Kadriye Simsek Alan^*, Busra Senel Kahyaoglu

Journal on Artificial Intelligence, Vol.8, pp. 203-230, 2026, DOI:10.32604/jai.2026.078359 - 15 April 2026

Abstract Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and reliable diagnosis a critical challenge for modern healthcare systems. In this study, a systematic comparative performance analysis of widely used machine learning algorithms is conducted for the early detection of heart disease using tabular clinical data. Rather than proposing a novel model architecture, the primary objective is to provide a fair, reproducible, and clinically meaningful evaluation of commonly adopted classifiers under consistent experimental conditions. The Kaggle Heart Failure dataset is employed, and multiple machine learning models—including tuned Random Forest, tuned XGBoost,… More >

Yan-Hao Huang^*, Chung-Ming Kao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079381 - 09 April 2026

Abstract Photovoltaic (PV) equivalent-circuit models are widely used for performance evaluation and diagnostics, but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current–voltage (I–V) behavior. For nonlinear and strongly coupled PV models, conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence. This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition. The Starfish Optimization Algorithm (SFOA) is first used for parameter identification by searching for the optimal parameter set that… More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ting-Yuan Wu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079302 - 30 March 2026

Abstract Accurate soil classification is essential for pavement design; however, the traditional American Association of State Highway and Transportation Officials (AASHTO) classification system relies on extensive laboratory testing and subjective judgment. This study presents an artificial intelligence (AI) enhanced framework for AASHTO soil classification. A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development. Four machine learning models were trained, analyzed, and compared where the random forest (RF) consistently achieved the highest accuracy of 100% among the four models in predicting AASHTO soil groups. Feature importance More >

Hoyoon Lee¹, Jeonghoon Jee¹, Hoseon Kim², Cheol Oh^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076980 - 12 March 2026

Abstract Analyzing the driving behavior of autonomous vehicles (AV) in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design, providing infrastructure-based guidance information, and developing capability-enhanced AV perception systems. This study investigated the contributing factors affecting AV driving behavior using the Waymo Open Dataset. Binarized autonomous driving stability metrics, derived via a kernel density estimation, served as the target variables for a random forest classification model. The model’s input variables included 15 factors divided into four types: intersection-related, surrounding object-related, road infrastructure-related, and time-of-day-related types. The random forest classification model was… More >

Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

Sushruta Mishra¹, Sunil Kumar Mohapatra², Kshira Sagar Sahoo³, Anand Nayyar⁴, Tae-Kyung Kim^5,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072493 - 12 January 2026

Abstract With an increase in internet-connected devices and a dependency on online services, the threat of Distributed Denial of Service (DDoS) attacks has become a significant concern in cybersecurity. The proposed system follows a multi-step process, beginning with the collection of datasets from different edge devices and network nodes. To verify its effectiveness, experiments were conducted using the CICDoS2017, NSL-KDD, and CICIDS benchmark datasets alongside other existing models. Recursive feature elimination (RFE) with random forest is used to select features from the CICDDoS2019 dataset, on which a BiLSTM model is trained on local nodes. Local models… More >

Abeer Alnuaim^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.069110 - 10 November 2025

Abstract The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification… 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 >

Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3813-3837, 2025, DOI:10.32604/cmes.2025.073615 - 23 December 2025

Abstract Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of More >

Ayaz Hussain¹, Asad Hayat², Muhammad Hasnain^1,*

Journal on Artificial Intelligence, Vol.7, pp. 485-498, 2025, DOI:10.32604/jai.2025.071933 - 17 November 2025

Abstract Violent hate speech and scapegoating people against one another have emerged as a rising worldwide issue. But identifying and combating such content is crucial to create safer and more inclusive societies. The current study conducted research using Machine Learning models to classify hate speech and overcome the limitations posed in the existing detection techniques. Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbour (KNN) and Decision Tree were used on top of a publicly available hate speech dataset. The data was preprocessed by cleaning the text and tokenization and using normalization techniques to efficiently train the… More >