Atta Rahman^1,*, Mustafa Youldash², Ghaida Alshammari², Abrar Sebiany², Joury Alzayat², Manar Alsayed², Mona Alqahtani², Noor Aljishi²

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4561-4576, 2024, DOI:10.32604/cmc.2024.055106 - 12 September 2024

Abstract Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support… More >

Mahmoud E. Farfoura^1,*, Ahmad Alkhatib¹, Deema Mohammed Alsekait^2,*, Mohammad Alshinwan^3,7, Sahar A. El-Rahman⁴, Didi Rosiyadi⁵, Diaa Salama AbdElminaam^6,7

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4833-4857, 2024, DOI:10.32604/cmc.2024.054849 - 12 September 2024

Abstract The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature… More >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >

Zixuan Wu¹, Ye Wang^1,*, Lifeng Shen², Feng Hu¹, Hong Yu^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581 - 12 September 2024

Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >

Congcong Ma^1,2, Jiaqi Mi¹, Wanlin Gao^1,2, Sha Tao^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4243-4261, 2024, DOI:10.32604/cmc.2024.054506 - 12 September 2024

Abstract Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes. This task is prevalent in practical scenarios such as industrial fault diagnosis, network intrusion detection, cancer detection, etc. In imbalanced classification tasks, the focus is typically on achieving high recognition accuracy for the minority class. However, due to the challenges presented by imbalanced multi-class datasets, such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries, existing methods often do not perform well in multi-class imbalanced data… More >

Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024

Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

Xiaoyu Liu, Yong Hu^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938 - 12 September 2024

Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

Zhaojie Sun¹, Linlin Xing^1,*, Longbo Zhang¹, Hongzhen Cai², Maozu Guo³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588 - 12 September 2024

Abstract Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we… More >

Dennies Tsietso^1,*, Abid Yahya¹, Ravi Samikannu¹, Basit Qureshi², Muhammad Babar^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4749-4765, 2024, DOI:10.32604/cmc.2024.052793 - 12 September 2024

Abstract Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide. Various Computer-Aided Diagnosis (CAD) tools, based on breast thermograms, have been developed for early detection of this disease. However, accurately segmenting the Region of Interest (ROI) from thermograms remains challenging. This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottom boundary using a second-degree polynomial. The proposed method demonstrated high efficacy, achieving an impressive Jaccard coefficient of 86% and a Dice… More >

Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599 - 12 September 2024

Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >