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  • Open Access

    ARTICLE

    Wavelet Transform Convolution and Transformer-Based Learning Approach for Wind Power Prediction in Extreme Scenarios

    Jifeng Liang1, Qiang Wang2, Leibao Wang1, Ziwei Zhang3, Yonghui Sun3,*, Hongzhu Tao4, Xiaofei Li5

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 945-965, 2025, DOI:10.32604/cmes.2025.062315 - 11 April 2025

    Abstract Wind power generation is subjected to complex and variable meteorological conditions, resulting in intermittent and volatile power generation. Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations. This enhances the efficiency of wind power integration into the grid. It allows grid operators to anticipate and mitigate the impact of wind power fluctuations, significantly improving the resilience of wind farms and the overall power grid. Furthermore, it assists wind farm operators in optimizing the management of power generation facilities and reducing… More > Graphic Abstract

    Wavelet Transform Convolution and Transformer-Based Learning Approach for Wind Power Prediction in Extreme Scenarios

  • Open Access

    ARTICLE

    Integrating Attention Mechanisms in YOLOv8 for Improved Fall Detection Performance

    Nizar Zaghden1, Emad Ibrahim2, Mukaram Safaldin2,*, Mahmoud Mejdoub3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

    Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

  • Open Access

    ARTICLE

    YOLO-SIFD: YOLO with Sliced Inference and Fractal Dimension Analysis for Improved Fire and Smoke Detection

    Mariam Ishtiaq1,2, Jong-Un Won1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 06 March 2025

    Abstract Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and proposeMore >

  • Open Access

    ARTICLE

    DaC-GANSAEBF: Divide and Conquer-Generative Adversarial Network—Squeeze and Excitation-Based Framework for Spam Email Identification

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*, Yahia Said3, Shaaban M. Shaaban3, Husam Lahza4, Aws I. AbuEid5, Abdulrahman Alzahrani6

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3181-3212, 2025, DOI:10.32604/cmes.2025.061608 - 03 March 2025

    Abstract Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and… More >

  • Open Access

    ARTICLE

    A Study on Polyp Dataset Expansion Algorithm Based on Improved Pix2Pix

    Ziji Xiao1, Kaibo Yang1, Mingen Zhong1,*, Kang Fan2, Jiawei Tan2, Zhiying Deng1

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2665-2686, 2025, DOI:10.32604/cmc.2024.058345 - 17 February 2025

    Abstract The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the… More >

  • Open Access

    ARTICLE

    5DGWO-GAN: A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

    Sarvenaz Sadat Khatami1, Mehrdad Shoeibi2, Anita Ershadi Oskouei3, Diego Martín4,*, Maral Keramat Dashliboroun5

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 881-911, 2025, DOI:10.32604/cmc.2024.059999 - 03 January 2025

    Abstract The Internet of Things (IoT) is integral to modern infrastructure, enabling connectivity among a wide range of devices from home automation to industrial control systems. With the exponential increase in data generated by these interconnected devices, robust anomaly detection mechanisms are essential. Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns. This paper presents a novel approach utilizing generative adversarial networks (GANs) for anomaly detection in IoT systems. However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms,… More >

  • Open Access

    ARTICLE

    Research on Stock Price Prediction Method Based on the GAN-LSTM-Attention Model

    Peng Li, Yanrui Wei, Lili Yin*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 609-625, 2025, DOI:10.32604/cmc.2024.056651 - 03 January 2025

    Abstract Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. Firstly, the generator of this model combines the Long and Short-Term Memory Network (LSTM), the Attention Mechanism and, the Fully-Connected Layer, focusing on generating the predicted stock price. The discriminator combines the Convolutional Neural Network (CNN) and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices. Secondly, to evaluate the practical application… More >

  • Open Access

    ARTICLE

    Evaluation of Modern Generative Networks for EchoCG Image Generation

    Sabina Rakhmetulayeva1,*, Zhandos Zhanabekov2, Aigerim Bolshibayeva3

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4503-4523, 2024, DOI:10.32604/cmc.2024.057974 - 19 December 2024

    Abstract The applications of machine learning (ML) in the medical domain are often hindered by the limited availability of high-quality data. To address this challenge, we explore the synthetic generation of echocardiography images (echoCG) using state-of-the-art generative models. We conduct a comprehensive evaluation of three prominent methods: Cycle-consistent generative adversarial network (CycleGAN), Contrastive Unpaired Translation (CUT), and Stable Diffusion 1.5 with Low-Rank Adaptation (LoRA). Our research presents the data generation methodology, image samples, and evaluation strategy, followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of More > Graphic Abstract

    Evaluation of Modern Generative Networks for EchoCG Image Generation

  • Open Access

    ARTICLE

    A Location Trajectory Privacy Protection Method Based on Generative Adversarial Network and Attention Mechanism

    Xirui Yang, Chen Zhang*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3781-3804, 2024, DOI:10.32604/cmc.2024.057131 - 19 December 2024

    Abstract User location trajectory refers to the sequence of geographic location information that records the user’s movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user participates in the sensing task, the process of sensing data collection faces the problem of privacy leakage. To address the privacy leakage issue of trajectory data during uploading, publishing, and sharing when users use location services on mobile smart group sensing terminal devices, this paper proposes a privacy protection method based on generative adversarial networks and attention mechanisms (BiLS-A-GAN).… More >

  • Open Access

    REVIEW

    A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

    Monirah Al-Ajlan*, Mourad Ykhlef

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

    Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

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