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  • Open Access

    ARTICLE

    A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

    Federica Uccello1,2, Marek Pawlicki3,4, Salvatore D'Antonio1, Rafał Kozik3,4, Michał Choraś3,4,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1607-1621, 2025, DOI:10.32604/cmc.2025.062801 - 16 April 2025

    Abstract The growing sophistication of cyberthreats, among others the Distributed Denial of Service attacks, has exposed limitations in traditional rule-based Security Information and Event Management systems. While machine learning–based intrusion detection systems can capture complex network behaviours, their “black-box” nature often limits trust and actionable insight for security operators. This study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules, thereby enhancing the detection of Distributed Denial of Service (DDoS) attacks. The proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules More >

  • Open Access

    ARTICLE

    Enhancing Educational Materials: Integrating Emojis and AI Models into Learning Management Systems

    Shaya A. Alshaya*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3075-3095, 2025, DOI:10.32604/cmc.2025.062360 - 16 April 2025

    Abstract The integration of visual elements, such as emojis, into educational content represents a promising approach to enhancing student engagement and comprehension. However, existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation. This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes (DECOC), Long Short-Term Memory (LSTM) networks, and Multi-Layer Deep Neural Networks (ML-DNN) to identify optimal emoji placements within computer science course materials. The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings… More >

  • Open Access

    ARTICLE

    SNN-IoMT: A Novel AI-Driven Model for Intrusion Detection in Internet of Medical Things

    Mourad Benmalek1,*,#,*, Abdessamed Seddiki2,#, Kamel-Dine Haouam1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1157-1184, 2025, DOI:10.32604/cmes.2025.062841 - 11 April 2025

    Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >

  • Open Access

    ARTICLE

    Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

    Saad Alahmari1,*, Abdulwhab Alkharashi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 849-873, 2025, DOI:10.32604/cmes.2025.062549 - 11 April 2025

    Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >

  • Open Access

    ARTICLE

    An Enhanced Task Migration Technique Based on Convolutional Neural Network in Machine Learning Framework

    Hamayun Khan1,*, Muhammad Atif Imtiaz2, Hira Siddique3, Muhammad Tausif Afzal Rana4, Arshad Ali5, Muhammad Zeeshan Baig6, Saif ur Rehman7, Yazed Alsaawy5

    Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

    Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

  • Open Access

    REVIEW

    Artificial Intelligence Revolutionising the Automotive Sector: A Comprehensive Review of Current Insights, Challenges, and Future Scope

    Md Naeem Hossain1, Md. Abdur Rahim2, Md Mustafizur Rahman1,3,*, Devarajan Ramasamy1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3643-3692, 2025, DOI:10.32604/cmc.2025.061749 - 06 March 2025

    Abstract The automotive sector is crucial in modern society, facilitating essential transportation needs across personal, commercial, and logistical domains while significantly contributing to national economic development and employment generation. The transformative impact of Artificial Intelligence (AI) has revolutionised multiple facets of the automotive industry, encompassing intelligent manufacturing processes, diagnostic systems, control mechanisms, supply chain operations, customer service platforms, and traffic management solutions. While extensive research exists on the above aspects of AI applications in automotive contexts, there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research. This review introduces a… More >

  • Open Access

    ARTICLE

    An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

    Isha Kiran1, Shahzad Ali2,3, Sajawal ur Rehman Khan4,5, Musaed Alhussein6, Sheraz Aslam7,8,*, Khursheed Aurangzeb6,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

    Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

  • Open Access

    ARTICLE

    Semantic Malware Classification Using Artificial Intelligence Techniques

    Eliel Martins1, Javier Bermejo Higuera2,*, Ricardo Sant’Ana1, Juan Ramón Bermejo Higuera2, Juan Antonio Sicilia Montalvo2, Diego Piedrahita Castillo3

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

    Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >

  • Open Access

    ARTICLE

    Evaluating Effect of Magnetic Field on Nusselt Number and Friction Factor of Fe3O4-TiO2/Water Nanofluids in Heat-Sink Using Artificial Intelligence Techniques

    L. S. Sundar*, Sérgio M. O. Tavares, António M. B. Pereira, Antonio C. M. Sousa

    Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 131-162, 2025, DOI:10.32604/fhmt.2025.055854 - 26 February 2025

    Abstract The experimental analysis takes too much time-consuming process and requires considerable effort, while, the Artificial Neural Network (ANN) algorithms are simple, affordable, and fast, and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters. This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient, Nusselt number, and friction factor of water-based Fe3O4-TiO2 magnetic hybrid nanofluids in a mini heat sink under magnetic fields. The data considered for the ANN network is at different Reynolds numbers (239 to 1874),… More >

  • Open Access

    EDITORIAL

    Guest Editorial Special Issue on Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems

    Jipu Li1, Haidong Shao2,*, Yun Kong3, Zhuyun Chen4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3609-3613, 2025, DOI:10.32604/cmc.2024.062183 - 17 February 2025

    Abstract This article has no abstract. More >

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