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 >

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 >

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 >

Rongwei Yu¹, Jie Yin^1,*, 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 >

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 >

Sameera V Mohd Sagheer¹, Orwel P V², P M Ameer³, Amal BaQais⁴, Shaeen Kalathil^5,*

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 >

Chih-Ta Yen^1,*, 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 >

Ghada Atteia^1,*, 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 >

Rahim Khan¹, Ihsan Rabbi¹, Umar Farooq¹, Jawad Khan^2,*, Fahad Alturise^3,*

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 >

Tengyue Li^1,*, Shuangli Song¹, Jiaming Zhou², Simon Fong^2,3, 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 >