Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

Ka-Kyung Kim, Joon-Seok Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077315 - 26 February 2026

Abstract As attack techniques evolve and data volumes increase, the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential. Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems. However, its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm. To address these concerns, this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments, examining both their benefits and associated… More > Graphic Abstract

Itthidet Thawon^1,2, Duy Vo^3,4, Tinh Quoc Bui^3,4, Kanya Rattanamongkhonkun¹, Chakkapong Chamroon¹, Nakorn Tippayawong¹, Yuttana Mona¹, Ramnarong Wanison¹, Pana Suttakul^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077044 - 26 February 2026

Abstract Physics-informed neural networks (PINNs) have emerged as a promising class of scientific machine learning techniques that integrate governing physical laws into neural network training. Their ability to enforce differential equations, constitutive relations, and boundary conditions within the loss function provides a physically grounded alternative to traditional data-driven models, particularly for solid and structural mechanics, where data are often limited or noisy. This review offers a comprehensive assessment of recent developments in PINNs, combining bibliometric analysis, theoretical foundations, application-oriented insights, and methodological innovations. A bibliometric survey indicates a rapid increase in publications on PINNs since 2018,… More >

Fazal Ur Rehman¹, Yazeed Alkhrijah², Syed Muhammad Usman³, Muhammad Irfan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076332 - 26 February 2026

Abstract Automated detection of Motor Imagery (MI) tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation. Prediction of MI tasks can be performed with the help of Electroencephalogram (EEG) signals recorded by placing electrodes on the scalp of subjects; however, accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process, the extraction of a feature vector with high interclass variance, and accurate classification. The proposed method consists of preprocessing, feature extraction, and classification. First, EEG signals are denoised using a… More >

Silvia Alayón^1,*, Carlos Martín¹, Jesús Torres¹, Manuel Bacallado¹, Rosa Aguilar¹, Guzmán Savirón²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073488 - 26 February 2026

Abstract Business Process Modelling (BPM) is essential for analyzing, improving, and automating the flow of information within organizations, but traditional approaches based on manual interpretation are slow, error-prone, and require a high level of expertise. This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation (BPMN) diagrams solely from verbal descriptions of the processes to be modeled, utilizing Large Language Models (LLMs) and multimodal Artificial Intelligence (AI). Experimental results, based on video recordings of process explanations provided by an expert from an organization (in this case,… More >

Waseem Akhtar^1,2, Mahwish Ilyas³, Romana Aziz^4,*, Ghadah Aldehim⁴, Tassawar Iqbal⁵, Muhammad Ramzan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.071669 - 26 February 2026

Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.8, pp. 65-87, 2026, DOI:10.32604/jai.2026.072857 - 13 February 2026

Abstract This paper presents a comprehensive investigation into the development and evaluation of Convolutional Neural Network (CNN) models for limited-vocabulary spoken word classification, a fundamental component of many voice-controlled systems. Two distinct CNN architectures are examined: a timeseries 1D CNN that operates directly on the temporal waveform samples of the audio signal, and a 2D CNN that leverages the richer time–frequency representation provided by spectrograms. The study systematically analyzes the influence of key architectural and training parameters, including the number of CNN layers, convolution kernel sizes, and the dimensionality of fully connected layers, on classification accuracy.… More >

Alaa Tolah^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074997 - 10 February 2026

Abstract The rapidly evolving cybersecurity threat landscape exposes a critical flaw in traditional educational programs where static curricula cannot adapt swiftly to novel attack vectors. This creates a significant gap between theoretical knowledge and the practical defensive capabilities needed in the field. To address this, we propose TeachSecure-CTI, a novel framework for adaptive cybersecurity curriculum generation that integrates real-time Cyber Threat Intelligence (CTI) with AI-driven personalization. Our framework employs a layered architecture featuring a CTI ingestion and clustering module, natural language processing for semantic concept extraction, and a reinforcement learning agent for adaptive content sequencing. By… More >

Abdu Salam¹, Fahd M. Aldosari², Donia Y. Badawood³, Farhan Amin^4,*, Isabel de la Torre^5,*, Gerardo Mendez Mezquita⁶, Henry Fabian Gongora⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073342 - 10 February 2026

Abstract Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple… More >

Sangeeta Mittal¹, Chetna Gupta¹, Varun Gupta^2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071599 - 10 February 2026

Abstract This survey presents a comprehensive examination of sensor fusion research spanning four decades, tracing the methodological evolution, application domains, and alignment with classical hierarchical models. Building on this long-term trajectory, the foundational approaches such as probabilistic inference, early neural networks, rule-based methods, and feature-level fusion established the principles of uncertainty handling and multi-sensor integration in the 1990s. The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering, Bayesian–Dempster–Shafer hybrids, distributed consensus algorithms, and machine learning ensembles for more robust and domain-specific implementations. From 2011 to 2020, the widespread… More >