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  • Open Access

    ARTICLE

    An Explainable Autoencoder-Based Feature Extraction Combined with CNN-LSTM-PSO Model for Improved Predictive Maintenance

    Ishaani Priyadarshini*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 635-659, 2025, DOI:10.32604/cmc.2025.061062 - 26 March 2025

    Abstract Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions. This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations, such as transparency, fairness, and explainability, in artificial intelligence driven decision-making. The framework employs an Autoencoder for feature reduction, a Convolutional Neural Network for pattern recognition, and a Long Short-Term Memory network for temporal analysis.… More >

  • Open Access

    ARTICLE

    SESDP: A Sentiment Analysis-Driven Approach for Enhancing Software Product Security by Identifying Defects through Social Media Reviews

    Farah Mohammad1,2,*, Saad Al-Ahmadi3, Jalal Al-Muhtadi1,3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1327-1345, 2025, DOI:10.32604/cmc.2025.060228 - 26 March 2025

    Abstract Software defect prediction is a critical component in maintaining software quality, enabling early identification and resolution of issues that could lead to system failures and significant financial losses. With the increasing reliance on user-generated content, social media reviews have emerged as a valuable source of real-time feedback, offering insights into potential software defects that traditional testing methods may overlook. However, existing models face challenges like handling imbalanced data, high computational complexity, and insufficient integration of contextual information from these reviews. To overcome these limitations, this paper introduces the SESDP (Sentiment Analysis-Based Early Software Defect Prediction)… More >

  • Open Access

    ARTICLE

    LLE-Fuse: Lightweight Infrared and Visible Light Image Fusion Based on Low-Light Image Enhancement

    Song Qian, Guzailinuer Yiming, Ping Li, Junfei Yang, Yan Xue, Shuping Zhang*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4069-4091, 2025, DOI:10.32604/cmc.2025.059931 - 06 March 2025

    Abstract Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information. However, in low-light scenarios, the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene. At this time, relying solely on the target saliency information provided by infrared images is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. The method is based on the… More >

  • Open Access

    ARTICLE

    A Weakly Supervised Semantic Segmentation Method Based on Improved Conformer

    Xueli Shen, Meng Wang*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025

    Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

  • Open Access

    ARTICLE

    Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

    Asma Aldrees1, Hong Min2,*, Ashit Kumar Dutta3, Yousef Ibrahim Daradkeh4, Mohd Anjum5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2487-2511, 2025, DOI:10.32604/cmes.2025.061103 - 03 March 2025

    Abstract Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus… More >

  • Open Access

    ARTICLE

    PD-YOLO: Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

    Yicong Yu1,2, Kaixin Lin1, Jiajun Hong1, Rong-Guei Tsai3,*, Yuanzhi Huang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 913-928, 2025, DOI:10.32604/cmc.2024.058467 - 03 January 2025

    Abstract In recent years, the number of patients with colon disease has increased significantly. Colon polyps are the precursor lesions of colon cancer. If not diagnosed in time, they can easily develop into colon cancer, posing a serious threat to patients’ lives and health. A colonoscopy is an important means of detecting colon polyps. However, in polyp imaging, due to the large differences and diverse types of polyps in size, shape, color, etc., traditional detection methods face the problem of high false positive rates, which creates problems for doctors during the diagnosis process. In order to… More >

  • Open Access

    ARTICLE

    Prairie Araneida Optimization Based Fused CNN Model for Intrusion Detection

    Nishit Patil, Shubhalaxmi Joshi*

    Computer Systems Science and Engineering, Vol.49, pp. 49-77, 2025, DOI:10.32604/csse.2024.057702 - 03 January 2025

    Abstract Intrusion detection (ID) is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions. This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks, generating many false alarms, and their inability and difficulty to adapt themselves with time when it comes to threats, hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model (PAO-CNN) for intrusion detection. The fused CNN (Convolutional Neural Netowrk) is a remarkable development since it combines statistical… More >

  • Open Access

    REVIEW

    Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models

    Shanmugasundaram Hariharan1, D. Anandan2, Murugaperumal Krishnamoorthy3, Vinay Kukreja4, Nitin Goyal5, Shih-Yu Chen6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 91-122, 2025, DOI:10.32604/cmes.2024.057214 - 17 December 2024

    Abstract Liver cancer remains a leading cause of mortality worldwide, and precise diagnostic tools are essential for effective treatment planning. Liver Tumors (LTs) vary significantly in size, shape, and location, and can present with tissues of similar intensities, making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging. This review examines recent advancements in Liver Segmentation (LS) and Tumor Segmentation (TS) algorithms, highlighting their strengths and limitations regarding precision, automation, and resilience. Performance metrics are utilized to assess key detection algorithms and analytical methods, emphasizing their effectiveness and relevance in clinical contexts. The More >

  • Open Access

    ARTICLE

    Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments

    Yahia Said1,*, Yahya Alassaf2, Taoufik Saidani3, Refka Ghodhbani3, Olfa Ben Rhaiem4, Ali Ahmad Alalawi1

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

    Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

  • Open Access

    ARTICLE

    Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

    Nahed Tawfik1,*, Heba M. Emara2, Walid El-Shafai3, Naglaa F. Soliman4, Abeer D. Algarni4, Fathi E. Abd El-Samie4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404 - 15 October 2024

    Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

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