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 >

Kirubavathi Ganapathiyappan^1,*, Kiruba Marimuthu Eswaramoorthy¹, Abi Thangamuthu Shanthamani¹, Aksaya Venugopal¹, Asita Pon Bhavya Iyyappan¹, Thilaga Manickam¹, Ateeq Ur Rehman^2,*, Habib Hamam^3,4,5,6

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070067 - 09 December 2025

Abstract The growing use of Portable Document Format (PDF) files across various sectors such as education, government, and business has inadvertently turned them into a major target for cyberattacks. Cybercriminals take advantage of the inherent flexibility and layered structure of PDFs to inject malicious content, often employing advanced obfuscation techniques to evade detection by traditional signature-based security systems. These conventional methods are no longer adequate, especially against sophisticated threats like zero-day exploits and polymorphic malware. In response to these challenges, this study introduces a machine learning-based detection framework specifically designed to combat such threats. Central to… More >

Muhammad Saad Farooqui¹, Aizaz Ahmad Khattak², Bakri Hossain Awaji³, Nazik Alturki⁴, Noha Alnazzawi⁵, Muhammad Hanif^6,*, Muhammad Shahbaz Khan²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4395-4439, 2025, DOI:10.32604/cmes.2025.072023 - 23 December 2025

Abstract Quality of Service (QoS) assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and botnet intrusions. This paper presents AutoSHARC, a feedback-driven, explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier. AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones, followed by post-hoc SHAP-guided retraining to refine feature importance, forming a feedback loop where only the most impactful attributes are More >

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Zekun Liu¹, Junwei Ma^1,2,*, Xin Gong¹, Xiu Liu¹, Bingbing Liu¹, Long An¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 983-999, 2025, DOI:10.32604/cmc.2025.065978 - 29 August 2025

Abstract With the ongoing digitalization and intelligence of power systems, there is an increasing reliance on large-scale data-driven intelligent technologies for tasks such as scheduling optimization and load forecasting. Nevertheless, power data often contains sensitive information, making it a critical industry challenge to efficiently utilize this data while ensuring privacy. Traditional Federated Learning (FL) methods can mitigate data leakage by training models locally instead of transmitting raw data. Despite this, FL still has privacy concerns, especially gradient leakage, which might expose users’ sensitive information. Therefore, integrating Differential Privacy (DP) techniques is essential for stronger privacy protection.… More >

Namal Rathnayake¹, Jeevani Jayasinghe^2,3, Rashmi Semasinghe², Upaka Rathnayake^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R² values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

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 >

Isha Kiran¹, Shahzad Ali^2,3, Sajawal ur Rehman Khan^4,5, Musaed Alhussein⁶, Sheraz Aslam^7,8,*, Khursheed Aurangzeb^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5057-5078, 2025, DOI:10.32604/cmc.2025.058724 - 06 March 2025

Abstract Cardiovascular disease (CVD) remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis, driven by risk factors such as hypertension, high cholesterol, and irregular pulse rates. Traditional diagnostic methods often struggle with the nuanced interplay of these risk factors, making early detection difficult. In this research, we propose a novel artificial intelligence-enabled (AI-enabled) framework for CVD risk prediction that integrates machine learning (ML) with eXplainable AI (XAI) to provide both high-accuracy predictions and transparent, interpretable insights. Compared to existing studies that typically focus on either optimizing ML… More >

B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >