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  • 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

    Bird Species Classification Using Image Background Removal for Data Augmentation

    Yu-Xiang Zhao*, Yi Lee

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

    Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

  • Open Access

    ARTICLE

    Low-Rank Adapter Layers and Bidirectional Gated Feature Fusion for Multimodal Hateful Memes Classification

    Youwei Huang, Han Zhong*, Cheng Cheng, Yijie Peng

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1863-1882, 2025, DOI:10.32604/cmc.2025.064734 - 09 June 2025

    Abstract Hateful meme is a multimodal medium that combines images and texts. The potential hate content of hateful memes has caused serious problems for social media security. The current hateful memes classification task faces significant data scarcity challenges, and direct fine-tuning of large-scale pre-trained models often leads to severe overfitting issues. In addition, it is a challenge to understand the underlying relationship between text and images in the hateful memes. To address these issues, we propose a multimodal hateful memes classification model named LABF, which is based on low-rank adapter layers and bidirectional gated feature fusion. More >

  • Open Access

    ARTICLE

    ONTDAS: An Optimized Noise-Based Traffic Data Augmentation System for Generalizability Improvement of Traffic Classifiers

    Rongwei Yu1, Jie Yin1,*, Jingyi Xiang1, Qiyun Shao2, Lina Wang1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 365-391, 2025, DOI:10.32604/cmc.2025.064438 - 09 June 2025

    Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >

  • Open Access

    ARTICLE

    Multimodal Convolutional Mixer for Mild Cognitive Impairment Detection

    Ovidijus Grigas, Robertas Damaševičius*, Rytis Maskeliūnas

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1805-1838, 2025, DOI:10.32604/cmc.2025.064354 - 09 June 2025

    Abstract Brain imaging is important in detecting Mild Cognitive Impairment (MCI) and related dementias. Magnetic Resonance Imaging (MRI) provides structural insights, while Positron Emission Tomography (PET) evaluates metabolic activity, aiding in the identification of dementia-related pathologies. This study integrates multiple data modalities—T1-weighted MRI, Pittsburgh Compound B (PiB) PET scans, cognitive assessments such as Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and Functional Activities Questionnaire (FAQ), blood pressure parameters, and demographic data—to improve MCI detection. The proposed improved Convolutional Mixer architecture, incorporating B-cos modules, multi-head self-attention, and a custom classifier, achieves a classification accuracy of 96.3% More >

  • Open Access

    ARTICLE

    A Deep Learning Approach to Classification of Diseases in Date Palm Leaves

    Sameera V Mohd Sagheer1, Orwel P V2, P M Ameer3, Amal BaQais4, Shaeen Kalathil5,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1329-1349, 2025, DOI:10.32604/cmc.2025.063961 - 09 June 2025

    Abstract The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods. Conventional approaches rely on visual examination by experts to detect infected palm leaves, which is time intensive and susceptible to mistakes. This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability. The system leverages pretrained convolutional neural network architectures (InceptionV3, DenseNet, and MobileNet) to extract and examine leaf characteristics for classification purposes. A publicly accessible dataset comprising multiple… More >

  • Open Access

    ARTICLE

    Multi-Label Machine Learning Classification of Cardiovascular Diseases

    Chih-Ta Yen1,*, Jung-Ren Wong2, Chia-Hsang Chang2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 347-363, 2025, DOI:10.32604/cmc.2025.063389 - 09 June 2025

    Abstract In its 2023 global health statistics, the World Health Organization noted that noncommunicable diseases (NCDs) remain the leading cause of disease burden worldwide, with cardiovascular diseases (CVDs) resulting in more deaths than the three other major NCDs combined. In this study, we developed a method that can comprehensively detect which CVDs are present in a patient. Specifically, we propose a multi-label classification method that utilizes photoplethysmography (PPG) signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions: hypertension, diabetes, cerebral infarction, and cerebrovascular disease. Our approach to multi-disease classification… 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

    Detection and Classification of Fig Plant Leaf Diseases Using Convolution Neural Network

    Rahim Khan1, Ihsan Rabbi1, Umar Farooq1, Jawad Khan2,*, Fahad Alturise3,*

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 827-842, 2025, DOI:10.32604/cmc.2025.063303 - 09 June 2025

    Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >

  • Open Access

    ARTICLE

    Salient Features Guided Augmentation for Enhanced Deep Learning Classification in Hematoxylin and Eosin Images

    Tengyue Li1,*, Shuangli Song1, Jiaming Zhou2, Simon Fong2,3, Geyue Li4, Qun Song3, Sabah Mohammed5, Weiwei Lin6, Juntao Gao7

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1711-1730, 2025, DOI:10.32604/cmc.2025.062489 - 09 June 2025

    Abstract Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify… More >

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