Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Zongqi Li¹, Hongwei Zhao^2,*, Jianyong Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 345-357, 2025, DOI:10.32604/cmes.2025.066175 - 31 July 2025

Abstract Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain More >

Selime Tuba Pesen, Mehmet Ali Altuncu^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5597-5611, 2025, DOI:10.32604/cmc.2025.066702 - 30 July 2025

Abstract EEG signals are widely used in emotion recognition due to their ability to reflect involuntary physiological responses. However, the high dimensionality of EEG signals and their continuous variability in the time-frequency plane make their analysis challenging. Therefore, advanced deep learning methods are needed to extract meaningful features and improve classification performance. This study proposes a hybrid model that integrates the Swin Transformer and Temporal Convolutional Network (TCN) mechanisms for EEG-based emotion recognition. EEG signals are first converted into scalogram images using Continuous Wavelet Transform (CWT), and classification is performed on these images. Swin Transformer is… More >

Saeed Alzahrani¹, Nazar Hussain², Farah Mohammad^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4825-4849, 2025, DOI:10.32604/cmc.2025.066322 - 30 July 2025

Abstract Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients. This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling. By integrating real-time images, electronic health records, and genomic information, the system enables personalized simulations for disease progression modeling, treatment response prediction, and preventive care strategies. In dysarthric speech, which is characterized by articulation imprecision, temporal misalignments, and phoneme distortions, existing models struggle to capture these irregularities. Traditional approaches, often relying solely on audio features, fail to address… More >

Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025

Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >

Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Xi Li^1,2, Weiwei Zhu², Qian Li^3,*, Changhui Hou^1,*, Yaozong Zhang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 369-391, 2024, DOI:10.32604/cmc.2024.054982 - 15 October 2024

Abstract Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components:… More >

Wenfeng Zheng¹, Siyu Lu¹, Zhuohang Cai¹, Ruiyang Wang¹, Lei Wang², Lirong Yin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2023.046692 - 11 March 2024

Abstract In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language More >