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Roberto Caviglia¹, Daniyar Aliaskharov², Alessio Aceti¹, Mila Dalla Preda³, Paola Girdinio², Giovanni Battista Gaggero^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5327-5340, 2025, DOI:10.32604/cmc.2025.068509 - 23 October 2025
（This article belongs to the Special Issue: Cyber Attack Detection in Cyber-Physical Systems)
Abstract Industrial Control Systems (ICS) in Operational Technology (OT) environments face unique cybersecurity challenges due to legacy systems, critical operational needs, and incompatibility with standard IT security practices. To address these challenges, this paper presents the Security Operation and Event Management (SOEM) platform, a software designed to support Security Operations Centers (SOCs) in reaching full visibility of OT environments. SOEM integrates diverse log sources and intrusion detection systems, including logs generated by the control system itself and additional on-the-shelf products, to enhance situational awareness and enable rapid incident response. The pilot project was carried out within More >

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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
（This article belongs to the Special Issue: Fuzzy Logic: Next-Generation Algorithms and Applications)
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 >

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261

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Zengyao Tian^1,2, Li Lv^1,*, Wenchen Deng¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5359-5376, 2025, DOI:10.32604/cmc.2025.069427 - 23 October 2025
（This article belongs to the Special Issue: Multimodal Learning for Big Data)
Abstract Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a… More >

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Sheeba Razzaq^1,*, Majid Iqbal Khan²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5377-5398, 2025, DOI:10.32604/cmc.2025.068539 - 23 October 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Multi-Object Tracking (MOT) represents a fundamental but computationally demanding task in computer vision, with particular challenges arising in occluded and densely populated environments. While contemporary tracking systems have demonstrated considerable progress, persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment. This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features. Proposed framework employs: (1) a Height Modulated and Scale Adaptive Spatial Intersection-over-Union (HMSIoU) metric for improved spatial correspondence estimation across variable object scales and partial occlusions; (2) a feature More >

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303

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Chenquan Gan^1,2,*, Xu Liu¹, Yu Tang², Xianrong Yu³, Qingyi Zhu¹, Deepak Kumar Jain⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5399-5421, 2025, DOI:10.32604/cmc.2025.068126 - 23 October 2025
Abstract Multimodal sentiment analysis aims to understand emotions from text, speech, and video data. However, current methods often overlook the dominant role of text and suffer from feature loss during integration. Given the varying importance of each modality across different contexts, a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process. In response to these critical limitations, we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues. In our model, text is… More >

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181

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Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025
Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

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319

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Muhammad Asif Khan¹, Dildar Hussain², Bhuyan Kaibalya Prasad³, Irfan Ullah⁴, Inayat Khan⁵, Jawad Khan^6,*, Yeong Hyeon Gu^2,*, Pavlos Kefalas⁷
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5451-5468, 2025, DOI:10.32604/cmc.2025.069134 - 23 October 2025
Abstract Dialogue State Tracking (DST) is a critical component of task-oriented spoken dialogue systems (SDS), tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values. Recent advances leverage Large Language Models (LLMs) with prompt-based tuning to improve tracking accuracy and efficiency. However, these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts, without explicitly modeling the complex dependencies between slots and values. In this work, we propose PUGG, a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer… More >

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311

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Shekaina Justin^1,*, Wafaa Saleh², Hind Mohammed Albalawi³, J. Shermina⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5469-5487, 2025, DOI:10.32604/cmc.2025.066888 - 23 October 2025
Abstract Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles, surface radiation balance, weather and climate changes, and vegetation photosynthesis. Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry. This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period. Solar energy is the cheapest form of clean energy, and due to the intermittent nature of the energy, accurate forecasting across multiple timeframes is necessary for… More >

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165

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Carlos Rojas¹, Cristian Bravo¹, Carlos Enrique Montenegro-Marín¹, Rubén González-Crespo^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5489-5507, 2025, DOI:10.32604/cmc.2025.067394 - 23 October 2025
Abstract The increasing prevalence of violent incidents in public spaces has created an urgent need for intelligent surveillance systems capable of detecting dangerous objects in real time. While traditional video surveillance relies on human monitoring, this approach suffers from limitations such as fatigue and delayed response times. This study addresses these challenges by developing an automated detection system using advanced deep learning techniques to enhance public safety. Our approach leverages state-of-the-art convolutional neural networks (CNNs), specifically You Only Look Once version 4 (YOLOv4) and EfficientDet, for real-time object detection. The system was trained on a comprehensive… More >

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200

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Zhiwei Ye^1,2,3, Dingfeng Song¹, Haitao Xie^1,2,3,*, Jixin Zhang^1,2, Wen Zhou^1,2, Mengya Lei^1,2, Xiao Zheng^1,2, Jie Sun¹, Jing Zhou¹, Mengxuan Li¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5509-5530, 2025, DOI:10.32604/cmc.2025.067342 - 23 October 2025
Abstract The Multilayer Perceptron (MLP) is a fundamental neural network model widely applied in various domains, particularly for lightweight image classification, speech recognition, and natural language processing tasks. Despite its widespread success, training MLPs often encounter significant challenges, including susceptibility to local optima, slow convergence rates, and high sensitivity to initial weight configurations. To address these issues, this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer (LOEV-APO), which enhances both global exploration and local exploitation simultaneously. LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling (LHS) with Opposition-Based Learning (OBL), thus… More >

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201

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Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025
（This article belongs to the Special Issue: Advances in Efficient Vision Transformers: Architectures, Optimization, and Applications)
Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >

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Sanaa Lakrouni^*, Slimane Bah, Marouane Sebgui
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5555-5569, 2025, DOI:10.32604/cmc.2025.067654 - 23 October 2025
（This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)
Abstract Federated Learning (FL) has recently emerged as a promising paradigm that enables medical institutions to collaboratively train robust models without centralizing sensitive patient information. Data collected from different institutions represent distinct source domains. Consequently, discrepancies in feature distributions can significantly hinder a model’s generalization to unseen domains. While domain generalization (DG) methods have been proposed to address this challenge, many may compromise data privacy in FL by requiring clients to transmit their local feature representations to the server. Furthermore, existing adversarial training methods, commonly used to align marginal feature distributions, fail to ensure the consistency… More >

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151

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Hongle Li¹, SeongKi Kim^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5571-5594, 2025, DOI:10.32604/cmc.2025.067424 - 23 October 2025
（This article belongs to the Special Issue: Algorithms for Planning and Scheduling Problems)
Abstract Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles, as well as the realism and immersion of virtual environments. However, traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments. This study addresses these limitations through a proposed real-time dynamic multiobjective (RDMO) path-planning algorithm based on an enhanced A* framework. The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real… More >

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258

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Jyun-Jie Wang¹, Yin-Chen Lin¹, Chi-Chun Chen^2,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5595-5624, 2025, DOI:10.32604/cmc.2025.066160 - 23 October 2025
Abstract Digital watermarking must balance imperceptibility, robustness, complexity, and security. To address the challenge of computational efficiency in trellis-based informed embedding, we propose a modified watermarking framework that integrates fuzzy c-means (FCM) clustering into the generation off block codewords for labeling trellis arcs. The system incorporates a parallel trellis structure, controllable embedding parameters, and a novel informed embedding algorithm with reduced complexity. Two types of embedding schemes—memoryless and memory-based—are designed to flexibly trade-off between imperceptibility and robustness. Experimental results demonstrate that the proposed method outperforms existing approaches in bit error rate (BER) and computational complexity under More >

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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. 5625-5642, 2025, DOI:10.32604/cmc.2025.069636 - 23 October 2025
Abstract Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance. This method effectively integrates prior knowledge using pre-labeled data. While semi-supervised fuzzy clustering (SSFC) methods leverage limited labeled data to enhance accuracy, they remain highly susceptible to inappropriate or mislabeled prior knowledge, especially in noisy or overlapping datasets where cluster boundaries are ambiguous. To enhance the effectiveness of clustering algorithms, it is essential to leverage labeled data while ensuring the safety of the previous knowledge. Existing solutions, such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method (TS3FCM),… More >

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177

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Khawaja Tahir Mehmood^1,*, Muhammad Majid Hussain²
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5643-5670, 2025, DOI:10.32604/cmc.2025.068404 - 23 October 2025
Abstract In the modern era of 5th generation (5G) networks, the data generated by User Equipments (UE) has increased significantly, with data file sizes varying from modest sensor logs to enormous multimedia files. In modern telecommunications networks, the need for high-end security and efficient management of these large data files is a great challenge for network designers. The proposed model provides the efficient real-time virtual data storage of UE data files (light and heavy) using an object storage system MinIO having inbuilt Software Development Kits (SDKs) that are compatible with Amazon (S3) Application Program Interface (API)… More >

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263

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Diyar Wirya Omar Ameenulhakeem^*, Osman Nuri Uçan
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5671-5702, 2025, DOI:10.32604/cmc.2025.070422 - 23 October 2025
（This article belongs to the Special Issue: Development and Application of Deep Learning based Object Detection)
Abstract Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in… More >

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Sahbi Boubaker^1,2,*, Salim Hadj Said³, Souad Kamel¹, Habib Dimassi³
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5703-5721, 2025, DOI:10.32604/cmc.2025.069047 - 23 October 2025
Abstract Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear, time-varying dynamics of human-robot interaction, limited sensor availability, and unknown external disturbances. This study proposes a novel control strategy that combines flatness-based control with two cascaded observers: a high-gain observer to estimate unmeasured joint velocities, and a nonlinear disturbance observer to reconstruct external torque disturbances in real time. These estimates are integrated into the control law to enable robust, state-feedback-based trajectory tracking. The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations, reflecting realistic rehabilitation conditions. Results confirm More >

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184

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Hari Mohan Rai^1,#, Chandra Mukherjee^2,#, Joon Yoo¹, Hanaa A. Abdallah³, Saurabh Agarwal^4,*, Wooguil Pak^4,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5723-5745, 2025, DOI:10.32604/cmc.2025.070391 - 23 October 2025
（This article belongs to the Special Issue: Recent Advancements in Machine Learning and Data Analysis for Disease Detection)
Abstract A hybrid Compressed Sensing and Primal-Dual Wavelet (CSP-PDW) technique is proposed for the compression and reconstruction of ECG signals. The compression and reconstruction algorithms are implemented using four key concepts: Sparsifying Basis, Restricted Isometry Principle, Gaussian Random Matrix, and Convex Minimization. In addition to the conventional compression sensing reconstruction approach, wavelet-based processing is employed to enhance reconstruction efficiency. A mathematical model of the proposed algorithm is derived analytically to obtain the essential parameters of compression sensing, including the sparsifying basis, measurement matrix size, and number of iterations required for reconstructing the original signal and determining More >

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211

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Moshiur Rahman Tonmoy¹, Md. Mithun Hossain¹, Mejdl Safran^2,*, Sultan Alfarhood², Dunren Che³, M. F. Mridha⁴
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5747-5763, 2025, DOI:10.32604/cmc.2025.067589 - 23 October 2025
Abstract Automated recognition of violent activities from videos is vital for public safety, but often raises significant privacy concerns due to the sensitive nature of the footage. Moreover, resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices. With this motivation, this study aims to investigate an effective violent activity classifier while minimizing computational complexity, attaining competitive performance, and mitigating user data privacy concerns. We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition. We utilize a two-stream formation of 3D depthwise separable convolution coupled More >

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Fahd N. Al-Wesabi^1,*, Hamad Almansour², Huda G. Iskandar^3,4, Ishfaq Yaseen⁵
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5765-5787, 2025, DOI:10.32604/cmc.2025.067571 - 23 October 2025
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired (BVI) individuals in a limited timeframe in the event of emergencies, particularly in enclosed areas. Fire detection becomes crucial as it directly impacts human safety and the environment. While modern technology requires precise techniques for early detection to prevent damage and loss, few research has focused on artificial intelligence (AI)-based early fire alert systems for BVI individuals in indoor settings. To prevent such fire incidents, it is crucial to identify fires accurately and promptly, and alert BVI personnel… More >

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201

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Ahmed El Maloufy, Mohamed Amine Tahiri, Ahmed Bencherqui, Hicham Karmouni, Mhamed Sayyouri^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5789-5822, 2025, DOI:10.32604/cmc.2025.068885 - 23 October 2025
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract In the current digital context, safeguarding copyright is a major issue, particularly for architectural drawings produced by students. These works are frequently the result of innovative academic thinking combining creativity and technical precision. They are particularly vulnerable to the risk of illegal reproduction when disseminated in digital format. This research suggests, for the first time, an innovative approach to copyright protection by embedding a double digital watermark to address this challenge. The solution relies on a synergistic fusion of several sophisticated methods: Krawtchouk Optimized Octonion Moments (OKOM), Quaternion Singular Value Decomposition (QSVD), and Discrete Waveform… More >

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158

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Dan Wang¹, Zhoubin Li¹, Yuze Xia^1,2,*, Zhenhua Yu^1,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5823-5845, 2025, DOI:10.32604/cmc.2025.071656 - 23 October 2025
（This article belongs to the Special Issue: Sentiment Analysis for Social Media Data: Lexicon-Based and Large Language Model Approaches)
Abstract Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs. However, existing models suffer from weak aspect-image alignment, modality imbalance dominated by textual signals, and limited reasoning for implicit or ambiguous sentiments requiring external knowledge. To address these issues, we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network (GLAMSNet). First of all, an input encoding module is employed to construct modality-specific and aspect-aware representations. Subsequently, we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment. Building upon these components, More >

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227

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Tiren Huang, Chong Luo, Xu Li^*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5847-5865, 2025, DOI:10.32604/cmc.2025.067986 - 23 October 2025
Abstract Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection, treatment planning and therapeutic evaluation. However, the pancreas’s small size, irregular morphology, and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task. To address these challenges, we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images. First, a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network, enhancing pancreatic feature extraction and improving localization accuracy.… More >

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