Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Vaskar Chakma^1,#, Xiaolin Ju^1,#, Heling Cao², Xue Feng³, Xiaodong Ji³, Haiyan Pan^3,*, Gao Zhan^1,*

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 37-86, 2026, DOI:10.32604/jimh.2026.075201 - 23 January 2026

Abstract Wide QRS Complex Tachycardia (WCT) is a life-threatening cardiac arrhythmia requiring rapid and accurate diagnosis. Traditional manual ECG interpretation is time-consuming and subject to inter-observer variability, while existing AI models often lack the clinical interpretability necessary for trusted deployment in emergency settings. We developed CardioForest, an optimized Random Forest ensemble model, for automated WCT detection from 12-lead ECG signals. The model was trained, tested, and validated using 10-fold cross-validation on 800,000 ten-second-long 12-lead Electrocardiogram (ECG) recordings from the MIMIC-IV dataset (15.46% WCT prevalence), with comparative evaluation against XGBoost, LightGBM, and Gradient Boosting models. Performance was… More >

Williams Kyei^*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >

Weishan Gao^1,2, Ye Wang^1,2, Xiaoyin Wang^1,2, Xiaochuan Jing^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026

Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >

Md. Ehsanul Haque¹, Md. Nurul Absur², Fahmid Al Farid³, Md Kamrul Siam⁴, Jia Uddin^5,*, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071771 - 12 January 2026

Abstract Manual inspection of onba earing casting defects is not realistic and unreliable, particularly in the case of some micro-level anomalies which lead to major defects on a large scale. To address these challenges, we propose BearFusionNet, an attention-based deep learning architecture with multi-stream, which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19. This hybrid design, figuratively beaming from one layer to another, extracts the enormity of representations on different scales, backed by a pre-preprocessing pipeline that brings defect saliency to the fore through contrast adjustment, denoising, and edge… More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Jaeho Shin¹, Jaekwang Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068767 - 10 November 2025

Abstract Network attacks have become a critical issue in the internet security domain. Artificial intelligence technology-based detection methodologies have attracted attention; however, recent studies have struggled to adapt to changing attack patterns and complex network environments. In addition, it is difficult to explain the detection results logically using artificial intelligence. We propose a method for classifying network attacks using graph models to explain the detection results. First, we reconstruct the network packet data into a graphical structure. We then use a graph model to predict network attacks using edge classification. To explain the prediction results, we… More >

Omar Darwish¹, Shorouq Al-Eidi², Abdallah Al-Shorman¹, Majdi Maabreh³, Anas Alsobeh⁴, Plamen Zahariev⁵, Yahya Tashtoush^6,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.068266 - 10 November 2025

Abstract Covert timing channels (CTC) exploit network resources to establish hidden communication pathways, posing significant risks to data security and policy compliance. Therefore, detecting such hidden and dangerous threats remains one of the security challenges. This paper proposes LinguTimeX, a new framework that combines natural language processing with artificial intelligence, along with explainable Artificial Intelligence (AI) not only to detect CTC but also to provide insights into the decision process. LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely. LinguTimeX demonstrates strong effectiveness in detecting CTC across… More >

MD Hamid Borkot Tulla^1,*, MD Moniur Rahman Ratan², Rashid MD Mamunur³, Abdullah Hil Safi Sohan⁴, MD Matiur Rahman⁵

Journal of Cyber Security, Vol.7, pp. 675-691, 2025, DOI:10.32604/jcs.2025.074737 - 24 December 2025

Abstract Phishing is considered one of the most widespread cybercrimes due to the fact that it combines both technical and human vulnerabilities with the intention of stealing sensitive information. Traditional blacklist and heuristic-based defenses fail to detect such emerging attack patterns; hence, intelligent and transparent detection systems are needed. This paper proposes an explainable machine learning framework that integrates predictive performance with regulatory accountability. Four models were trained and tested on a balanced dataset of 10,000 URLs, comprising 5000 phishing and 5000 legitimate samples, each characterized by 48 lexical and content-based features: Decision Tree, XGBoost, Logistic… More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3029-3085, 2025, DOI:10.32604/cmes.2025.073705 - 23 December 2025

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >