Jixin Wu^1,2, Mingtao Zhou^2,3, Di Wu^2,3, Wenqi Ren⁴, Jiatian Mei^2,3, Shu Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072964 - 12 January 2026

Abstract End-to-end Temporal Action Detection (TAD) has achieved remarkable progress in recent years, driven by innovations in model architectures and the emergence of Video Foundation Models (VFMs). However, existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs, which become particularly pronounced when processing long video sequences. Moreover, the need for precise temporal boundary annotations makes data labeling extremely expensive. In low-resource settings where annotated samples are scarce, direct fine-tuning tends to cause overfitting. To address these challenges, we introduce Dynamic Low-Rank Adapter (DyLoRA), a lightweight fine-tuning framework tailored specifically… More >

Himadri Nath Saha¹, Dipanwita Chakraborty Bhattacharya^2,*, Sancharita Dutta³, Arnab Bera³, Srutorshi Basuray⁴, Satyasaran Changdar⁵, Saptarshi Banerjee⁶, Jon Turdiev⁷

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-56, 2026, DOI:10.32604/cmc.2025.070507 - 09 December 2025

Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >

Soumia Zertal^1,2,*, Asma Saighi^1,2, Sofia Kouah^1,2, Souham Meshoul^3,*, Zakaria Laboudi^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3737-3782, 2025, DOI:10.32604/cmes.2025.068558 - 30 September 2025

Abstract Cardiovascular diseases (CVDs) continue to present a leading cause of mortality worldwide, emphasizing the importance of early and accurate prediction. Electrocardiogram (ECG) signals, central to cardiac monitoring, have increasingly been integrated with Deep Learning (DL) for real-time prediction of CVDs. However, DL models are prone to performance degradation due to concept drift and to catastrophic forgetting. To address this issue, we propose a real-time CVDs prediction approach, referred to as ADWIN-GFR that combines Convolutional Neural Network (CNN) layers, for spatial feature extraction, with Gated Recurrent Units (GRU), for temporal modeling, alongside adaptive drift detection and… More > Graphic Abstract

Osama M. Alshehri¹, Ahmad Shaf^2,*, Muhammad Irfan^3,*, Mohammed M. Jalal⁴, Malik A. Altayar⁴, Mohammed H. Abu-Alghayth⁵, Humood Al Shmrany⁶, Tariq Ali⁷, Toufique A. Soomro⁸, Ali G. Alkhathami⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025

Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

Jong-Yih Kuo¹, Ping-Feng Wang^2,*, Ti-Feng Hsieh^1,*, Cheng-Hsuan Kuo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3133-3166, 2025, DOI:10.32604/cmes.2025.064104 - 30 June 2025

Abstract In recent years, cyber threats have escalated across diverse sectors, with cybercrime syndicates increasingly exploiting system vulnerabilities. Traditional passive defense mechanisms have proven insufficient, particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets. According to Trend Micro, there has been a substantial increase in Linux-targeted malware, with ransomware attacks on Linux surpassing those on macOS. This alarming trend underscores the need for detection strategies specifically designed for Linux environments. To address this challenge, this study proposes a comprehensive malware detection framework tailored for Linux systems, integrating dynamic behavioral analysis with the… More >

Uddagiri Sirisha^1,*, Chanumolu Kiran Kumar², Revathi Durgam³, Poluru Eswaraiah⁴, G Muni Nagamani⁵

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4031-4059, 2025, DOI:10.32604/cmc.2025.063721 - 19 May 2025

Abstract A complete examination of Large Language Models’ strengths, problems, and applications is needed due to their rising use across disciplines. Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance, strengths, and weaknesses. This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies. In this research, 50 studies on 25+ LLMs, including GPT-3, GPT-4, Claude 3.5, DeepKet, and hybrid multimodal frameworks like ContextDET and GeoRSCLIP, are thoroughly reviewed. We propose LLM application taxonomy by grouping techniques by task focus—healthcare,… More >

Vidivelli Soundararajan^*, Manikandan Ramachandran^*, Srivatsan Vinodh Kumar

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5259-5277, 2025, DOI:10.32604/cmc.2025.063059 - 19 May 2025

Abstract Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze… More >

Ricardo Rodriguez-Torrealba, Eva Garcia-Lopez^*, Antonio Garcia-Cabot

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1683-1705, 2025, DOI:10.32604/cmc.2025.062004 - 16 April 2025

Abstract Generation of good-quality distractors is a key and time-consuming task associated with multiple-choice questions (MCQs), one of the assessment items that have dominated the educational field for years. Recent advances in language models and architectures present an opportunity for helping teachers to generate and update these elements to the required speed and scale of widespread increase in online education. This study focuses on a text-to-text approach for joints generation of distractors for MCQs, where the context, question and correct answer are used as input, while the set of distractors corresponds to the output, allowing the… More >

Stefan Ćirković¹, Vladimir Mladenović¹, Siniša Tomić², Dalibor Drljača², Olga Ristić^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696 - 06 March 2025

Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >

Konstantinos I. Roumeliotis^1,*, Nikolaos D. Tselikas², Dimitrios K. Nasiopoulos³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2769-2792, 2025, DOI:10.32604/cmc.2025.059567 - 17 February 2025

Abstract In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder… More >