Raed Alotaibi^1,*, Muhammad Atta Othman Ahmed², Omar Reyad^3,4,*, Nahla Fathy Omran⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076156 - 09 April 2026

Abstract The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks… More >

Ruida Liu, Dan Wang^*, Jiaming Chen, Meng Xu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074554 - 09 April 2026

Abstract Accurate prediction of shoulder joint angles based on surface electromyography (sEMG) signals is critical in human–machine interaction and rehabilitation engineering. However, due to the shoulder joint’s complex degrees of freedom, dynamically varying muscle coordination patterns, and the susceptibility of sEMG signals to cross-talk and noise interference, achieving high-precision prediction remains challenging. In this study, LASENet (BiLSTM–Attention–SE Network) is proposed as an end-to-end deep learning framework that integrates a bidirectional long short-term memory network (BiLSTM), a multi-head self-attention (MHSA) mechanism, and a squeeze-and-excitation (SE) block to predict shoulder joint angles across three degrees of freedom directly More >

Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026

Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >

Abiodun Abiola^*, Francisca Segura, José Manuel Andújar, Antonio Javier Barragán

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.070788 - 30 March 2026

Abstract This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related… More >

Hongbo Sun¹, Xingyu Jiang^1,*, Wenyao Sun¹, Yi Zhao¹, Jifeng Cheng², Xiaoyi Qian¹, Guo Wang³

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.073012 - 27 March 2026

Abstract The accuracy of photovoltaic (PV) power prediction is significantly influenced by meteorological and environmental factors. To enhance ultra-short-term forecasting precision, this paper proposes an interpretable feedback prediction method based on a parallel dual-stream Temporal Convolutional Network-Bidirectional Long Short-Term Memory (TCN-BiLSTM) architecture incorporating a spatiotemporal attention mechanism. Firstly, during data preprocessing, the optimal historical time window is determined through autocorrelation analysis while highly correlated features are selected as model inputs using Pearson correlation coefficients. Subsequently, a parallel dual-stream TCN-BiLSTM model is constructed where the TCN branch extracts localized transient features and the BiLSTM branch captures long-term… More >

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026

Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

Guotai Huang, Xiyu Gao, Peng Liu, Liming Zhou^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074941 - 12 March 2026

Abstract To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions, this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory (LSTM) encoder, a Gated Recurrent Unit (GRU) decoder, and a multi-head attention mechanism. This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions, thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics. Experiments using long-term data from real test benches demonstrate that the model achieves… More >

Soo-Yeon Jeong¹, Ho-Yeon Jeong², Sun-Young Ihm^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074016 - 12 March 2026

Abstract Sign language is a primary mode of communication for individuals with hearing impairments, conveying meaning through hand shapes and hand movements. Contrary to spoken or written languages, sign language relies on the recognition and interpretation of hand gestures captured in video data. However, sign language datasets remain relatively limited compared to those of other languages, which hinders the training and performance of deep learning models. Additionally, the distinct word order of sign language, unlike that of spoken language, requires context-aware and natural sentence generation. To address these challenges, this study applies data augmentation techniques to… More >

Oumaima Saidani¹, Nazia Azim², Ateeq Ur Rehman^3,*, Akbayan Bekarystankyzy⁴, Hala AbdelHameed Mostafa⁵, Mohamed R. Abonazel⁶, Ehab Ebrahim Mohamed Ebrahim⁷, Sarah Abu Ghazalah⁸

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.072772 - 12 March 2026

Abstract The convergence of Software Defined Networking (SDN) in Internet of Vehicles (IoV) enables a flexible, programmable, and globally visible network control architecture across Road Side Units (RSUs), cloud servers, and automobiles. While this integration enhances scalability and safety, it also raises sophisticated cyberthreats, particularly Distributed Denial of Service (DDoS) attacks. Traditional rule-based anomaly detection methods often struggle to detect modern low-and-slow DDoS patterns, thereby leading to higher false positives. To this end, this study proposes an explainable hybrid framework to detect DDoS attacks in SDN-enabled IoV (SDN-IoV). The hybrid framework utilizes a Residual Network (ResNet)… More >

Chengqi Liu¹, Yongtao Li², Weiping Zou^3,*, Deyu Lin^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074975 - 10 February 2026

Abstract With the large-scale deployment of the Internet of Things (IoT) devices, their weak security mechanisms make them prime targets for malware attacks. Attackers often use Domain Generation Algorithm (DGA) to generate random domain names, hiding the real IP of Command and Control (C&C) servers to build botnets. Due to the randomness and dynamics of DGA, traditional methods struggle to detect them accurately, increasing the difficulty of network defense. This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments. Specifically, a teacher model combining CharacterBERT, a bidirectional long short-term memory More >