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  • Open Access

    ARTICLE

    Upholding Academic Integrity amidst Advanced Language Models: Evaluating BiLSTM Networks with GloVe Embeddings for Detecting AI-Generated Scientific Abstracts

    Lilia-Eliana Popescu-Apreutesei, Mihai-Sorin Iosupescu, Sabina Cristiana Necula, Vasile-Daniel Păvăloaia*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2605-2644, 2025, DOI:10.32604/cmc.2025.064747 - 03 July 2025

    Abstract The increasing fluency of advanced language models, such as GPT-3.5, GPT-4, and the recently introduced DeepSeek, challenges the ability to distinguish between human-authored and AI-generated academic writing. This situation is raising significant concerns regarding the integrity and authenticity of academic work. In light of the above, the current research evaluates the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with pre-trained GloVe (Global Vectors for Word Representation) embeddings to detect AI-generated scientific abstracts drawn from the AI-GA (Artificial Intelligence Generated Abstracts) dataset. Two core BiLSTM variants were assessed: a single-layer approach and a dual-layer… More >

  • Open Access

    ARTICLE

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

    Jungpil Shin1,*, Md. Al Mehedi Hasan2, Md. Maniruzzaman3, Satoshi Nishimura1, Sultan Alfarhood4

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025

    Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More > Graphic Abstract

    Video-Based Human Activity Recognition Using Hybrid Deep Learning Model

  • Open Access

    ARTICLE

    Research on Flexible Load Aggregation and Coordinated Control Methods Considering Dynamic Demand Response

    Chun Xiao1,2,*

    Energy Engineering, Vol.122, No.7, pp. 2719-2750, 2025, DOI:10.32604/ee.2025.063782 - 27 June 2025

    Abstract In contemporary power systems, delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids. This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration. In the initial stage, using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads, including temperature-controlled loads, electric vehicles, and energy storage devices, a novel calculation method for their maximum adjustable capacities is devised. Distinct from conventional methods, this newly developed approach enables more precise and adaptable quantification… More >

  • Open Access

    ARTICLE

    Intelligent Detection of Abnormal Traffic Based on SCN-BiLSTM

    Lulu Zhang, Xuehui Du*, Wenjuan Wang, Yu Cao, Xiangyu Wu, Shihao Wang

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1901-1919, 2025, DOI:10.32604/cmc.2025.064270 - 09 June 2025

    Abstract To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features More >

  • Open Access

    ARTICLE

    Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM

    Yingyong Zou*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

    Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

  • Open Access

    ARTICLE

    A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

    Qi Fei1,2,*, Haojun Hu3, Guisheng Yin1, Zhian Sun2

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3251-3279, 2025, DOI:10.32604/cmc.2024.058931 - 17 February 2025

    Abstract Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Approach for Automating App Review Classification: Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

    Nahed Alsaleh1,2, Reem Alnanih1,*, Nahed Alowidi1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 949-976, 2025, DOI:10.32604/cmc.2024.059351 - 03 January 2025

    Abstract App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) More >

  • Open Access

    ARTICLE

    Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

    Jiachang Liu1,*, Zhengwei Huang2, Junfeng Xiang1, Lu Liu1, Manlin Hu1

    Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

    Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

  • Open Access

    ARTICLE

    Mathematical Named Entity Recognition Based on Adversarial Training and Self-Attention

    Qiuyu Lai1,2, Wang Kang3, Lei Yang1,2, Chun Yang1,2,*, Delin Zhang2,*

    Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724 - 06 September 2024

    Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >

  • Open Access

    ARTICLE

    DPAL-BERT: A Faster and Lighter Question Answering Model

    Lirong Yin1, Lei Wang1, Zhuohang Cai2, Siyu Lu2,*, Ruiyang Wang2, Ahmed AlSanad3, Salman A. AlQahtani3, Xiaobing Chen4, Zhengtong Yin5, Xiaolu Li6, Wenfeng Zheng2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622 - 20 August 2024

    Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

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