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  • Open Access

    ARTICLE

    Deep Learning Network Intrusion Detection Based on MI-XGBoost Feature Selection

    Manzheng Yuan1,2, Kai Yang2,*

    Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

    Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

  • Open Access

    ARTICLE

    AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm

    Hui Xu, Wei Huang*, Longtan Bai

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3053-3073, 2025, DOI:10.32604/cmc.2025.064930 - 03 July 2025

    Abstract With the birth of Software-Defined Networking (SDN), integration of both SDN and traditional architectures becomes the development trend of computer networks. Network intrusion detection faces challenges in dealing with complex attacks in SDN environments, thus to address the network security issues from the viewpoint of Artificial Intelligence (AI), this paper introduces the Crayfish Optimization Algorithm (COA) to the field of intrusion detection for both SDN and traditional network architectures, and based on the characteristics of the original COA, an Improved Crayfish Optimization Algorithm (ICOA) is proposed by integrating strategies of elite reverse learning, Levy flight,… More >

  • Open Access

    REVIEW

    Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms: An Extensive Review

    Siti Ramadhani1,2, Lestari Handayani2, Theam Foo Ng3, Sumayyah Dzulkifly1, Roziana Ariffin4,5, Haldi Budiman6, Shir Li Wang1,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2711-2765, 2025, DOI:10.32604/cmes.2025.062709 - 30 June 2025

    Abstract In recent years, feature selection (FS) optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification. This work reviews FS and classification methods that utilize evolutionary algorithms (EAs) for gene expression profiles in cancer or medical applications based on research motivations, challenges, and recommendations. Relevant studies were retrieved from four major academic databases–IEEE, Scopus, Springer, and ScienceDirect–using the keywords ‘cancer classification’, ‘optimization’, ‘FS’, and ‘gene expression profile’. A total of 67 papers were finally selected with key advancements identified as follows: (1) The majority of papers… More > Graphic Abstract

    Feature Selection Optimisation for Cancer Classification Based on Evolutionary Algorithms: An Extensive Review

  • Open Access

    ARTICLE

    Optimizing Feature Selection by Enhancing Particle Swarm Optimization with Orthogonal Initialization and Crossover Operator

    Indu Bala*, Wathsala Karunarathne, Lewis Mitchell

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 727-744, 2025, DOI:10.32604/cmc.2025.065706 - 09 June 2025

    Abstract Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets, significantly increasing data complexity and dimensionality in medical diagnostics. Efficient feature selection methods are critical for improving diagnostic accuracy, reducing computational costs, and enhancing the interpretability of predictive models. Particle Swarm Optimization (PSO), a widely used metaheuristic inspired by swarm intelligence, has shown considerable promise in feature selection tasks. However, conventional PSO often suffers from premature convergence and limited exploration capabilities, particularly in high-dimensional spaces. To overcome these limitations, this study proposes an enhanced PSO framework incorporating Orthogonal… More >

  • Open Access

    ARTICLE

    FSFS: A Novel Statistical Approach for Fair and Trustworthy Impactful Feature Selection in Artificial Intelligence Models

    Ali Hamid Farea1,*, Iman Askerzade1,2, Omar H. Alhazmi3, Savaş Takan4

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1457-1484, 2025, DOI:10.32604/cmc.2025.064872 - 09 June 2025

    Abstract Feature selection (FS) is a pivotal pre-processing step in developing data-driven models, influencing reliability, performance and optimization. Although existing FS techniques can yield high-performance metrics for certain models, they do not invariably guarantee the extraction of the most critical or impactful features. Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features. However, the challenge of discerning the most relevant and influential features persists, particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning and Optimized Feature Selection for Oil Spill Detection in Satellite Images

    Ghada Atteia1,*, Mohammed Dabboor2, Konstantinos Karantzalos3, Maali Alabdulhafith1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

    Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

  • Open Access

    ARTICLE

    Optimized Feature Selection for Leukemia Diagnosis Using Frog-Snake Optimization and Deep Learning Integration

    Reza Goodarzi1, Ali Jalali1,*, Omid Hashemi Pour Tafreshi1, Jalil Mazloum1, Peyman Beygi2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 653-679, 2025, DOI:10.32604/cmc.2025.062803 - 09 June 2025

    Abstract Acute lymphoblastic leukemia (ALL) is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis. One of the most prioritized tasks is the early and correct diagnosis of this malignancy; however, manual observation of the blood smear is very time-consuming and requires labor and expertise. Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging. Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2. This approach fuses deep and spatial features to… More >

  • Open Access

    ARTICLE

    Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

    Karim Gasmi1, Olfa Hrizi1,*, Najib Ben Aoun2,3, Ibrahim Alrashdi1, Ali Alqazzaz4, Omer Hamid5, Mohamed O. Altaieb1, Alameen E. M. Abdalrahman1, Lassaad Ben Ammar6, Manel Mrabet6, Omrane Necibi1

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025

    Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

  • Open Access

    ARTICLE

    Research on Vehicle Safety Based on Multi-Sensor Feature Fusion for Autonomous Driving Task

    Yang Su1,*, Xianrang Shi1, Tinglun Song2

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5831-5848, 2025, DOI:10.32604/cmc.2025.064036 - 19 May 2025

    Abstract Ensuring that autonomous vehicles maintain high precision and rapid response capabilities in complex and dynamic driving environments is a critical challenge in the field of autonomous driving. This study aims to enhance the learning efficiency of multi-sensor feature fusion in autonomous driving tasks, thereby improving the safety and responsiveness of the system. To achieve this goal, we propose an innovative multi-sensor feature fusion model that integrates three distinct modalities: visual, radar, and lidar data. The model optimizes the feature fusion process through the introduction of two novel mechanisms: Sparse Channel Pooling (SCP) and Residual Triplet-Attention… More >

  • Open Access

    ARTICLE

    Metaheuristic-Driven Abnormal Traffic Detection Model for SDN Based on Improved Tyrannosaurus Optimization Algorithm

    Hui Xu, Jiahui Chen*, Zhonghao Hu

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025

    Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

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