/cmc.2025.066579 - 23 September 2025
Abstract The digital revolution era has impacted various domains, including healthcare, where digital technology enables access to and control of medical information, remote patient monitoring, and enhanced clinical support based on the Internet of Health Things (IoHTs). However, data privacy and security, data management, and scalability present challenges to widespread adoption. This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT, highlighting their critical roles in ensuring secure data exchange and patient privacy. This includes various authentication technologies and strategies, such as biometric and multi-factor authentication, as well as the influence More >

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626

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Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025
（This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)
Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >

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584

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Gaoyong Lu¹, Yang Ou¹, Zhihong Wang², Yingnan Qu², Yingsheng Xia², Dibin Tang², Igor Kotenko³, Wei Li^2,4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2403-2441, 2025, DOI:10.32604/cmc.2025.068024 - 23 September 2025
Abstract Deep learning (DL) has revolutionized time series forecasting (TSF), surpassing traditional statistical methods (e.g., ARIMA) and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data. This comprehensive survey reviews state-of-the-art DL architectures for TSF, focusing on four core paradigms: (1) Convolutional Neural Networks (CNNs), adept at extracting localized temporal features; (2) Recurrent Neural Networks (RNNs) and their advanced variants (LSTM, GRU), designed for sequential dependency modeling; (3) Graph Neural Networks (GNNs), specialized for forecasting structured relational data with spatial-temporal dependencies; and (4) Transformer-based models, leveraging self-attention mechanisms to… More >

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1711

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722

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Omar Sabri¹, Bassam Al-Shargabi^2,*, Abdelrahman Abuarqoub²
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2443-2486, 2025, DOI:10.32604/cmc.2025.066987 - 23 September 2025
Abstract This review comprehensively analyzes advancements in artificial intelligence, particularly machine learning and deep learning, in medical imaging, focusing on their transformative role in enhancing diagnostic accuracy. Our in-depth analysis of 138 selected studies reveals that artificial intelligence (AI) algorithms frequently achieve diagnostic performance comparable to, and often surpassing, that of human experts, excelling in complex pattern recognition. Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy, alongside radiologist-level performance for pneumonia detection on chest X-rays. These technologies profoundly transform imaging by significantly improving processes in classification, segmentation, and sequential analysis across… More >

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560

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Pengpeng Yang^1,2,*, Chen Zhou¹, Dasara Shullani², Lanxi Liu¹, Daniele Baracchi²
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2487-2526, 2025, DOI:10.32604/cmc.2025.069129 - 23 September 2025
Abstract Images and videos play an increasingly vital role in daily life and are widely utilized as key evidentiary sources in judicial investigations and forensic analysis. Simultaneously, advancements in image and video processing technologies have facilitated the widespread availability of powerful editing tools, such as Deepfakes, enabling anyone to easily create manipulated or fake visual content, which poses an enormous threat to social security and public trust. To verify the authenticity and integrity of images and videos, numerous approaches have been proposed, which are primarily based on content analysis and their effectiveness is susceptible to interference… More >

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865

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543

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Oshan Mudannayake^1,#, Amila Indika^2,#, Upul Jayasinghe², Gyu Myoung Lee^3,*, Janaka Alawatugoda⁴
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2527-2578, 2025, DOI:10.32604/cmc.2025.068875 - 23 September 2025
Abstract The rapid adoption of machine learning in sensitive domains, such as healthcare, finance, and government services, has heightened the need for robust, privacy-preserving techniques. Traditional machine learning approaches lack built-in privacy mechanisms, exposing sensitive data to risks, which motivates the development of Privacy-Preserving Machine Learning (PPML) methods. Despite significant advances in PPML, a comprehensive and focused exploration of Secure Multi-Party Computing (SMPC) within this context remains underdeveloped. This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML, offering a structured overview of current techniques, challenges, and future directions. More >

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2394

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593

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Alsoudi Dua¹, Tan Fong Ang¹, Chin Soon Ku^2,*, Okmi Mohammed^1,3, Yu Luo⁴, Jiahui Chen⁴, Uzair Aslam Bhatti⁵, Lip Yee Por^1,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2579-2624, 2025, DOI:10.32604/cmc.2025.070413 - 23 September 2025
（This article belongs to the Special Issue: Advancing Network Intelligence: Communication, Sensing and Computation)
Abstract Blockchain interoperability enables seamless communication and asset transfer across isolated permissioned blockchain systems, but it introduces significant security and privacy vulnerabilities. This review aims to systematically assess the security and privacy landscape of interoperability protocols for permissioned blockchains, identifying key properties, attack vectors, and countermeasures. Using PRISMA 2020 guidelines, we analysed 56 peer-reviewed studies published between 2020 and 2025, retrieved from Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The review focused on interoperability protocols for permissioned blockchains with security and privacy analyses, including only English-language journal articles and conference proceedings. Risk of bias in… More >

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1829

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897

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REVIEW

Nezir Aydin^1,2,*, Melike Cari³, Betul Kara³, Ertugrul Ayyildiz^1,3
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2625-2650, 2025, DOI:10.32604/cmc.2025.067290 - 23 September 2025
（This article belongs to the Special Issue: Fuzzy Logic: Next-Generation Algorithms and Applications)
Abstract Transportation systems are rapidly transforming in response to urbanization, sustainability challenges, and advances in digital technologies. This review synthesizes the intersection of artificial intelligence (AI), fuzzy logic, and multi-criteria decision-making (MCDM) in transportation research. A comprehensive literature search was conducted in the Scopus database, utilizing carefully selected AI, fuzzy, and MCDM keywords. Studies were rigorously screened according to explicit inclusion and exclusion criteria, resulting in 73 eligible publications spanning 2006–2025. The review protocol included transparent data extraction on methodological approaches, application domains, and geographic distribution. Key findings highlight the prevalence of hybrid fuzzy AHP and… More >

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1200

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590

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ARTICLE

Thai-Viet Dang^1,*, Dinh-Manh-Cuong Tran¹, Nhu-Nghia Bui¹, Phan Xuan Tan^2,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2651-2680, 2025, DOI:10.32604/cmc.2025.067500 - 23 September 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Precise and robust three-dimensional object detection (3DOD) presents a promising opportunity in the field of mobile robot (MR) navigation. Monocular 3DOD techniques typically involve extending existing two-dimensional object detection (2DOD) frameworks to predict the three-dimensional bounding box (3DBB) of objects captured in 2D RGB images. However, these methods often require multiple images, making them less feasible for various real-time scenarios. To address these challenges, the emergence of agile convolutional neural networks (CNNs) capable of inferring depth from a single image opens a new avenue for investigation. The paper proposes a novel ELDE-Net network designed to… More >

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1037

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634

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Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2681-2700, 2025, DOI:10.32604/cmc.2025.069235 - 23 September 2025
Abstract Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of visible and infrared images. However, the inherent differences in the imaging mechanisms of visible and infrared modalities make effective cross-modal fusion challenging. Furthermore, constrained by the physical characteristics of sensors and thermal diffusion effects, infrared images generally suffer from blurred object contours and missing details, making it difficult to extract object features effectively. To address these issues, we propose an infrared-visible image fusion network that realizes multimodal information fusion… More >

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1159

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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 >

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1116

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557

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Ashwini Bhat¹, Nagaraj N. Katagi¹, M. C. Gowrishankar², Manjunath Shettar^2,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2715-2728, 2025, DOI:10.32604/cmc.2025.069842 - 23 September 2025
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)
Abstract This research explores the water uptake behavior of glass fiber/epoxy composites filled with nanoclay and establishes an Artificial Neural Network (ANN) to predict water uptake percentage from experimental parameters. Composite laminates are fabricated with varying glass fiber and nanoclay contents. Water absorption is evaluated for 70 days of immersion following ASTM D570-98 standards. The inclusion of nanoclay reduces water uptake by creating a tortuous path for moisture diffusion due to its high aspect ratio and platelet morphology, thereby enhancing the composite’s barrier properties. The ANN model is developed with a 3–4–1 feedforward structure and learned… More >

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921

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Shige Wang¹, Yalong Liang², Lian Huang³, Pei Li^4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2729-2748, 2025, DOI:10.32604/cmc.2025.068351 - 23 September 2025
（This article belongs to the Special Issue: Advanced Computational Modeling and Simulations for Engineering Structures and Multifunctional Materials: Bridging Theory and Practice)
Abstract This study introduces a hybrid Cuckoo Search-Deep Neural Network (CS-DNN) model for uncertainty quantification and composition optimization of Na_1/2Bi_1/2TiO₃ (NBT)-based dielectric energy storage ceramics. Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate (1 − x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD, SEM, and electrical characterization. The key innovation lies in integrating the CS metaheuristic algorithm with a DNN, overcoming local minima in training and establishing a robust composition-property prediction framework. Our model accurately… More >

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816

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485

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ARTICLE

Mianyue Yang^1,*, Huasheng Sun¹, Weigao Sheng²
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2749-2761, 2025, DOI:10.32604/cmc.2025.068009 - 23 September 2025
（This article belongs to the Special Issue: Computing Technology in the Design and Manufacturing of Advanced Materials)
Abstract The shear pin of the friction pendulum bearing (FPB) can be made of 40Cr steel. In conceptual design, the optimal cut-off point of the shear pin is predetermined, guiding the design of bridges isolated by FPBs to maximize their isolation performance. Current researches on the shear pins are mainly based on linear elastic models, neglecting their plasticity, damage, and fracture mechanical properties. To accurately predict its cutoff behavior, the elastic-plastic degradation model of 40Cr steel is indeed calibrated. For this purpose, the Ramberg-Osgood model, the Bao-Wierzbicki damage initiation criterion, and the linear damage evolution criterion… More >

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841

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503

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Wen Qi¹, Yu-Fei Du¹, Bo-Han Chen², Gui-Lei An^1,3,*, Chun Lu^4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2763-2780, 2025, DOI:10.32604/cmc.2025.069505 - 23 September 2025
（This article belongs to the Special Issue: Molecular Simulations of Polymer Materials)
Abstract In the production process of silicone sealant, mineral oil is used to replace methyl silicone oil plasticizer in silicone sealant to reduce costs and increase efficiency. However, the silicone sealant content in mineral oil is prone to premature aging, which significantly reduces the mechanical properties of the silicone sealant and severely affects its service life. At the same time, there are few reports on the simulation research of the performance of silicone sealant. In this study, three mixed system models of crosslinking silicone sealant/plasticizer are constructed by the molecular dynamics simulation method, and the effect… More >

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856

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Boudjema Bendaho¹, Abdelhak Mesbah¹, Zakaria Belabed^1,2,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2781-2805, 2025, DOI:10.32604/cmc.2025.069709 - 23 September 2025
（This article belongs to the Special Issue: Advanced Modeling of Smart and Composite Materials and Structures)
Abstract A new quadrilateral finite element IQ4 is developed for the free vibration of carbon nanotube-reinforced composite (CNTRC) perforated plates with a central cutout. By enriching the membrane part and incorporating a projected shear technique, the IQ4 element is proposed to address the known limitations of the standard Q4 element, such as shear locking and limited consistency in the coupling of membrane-bending components. The proposed element is formulated within the FSDT-based framework and assessed through benchmark tests to verify its convergence and accuracy. The governing equations are obtained via the weak form of Hamilton’s principle. Particular… More >

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Dan Xu^1,2, Chuanfu Luo^1,2,3,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2807-2818, 2025, DOI:10.32604/cmc.2025.069471 - 23 September 2025
（This article belongs to the Special Issue: Molecular Simulations of Polymer Materials)
Abstract The crystallization behavior of polymers is significantly influenced by molecular chain length and the dispersion of varying chain lengths. The complexity of studying crystallization arises from the dispersity of polymer materials and the typically slow cooling rates. Recent advancements in fast cooling techniques have rendered the investigation of polymer crystallization at varying cooling rates an attractive area of research; however, a systematic quantitative framework for this process is still lacking. We employ a coarse-grained model for polyvinyl alcohol (CG-PVA) in molecular dynamics simulations to study the crystallization of linear polymers with varying chain lengths under… More >

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828

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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 >

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975

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522

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Weiqiang Xin^1,2,3,#, Zheng Wang^4,#, Xi Chen^1,5, Yufeng Tang¹, Bing Li¹, Chunwei Tian^2,5,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2837-2849, 2025, DOI:10.32604/cmc.2025.069784 - 23 September 2025
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract Deep convolutional neural networks (CNNs) have demonstrated remarkable performance in video super-resolution (VSR). However, the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction. To address this limitation, we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network (3D-ERVSNet). This network employs a forward and backward bidirectional propagation module (FBBPM) that aligns features across frames using explicit optical flow through lightweight SPyNet. By incorporating an enhanced residual structure (ERS) with skip connections, shallow and deep features are effectively integrated,… More >

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904

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A-Seong Moon¹, Seungyeon Jeong¹, Donghee Kim¹, Mohd Asyraf Zulkifley², Bong-Soo Sohn^3,*, Jaesung Lee^1,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2851-2872, 2025, DOI:10.32604/cmc.2025.067103 - 23 September 2025
Abstract Emotion recognition under uncontrolled and noisy environments presents persistent challenges in the design of emotionally responsive systems. The current study introduces an audio-visual recognition framework designed to address performance degradation caused by environmental interference, such as background noise, overlapping speech, and visual obstructions. The proposed framework employs a structured fusion approach, combining early-stage feature-level integration with decision-level coordination guided by temporal attention mechanisms. Audio data are transformed into mel-spectrogram representations, and visual data are represented as raw frame sequences. Spatial and temporal features are extracted through convolutional and transformer-based encoders, allowing the framework to capture… More >

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Zihan Jia, Bowen Ma, Dongyue Chen^*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460 - 23 September 2025
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >

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802

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488

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Seulki Han¹, Sangho Son², Won Sakong², Haemin Jung^3,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2893-2912, 2025, DOI:10.32604/cmc.2025.067211 - 23 September 2025
Abstract As cyber threats become increasingly sophisticated, Distributed Denial-of-Service (DDoS) attacks continue to pose a serious threat to network infrastructure, often disrupting critical services through overwhelming traffic. Although unsupervised anomaly detection using convolutional autoencoders (CAEs) has gained attention for its ability to model normal network behavior without requiring labeled data, conventional CAEs struggle to effectively distinguish between normal and attack traffic due to over-generalized reconstructions and naive anomaly scoring. To address these limitations, we propose CA-CAE, a novel anomaly detection framework designed to improve DDoS detection through asymmetric joint reconstruction learning and refined anomaly scoring. Our… More >

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720

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465

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Panyu Liu¹, Tongqing Zhou^1,*, Guofeng Lu², Huaizhe Zhou³, Zhiping Cai¹
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2913-2934, 2025, DOI:10.32604/cmc.2025.069175 - 23 September 2025
Abstract The isolation of healthcare data among worldwide hospitals and institutes forms barriers for fully realizing the data-hungry artificial intelligence (AI) models promises in renewing medical services. To overcome this, privacy-preserving distributed learning frameworks, represented by swarm learning and federated learning, have been investigated recently with the sensitive healthcare data retaining in its local premises. However, existing frameworks use a one-size-fits-all mode that tunes one model for all healthcare situations, which could hardly fit the usually diverse disease prediction in practice. This work introduces the idea of ensemble learning into privacy-preserving distributed learning and presents the More >

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696

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478

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Noman Khan¹, Afnan², Mi Young Lee^3,*, Jakyoung Min^4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2935-2958, 2025, DOI:10.32604/cmc.2025.069058 - 23 September 2025
Abstract Fine-grained dog breed classification presents significant challenges due to subtle inter-class differences, pose variations, and intra-class diversity. To address these complexities and limitations of traditional handcrafted approaches, a novel and efficient two-stage Deep Learning (DL) framework tailored for robust fine-grained classification is proposed. In the first stage, a lightweight object detector, YOLO v8N (You Only Look Once Version 8 Nano), is fine-tuned to localize both the head and full body of the dog from each image. In the second stage, a dual-stream Vision Transformer (ViT) architecture independently processes the detected head and body regions, enabling… More >

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588

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Xi Li^1,2, Runpu Nie^1,*, Zhaoyong Fan², Lianying Zou², Zhenhua Xiao², Kaile Dong¹
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2959-2984, 2025, DOI:10.32604/cmc.2025.066740 - 23 September 2025
Abstract With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) More >

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1110

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626

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Hoon Ko¹, Marek R. Ogiela², Libor Mesicek³, Sangheon Kim^4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2985-2997, 2025, DOI:10.32604/cmc.2025.065885 - 23 September 2025
Abstract The rapid evolution of AI-driven cybersecurity solutions has led to increasingly complex network infrastructures, which in turn increases their exposure to sophisticated threats. This study proposes a Graph Neural Network (GNN)-based feature selection strategy specifically tailored for Network Intrusion Detection Systems (NIDS). By modeling feature correlations and leveraging their topological relationships, this method addresses challenges such as feature redundancy and class imbalance. Experimental analysis using the KDDTest+ dataset demonstrates that the proposed model achieves 98.5% detection accuracy, showing notable gains in both computational efficiency and minority class detection. Compared to conventional machine learning methods, the More >

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854

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513

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Peng Xiao¹, Haiyu Xu¹, Peng Xu¹, Zhiwei Guo^1,*, Amr Tolba^2,*, Osama Alfarraj²
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2999-3022, 2025, DOI:10.32604/cmc.2025.066937 - 23 September 2025
Abstract Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare. In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology, as well as the existence of information redundancy in cross-modal data fusion, this paper proposes a multimodal fusion framework based on cross-modal collaboration and weighted attention mechanism. In terms of feature extraction, the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture, overcoming the limitations of traditional single-modality models in… More >

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656

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Manuel J. C. S. Reis^*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3023-3039, 2025, DOI:10.32604/cmc.2025.066390 - 23 September 2025
（This article belongs to the Special Issue: Algorithms for Planning and Scheduling Problems)
Abstract The Vehicle Routing Problem with Time Windows (VRPTW) presents a significant challenge in combinatorial optimization, especially under real-world uncertainties such as variable travel times, service durations, and dynamic customer demands. These uncertainties make traditional deterministic models inadequate, often leading to suboptimal or infeasible solutions. To address these challenges, this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms (GA) with Local Search (LS), while incorporating stochastic uncertainty modeling through probabilistic travel times. The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance. This adaptivity enhances the algorithm’s… More >

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826

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512

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Siyi Wang, Yan Zhuang^*, Zhizhuang Zhou, Xinhao Wang, Menglan Li
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3041-3066, 2025, DOI:10.32604/cmc.2025.067636 - 23 September 2025
Abstract Heap memory anomalies, such as Use-After-Free (UAF), Double-Free, and Memory Leaks, pose critical security threats including system crashes, data leakage, and remote exploits. Existing methods often fail to handle multiple anomaly types and meet real-time detection demands. To address these challenges, this paper proposes MemHookNet, a real-time multi-class heap anomaly detection framework that combines log hooking with deep learning. Without modifying source code, MemHookNet non-intrusively captures memory operation logs at runtime and transforms them into structured sequences encoding operation types, pointer identifiers, thread context, memory sizes, and temporal intervals. A sliding-window Long Short-Term Memory (LSTM) More >

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579

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Tong Wu^*, Dawei Zhou
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3067-3094, 2025, DOI:10.32604/cmc.2025.066525 - 23 September 2025
Abstract Fault attacks have emerged as an increasingly effective approach for integrated circuit security attacks due to their short execution time and minimal data requirement. However, the lack of a unified leakage model remains a critical challenge, as existing methods often rely on algorithm-specific details or prior knowledge of plaintexts and intermediate values. This paper proposes the Fault Probability Model based on Hamming Weight (FPHW) to address this. This novel statistical framework quantifies fault attacks by solely analyzing the statistical response of the target device, eliminating the need for attack algorithm details or implementation specifics. Building… More >

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1018

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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
（This article belongs to the Special Issue: Emerging Machine Learning Methods and Applications)
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 >

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Chao Li^1,3,#, Chen Wang^1,3,#, Caichang Ding^2,*, Yonghao Liao^1,3, Zhiwei Ye^1,3
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3129-3149, 2025, DOI:10.32604/cmc.2025.067440 - 23 September 2025
Abstract Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the… More >

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759

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Siham Essahraui¹, Ismail Lamaakal¹, Yassine Maleh^2,*, Khalid El Makkaoui¹, Mouncef Filali Bouami¹, Ibrahim Ouahbi¹, May Almousa³, Ali Abdullah S. AlQahtani⁴, Ahmed A. Abd El-Latif^5,6
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3151-3183, 2025, DOI:10.32604/cmc.2025.067359 - 23 September 2025
Abstract The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments, as traditional proctoring methods fall short in preventing cheating. The increase in cheating during online exams highlights the need for efficient, adaptable detection models to uphold academic credibility. This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems, evaluating their accuracy, efficiency, and adaptability. We benchmark several advanced architectures, including EfficientNet, MobileNetV2, ResNet variants and more, using two specialized datasets (OEP and OP) tailored for online proctoring contexts. Our findings More >

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1677

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737

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Yiming Zhang¹, Wei Zhang^1,2,*, Cong Shen³
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3185-3211, 2025, DOI:10.32604/cmc.2025.067563 - 23 September 2025
Abstract Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement… More >

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575

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Valentin Stangaciu^*, Mihai-Vladimir Ghimpau, Adrian-Gabriel Sztanarec
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3213-3229, 2025, DOI:10.32604/cmc.2025.068844 - 23 September 2025
Abstract Along with process control, perception represents the main function performed by the Edge Layer of an Internet of Things (IoT) network. Many of these networks implement various applications where the response time does not represent an important parameter. However, in critical applications, this parameter represents a crucial aspect. One important sensing device used in IoT designs is the accelerometer. In most applications, the response time of the embedded driver software handling this device is generally not analysed and not taken into account. In this paper, we present the design and implementation of a predictable real-time More >

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483

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Xinyu Yu¹, Lucheng Chen^2,3, Xingzhi Feng^2,4, Xiaoping Lu^2,4,*, Yuye Yang¹, You Shi^5,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3231-3252, 2025, DOI:10.32604/cmc.2025.066975 - 23 September 2025
Abstract This paper investigates mobility-aware online optimization for digital twin (DT)-assisted task execution in edge computing environments. In such systems, DTs, hosted on edge servers (ESs), require proactive migration to maintain proximity to their mobile physical twin (PT) counterparts. To minimize task response latency under a stringent energy consumption constraint, we jointly optimize three key components: the status data uploading frequency from the PT, the DT migration decisions, and the allocation of computational and communication resources. To address the asynchronous nature of these decisions, we propose a novel two-timescale mobility-aware online optimization (TMO) framework. The TMO… More >

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564

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Nareshkumar Raveendhran, Nimala Krishnan^*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3253-3265, 2025, DOI:10.32604/cmc.2025.067490 - 23 September 2025
Abstract Social media outlets deliver customers a medium for communication, exchange, and expression of their thoughts with others. The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation. Utilising the user corpus, characteristics of social platform users, and other data, academic research may accurately discern the personality traits of users. This research examines the traits of consumer personalities. Usually, personality tests administered by psychological experts via interviews or self-report questionnaires are costly, time-consuming, complex, and labour-intensive. Currently, academics in computational linguistics are increasingly focused on predicting… More >

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681

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Gang Li^1,#, Ru Wang^1,#, Yang Zhang^2,*, Chuanyun Xu², Xinyu Fan¹, Zheng Zhou¹, Pengfei Lv¹, Zihan Ruan¹
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3267-3288, 2025, DOI:10.32604/cmc.2025.067763 - 23 September 2025
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract Currently, challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection. Since small objects occupy only a few pixels in an image, the extracted features are limited, and mainstream downsampling convolution operations further exacerbate feature loss. Additionally, due to the occlusion-prone nature of small objects and their higher sensitivity to localization deviations, conventional Intersection over Union (IoU) loss functions struggle to achieve stable convergence. To address these limitations, LR-Net is proposed for small object detection. Specifically, the proposed Lossless Feature Fusion (LFF) method transfers spatial… More >

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879

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ARTICLE

Salam Al-E’mari¹, Yousef Sanjalawe^2,*, Budoor Allehyani³, Ghader Kurdi⁴, Sharif Makhadmeh², Ameera Jaradat⁵, Duaa Hijazi⁶
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3289-3320, 2025, DOI:10.32604/cmc.2025.067950 - 23 September 2025
Abstract Electric Vehicle Charging Systems (EVCS) are increasingly vulnerable to cybersecurity threats as they integrate deeply into smart grids and Internet of Things (IoT) environments, raising significant security challenges. Most existing research primarily emphasizes network-level anomaly detection, leaving critical vulnerabilities at the host level underexplored. This study introduces a novel forensic analysis framework leveraging host-level data, including system logs, kernel events, and Hardware Performance Counters (HPC), to detect and analyze sophisticated cyberattacks such as cryptojacking, Denial-of-Service (DoS), and reconnaissance activities targeting EVCS. Using comprehensive forensic analysis and machine learning models, the proposed framework significantly outperforms existing More >

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773

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Nouman Ahmad^*, Changsheng Zhang
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3321-3334, 2025, DOI:10.32604/cmc.2025.067044 - 23 September 2025
（This article belongs to the Special Issue: Utilizing and Securing Large Language Models for Cybersecurity and Beyond)
Abstract Source code vulnerabilities present significant security threats, necessitating effective detection techniques. Rigid rule-sets and pattern matching are the foundation of traditional static analysis tools, which drown developers in false positives and miss context-sensitive vulnerabilities. Large Language Models (LLMs) like BERT, in particular, are examples of artificial intelligence (AI) that exhibit promise but frequently lack transparency. In order to overcome the issues with model interpretability, this work suggests a BERT-based LLM strategy for vulnerability detection that incorporates Explainable AI (XAI) methods like SHAP and attention heatmaps. Furthermore, to ensure auditable and comprehensible choices, we present a… More >

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540

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Hazem Farah¹, Akram Bennour^1,*, Hama Soltani¹, Mouaaz Nahas², Rashiq Rafiq Marie³, Mohammed Al-Sarem^3,4,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3335-3348, 2025, DOI:10.32604/cmc.2025.067226 - 23 September 2025
Abstract This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an… More >

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481

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Jun Li^#,*, Kai Xu^#,*, Baozhu Chen, Xiaohan Yang, Mengting Sun, Guojun Li, HaoJie Du
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3349-3368, 2025, DOI:10.32604/cmc.2025.067316 - 23 September 2025
Abstract Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving, social robotics, and intelligent surveillance systems. Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents. These interactions are critical to trajectory prediction accuracy. While prior studies have employed Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to model such interactions, these methods fail to distinguish varying influence levels among neighboring pedestrians. To address this, we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions. Specifically,… More >

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524

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You-Jyun Yeh¹, Edward T.-H. Chu^1,*, Chia-Rong Lee², Jiun Hsu³, Hui-Mei Wu³
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3369-3391, 2025, DOI:10.32604/cmc.2025.065336 - 23 September 2025
（This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)
Abstract Porters play a crucial role in hospitals because they ensure the efficient transportation of patients, medical equipment, and vital documents. Despite its importance, there is a lack of research addressing the prediction of completion times for porter tasks. To address this gap, we utilized real-world porter delivery data from National Taiwan University Hospital, Yunlin Branch, Taiwan. We first identified key features that can influence the duration of porter tasks. We then employed three widely-used machine learning algorithms: decision tree, random forest, and gradient boosting. To leverage the strengths of each algorithm, we finally adopted an… More >

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549

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Youlong Lyu^1,2,*, Ying Chu³, Qingpeng Qiu³, Jie Zhang^1,2, Jutao Guo⁴
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3393-3418, 2025, DOI:10.32604/cmc.2025.068157 - 23 September 2025
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract In intelligent manufacturing processes such as aerospace production, computer numerical control (CNC) machine tools require real-time optimization of process parameters to meet precision machining demands. These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings, highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime. However, varying conditions induce feature distribution shifts, and scarce fault samples limit model generalization. Therefore, this paper proposes a causal-Transformer-based meta-learning (CTML) method for bearing fault diagnosis in CNC machine tools, comprising three core modules:… More >

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956

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597

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Amal Al-Rasheed¹, Hashim Ali^2,*, Rahim Khan^2,*, Aamir Saeed³
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3419-3436, 2025, DOI:10.32604/cmc.2025.065156 - 23 September 2025
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract In the domain of Electronic Medical Records (EMRs), emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy. This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data. By leveraging the decentralized and immutable nature of blockchain, the proposed approach ensures transparency, integrity, and traceability of EMR transactions, effectively mitigating risks of unauthorized access and data tampering. Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions, eliminating reliance on intermediaries and reducing the potential for… More >

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546

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Xianwei Li^1,2, Bo Wei^3,4, Xiaoying Yang^5,6,*, Amr Tolba⁷, Zijian Zeng⁸, Osama Alfarraj^7,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3437-3455, 2025, DOI:10.32604/cmc.2025.067200 - 23 September 2025
Abstract With the advancements of the next-generation communication networking and Internet of Things (IoT) technologies, a variety of computation-intensive applications (e.g., autonomous driving and face recognition) have emerged. The execution of these IoT applications demands a lot of computing resources. Nevertheless, terminal devices (TDs) usually do not have sufficient computing resources to process these applications. Offloading IoT applications to be processed by mobile edge computing (MEC) servers with more computing resources provides a promising way to address this issue. While a significant number of works have studied task offloading, only a few of them have considered More >

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571

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Fatma S. Alrayes¹, Mohammed Zakariah^2,*, Mohammed K. Alzaylaee³, Syed Umar Amin⁴, Zafar Iqbal Khan⁴
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3457-3484, 2025, DOI:10.32604/cmc.2025.068599 - 23 September 2025
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that… More >

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790

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483

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Yiming Guo, Hongyu Ma^*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3485-3505, 2025, DOI:10.32604/cmc.2025.067590 - 23 September 2025
Abstract In dynamic 5G network environments, user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching. Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device (D2D) cooperative caching, limiting the reduction of transmission latency. To address this issue, this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning. First, a Transformer-based geolocation prediction model is designed, leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.… More >

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