Yuh-Shihng Chang¹, Hsuan-Chao Huang^2,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083954 - 13 August 2026

Abstract The integration of Building Information Modeling (BIM), Internet of Things (IoT), digital twins, and artificial intelligence (AI) has enabled advanced smart building operations with real-time monitoring and data driven decision-making capabilities. However, existing systems still face critical challenges in ensuring trusted data provenance, secure cross-layer data exchange, and robust access control in distributed IoT environments. These limitations significantly affect the reliability and trustworthiness of data driven analytics and system-level decision-making. To address these challenges, this study proposes a blockchain-assisted BIM–IoT digital twin architecture for trusted operational risk prediction in smart buildings. The proposed framework integrates… More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084555 - 23 July 2026

Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Mingqi Liu, Ying Wang^*, Wujiang Li, Juyong Cao, Fuyong Yang

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.074783 - 12 July 2026

Abstract Accurately predicting battery life is essential for performance management and system safety. Due to the complexity and diversity of internal mechanisms in lithium-ion batteries, their nonlinear characteristics directly give rise to uncertainty in the battery degradation process. However, most existing prediction methods do not fully account for the uncertainty caused by various factors and only provide a point estimate finally. To address this issue, this paper proposes a new framework that combines Random Forest and Conformal Prediction to predict battery life and quantify the uncertainty of the results. This approach leverages the efficiency of Random… More >

Jakkrawut Techo¹, Panupon Trairat¹, Karthikeyan Velmurugan^2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.080474 - 18 June 2026

Abstract In this study, selective and non-selective absorber-coated trays were employed to dry carrots and pears. Two trays with a selective absorber coating (1 mm thickness) were used, each loaded with 600 g of sliced carrots and pears. Similarly, two additional trays with a non-selective absorber coating were utilised. Furthermore, the performance of both selective and non-selective absorber-coated trays was compared with conventional open sun drying. The selective absorber-coated tray demonstrated higher thermal energy absorption and enabled the drying of carrots within 2 days, resulting in a weight loss of 529 g. In contrast, owing to… More >

Yu Zhang^1,2,#, Shihan Tan^1,#, Guangyao Lian², Congying Dun³, Qiwei Hu^1,*, Chiming Guo^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081931 - 15 June 2026

Abstract Gearboxes are critical components in the transmission systems of various mechanical equipment. Subjected to complex and harsh operating conditions for a long time, they suffer from a high failure rate and potentially severe consequences. Traditional fault diagnosis methods are limited by problems such as noise interference, and can hardly meet the requirements in terms of diagnostic accuracy, generalization ability, and reliability. To tackle the deficiencies of traditional gearbox fault diagnosis methods, including insufficient utilization of features, poor generalization under small-sample conditions, and weak model interpretability, this paper proposes a fault diagnosis method based on multi-dimensional… More >

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 >