Noor Ayesha¹, Muhammad Mujahid², Abeer Rashad Mirdad², Faten S. Alamri^3,*, Amjad R. Khan²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

Hemn Barzan Abdalla^1,*, Yulia Kumar², Jose Marchena², Stephany Guzman², Ardalan Awlla³, Mehdi Gheisari⁴, Maryam Cheraghy¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1073-1099, 2025, DOI:10.32604/cmc.2025.063551 - 09 June 2025

Abstract Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence (AI), and it will be interesting to see how we overcome this obstacle in the future, but for now, “THE SHOW MUST GO ON!!!” As AI spreads and transforms more industries, the lack of data is a significant obstacle: the best methods for teaching machines how real-world processes work. This paper explores the considerable implications of data scarcity for the AI industry, which threatens to restrict its growth and potential, and proposes plausible solutions and perspectives. In addition, this article focuses highly on… More >

Duo Cui^*, Qiusheng Wang

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 417-431, 2025, DOI:10.32604/cmc.2025.063507 - 09 June 2025

Abstract In the field of autonomous driving, the task of reliably and accurately detecting lane markings poses a significant and complex challenge. This study presents a lane recognition model that employs an encoder-decoder architecture. In the encoder section, we develop a feature extraction framework that operates concurrently with attention mechanisms and convolutional layers. We propose a spatial axis attention framework that integrates spatial information transfer regulated by gating units. This architecture places a strong emphasis on long-range dependencies and the spatial distribution of images. Furthermore, we incorporate multi-scale convolutional layers to extract intricate features from the More >

Kezhen Xie^1,2, Syed Mohd Zahid Syed Zainal Ariffin^1,*, Muhammad Izzad Ramli¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 997-1011, 2025, DOI:10.32604/cmc.2025.063469 - 09 June 2025

Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing 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 >

Mingen Zhong¹, Kaibo Yang^1,*, Ziji Xiao¹, Jiawei Tan², Kang Fan², Zhiying Deng¹, Mengli Zhou¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1055-1071, 2025, DOI:10.32604/cmc.2025.063383 - 09 June 2025

Abstract With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video… 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 >

Adarsh Anand¹, Divya¹, Deepti Aggrawal², Omar H. Alhazmi^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1529-1544, 2025, DOI:10.32604/cmc.2025.063361 - 09 June 2025

Abstract Software systems are vulnerable to security breaches as they expand in complexity and functionality. The confidentiality, integrity, and availability of data are gravely threatened by flaws in a system’s design, implementation, or configuration. To guarantee the durability & robustness of the software, vulnerability identification and fixation have become crucial areas of focus for developers, cybersecurity experts and industries. This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection. To uniquely model these processes, the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning… More >

Mahmood Ul Haq¹, Muhammad Athar Javed Sethi¹, Sadique Ahmad², Naveed Ahmad³, Muhammad Shahid Anwar^4,*, Alpamis Kutlimuratov⁵

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1-24, 2025, DOI:10.32604/cmc.2025.063341 - 09 June 2025

Abstract Face recognition has emerged as one of the most prominent applications of image analysis and understanding, gaining considerable attention in recent years. This growing interest is driven by two key factors: its extensive applications in law enforcement and the commercial domain, and the rapid advancement of practical technologies. Despite the significant advancements, modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions, occlusion, and diverse facial postures. In such scenarios, human perception is still well above the capabilities of present technology. Using the systematic mapping study, this paper presents an in-depth review More >

Yuqi Chen^1,2, Guangjun Liang^1,2,3,*, Qun Wang^1,2,3

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1145-1167, 2025, DOI:10.32604/cmc.2025.063319 - 09 June 2025

Abstract Structured Query Language (SQL) injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm. Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks. This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection. Features are extracted through content matching, effectively avoiding the loss of valid information, and an improved deep learning model is employed to enhance the detection effect of SQL injections. Considering that grammar More >