Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (142)
  • Open Access

    ARTICLE

    Short-Term Photovoltaic Power Prediction Based on Multi-Stage Temporal Feature Learning

    Qiang Wang1, Hao Cheng2, Wenrui Zhang2,*, Guangxi Li3, Fan Xu2, Dianhao Chen4, Haixiang Zang4

    Energy Engineering, Vol.122, No.2, pp. 747-764, 2025, DOI:10.32604/ee.2025.059533 - 31 January 2025

    Abstract Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources. However, the fluctuations and intermittency of photovoltaic (PV) power pose challenges for its extensive incorporation into power grids. Thus, enhancing the precision of PV power prediction is particularly important. Although existing studies have made progress in short-term prediction, issues persist, particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data. These factors hinder improvements in PV power prediction performance. To overcome these challenges, this paper… More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Forecast Based on STL-IAOA-iTransformer Algorithm: A Case Study in Northwest China

    Zhaowei Yang1, Bo Yang2,*, Wenqi Liu1, Miwei Li2, Jiarong Wang2, Lin Jiang3, Yiyan Sang4, Zhenning Pan5

    Energy Engineering, Vol.122, No.2, pp. 405-430, 2025, DOI:10.32604/ee.2025.059515 - 31 January 2025

    Abstract Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids. Although numerous studies have employed various methods to forecast wind power, there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction. To improve the accuracy of short-term wind power forecast, this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer, which is based on seasonal and trend decomposition using LOESS (STL) and iTransformer model optimized by improved arithmetic optimization algorithm (IAOA).… More >

  • Open Access

    ARTICLE

    Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

    Muhammad Armghan Latif1, Zohaib Mushtaq2,*, Saifur Rahman3, Saad Arif4, Salim Nasar Faraj Mursal3, Muhammad Irfan3, Haris Aziz5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

    Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >

  • Open Access

    ARTICLE

    Intrumer: A Multi Module Distributed Explainable IDS/IPS for Securing Cloud Environment

    Nazreen Banu A*, S.K.B. Sangeetha

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 579-607, 2025, DOI:10.32604/cmc.2024.059805 - 03 January 2025

    Abstract The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic. Cloud environments pose significant challenges in maintaining privacy and security. Global approaches, such as IDS, have been developed to tackle these issues. However, most conventional Intrusion Detection System (IDS) models struggle with unseen cyberattacks and complex high-dimensional data. In fact, this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system, named INTRUMER, which offers balanced accuracy, reliability, and security in cloud settings by multiple modules working together within it. The traffic captured… More >

  • Open Access

    ARTICLE

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

    Hongxing Wang1, Xilai Ju2, Hua Zhu1,*, Huafeng Li1,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

    Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

  • Open Access

    ARTICLE

    A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

    Jianwei Zhang1,2,*, Zhishang Zhao3, Zengyu Cai3, Yuan Feng4, Liang Zhu3, Yahui Sun3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 977-996, 2025, DOI:10.32604/cmc.2024.058438 - 03 January 2025

    Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >

  • Open Access

    ARTICLE

    Steel Surface Defect Detection Using Learnable Memory Vision Transformer

    Syed Tasnimul Karim Ayon1,#, Farhan Md. Siraj1,#, Jia Uddin2,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

    Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

  • Open Access

    ARTICLE

    A Hybrid Approach for Pavement Crack Detection Using Mask R-CNN and Vision Transformer Model

    Shorouq Alshawabkeh, Li Wu*, Daojun Dong, Yao Cheng, Liping Li

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 561-577, 2025, DOI:10.32604/cmc.2024.057213 - 03 January 2025

    Abstract Detecting pavement cracks is critical for road safety and infrastructure management. Traditional methods, relying on manual inspection and basic image processing, are time-consuming and prone to errors. Recent deep-learning (DL) methods automate crack detection, but many still struggle with variable crack patterns and environmental conditions. This study aims to address these limitations by introducing the MaskerTransformer, a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network (Mask R-CNN) with the global contextual awareness of Vision Transformer (ViT). The research focuses on leveraging the strengths of both architectures… More >

  • Open Access

    ARTICLE

    Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

    Jianfeng Lu1,2, Xiangye Huang1, Caijin Li1, Renlin Xin1, Shanqing Zhang1,2, Mahmoud Emam1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 405-423, 2025, DOI:10.32604/cmes.2024.058121 - 17 December 2024

    Abstract Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and counterfeiting. Stamped seal inspection is commonly audited manually to ensure document authenticity. However, manual assessment of seal images is tedious and labor-intensive due to human errors, inconsistent placement, and completeness of the seal. Traditional image recognition systems are inadequate enough to identify seal types accurately, necessitating a neural network-based method for seal image recognition. However, neural network-based classification algorithms, such as Residual Networks (ResNet) and Visual Geometry Group with 16 layers… More >

  • Open Access

    ARTICLE

    A Deep Learning-Based Automated Approach of Schizophrenia Detection from Facial Micro-Expressions

    Anum Saher1, Ghulam Gilanie1,*, Sana Cheema1, Akkasha Latif1, Syeda Naila Batool1, Hafeez Ullah2

    Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1053-1071, 2024, DOI:10.32604/iasc.2024.057047 - 30 December 2024

    Abstract Schizophrenia is a severe mental illness responsible for many of the world’s disabilities. It significantly impacts human society; thus, rapid, and efficient identification is required. This research aims to diagnose schizophrenia directly from a high-resolution camera, which can capture the subtle micro facial expressions that are difficult to spot with the help of the naked eye. In a clinical study by a team of experts at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan, there were 300 people with schizophrenia and 299 healthy subjects. Videos of these participants have been captured and converted into their frames using… More >

Displaying 1-10 on page 1 of 142. Per Page