Mustafa Lateef Fadhil Jumaili^1,2, Emrullah Sonuç^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2947-2969, 2025, DOI:10.32604/cmc.2025.062719 - 16 April 2025

Abstract Alzheimer’s disease (AD) is a significant challenge in modern healthcare, with early detection and accurate staging remaining critical priorities for effective intervention. While Deep Learning (DL) approaches have shown promise in AD diagnosis, existing methods often struggle with the issues of precision, interpretability, and class imbalance. This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence (XAI) techniques, in particular attention mechanisms, Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), to improve both model interpretability and feature selection. The study evaluates four different DL architectures (ResMLP, VGG16, Xception, More >

Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

Poonam Sharma¹, Bhisham Sharma^2,*, Dhirendra Prasad Yadav³, Surbhi Bhatia Khan^4,5,6,*, Ahlam Almusharraf⁷

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3227-3245, 2025, DOI:10.32604/cmc.2025.062376 - 16 April 2025

Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… More >

Omar Alqahtani, Mohamed Ghouse^*, Asfia Sabahath, Omer Bin Hussain, Arshiya Begum

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2221-2244, 2025, DOI:10.32604/cmc.2025.061977 - 16 April 2025

Abstract This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer (ViT) architectures and a dynamic multi-loss function. The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features, while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance. Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets, yielding notable improvements. Specifically, on the ISIC-2018 dataset, our method achieves an F1-Score improvement of +4.84% compared to the standard ViT, with a precision increase of +5.46% More >

Xiangbin Wang¹, Qingjun Yuan^1,*, Weina Niu², Qianwei Meng¹, Yongjuan Wang¹, Chunxiang Gu¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2267-2285, 2025, DOI:10.32604/cmc.2025.061767 - 16 April 2025

Abstract With the rise of encrypted traffic, traditional network analysis methods have become less effective, leading to a shift towards deep learning-based approaches. Among these, multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic, improving classification accuracy. However, existing research predominantly relies on late fusion techniques, which hinder the full utilization of deep features within the data. To address this limitation, we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction. Specifically, our approach performs real-time fusion of modalities More >

Hongyang Wang^1,2, Yichen Shi³, Jun Feng^1,2,*, Zitong Yu⁴, Zhuofu Tao⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3097-3112, 2025, DOI:10.32604/cmc.2025.061019 - 16 April 2025

Abstract Face Presentation Attack Detection (fPAD) plays a vital role in securing face recognition systems against various presentation attacks. While supervised learning-based methods demonstrate effectiveness, they are prone to overfitting to known attack types and struggle to generalize to novel attack scenarios. Recent studies have explored formulating fPAD as an anomaly detection problem or one-class classification task, enabling the training of generalized models for unknown attack detection. However, conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown attacks. To address this challenge, we propose a novel framework focusing on… More >

Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Jahanzaib Latif ¹, Ahsan Wajahat¹, Alishba Tahir², Anas Bilal^3,*, Mohammed Zakariah⁴, Abeer Alnuaim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 539-567, 2025, DOI:10.32604/cmes.2025.062301 - 11 April 2025

Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >

Saleh Albahli^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1095-1128, 2025, DOI:10.32604/cmes.2025.058821 - 11 April 2025

Abstract Predicting hospital readmission and length of stay (LOS) for diabetic patients is critical for improving healthcare quality, optimizing resource utilization, and reducing costs. This study leverages machine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade. A comprehensive preprocessing pipeline, including feature selection, data transformation, and class balancing, was implemented to ensure data quality and enhance model performance. Exploratory analysis revealed key patterns, such as the influence of age and the number of diagnoses on readmission rates, guiding the More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Tanveer Zia^2,3, Muhammad Hamza Faheem², Muhammad Imran⁴, Iftikhar Ahmad⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1009-1027, 2025, DOI:10.32604/cmes.2025.058352 - 11 April 2025

Abstract In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In More >