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  • Open Access


    Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

    Lin Ma1, Liyong Wang1, Shuang Zeng1, Yutong Zhao1, Chang Liu1, Heng Zhang1, Qiong Wu2,*, Hongbo Ren2

    Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332

    Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

  • Open Access


    Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

    Haoyi Zhong, Yongjiang Zhao, Chang Gyoon Lim*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1757-1781, 2024, DOI:10.32604/cmes.2024.049208

    Abstract This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery (LiB) time series data. As the energy sector increasingly focuses on integrating distributed energy resources, Virtual Power Plants (VPP) have become a vital new framework for energy management. LiBs are key in this context, owing to their high-efficiency energy storage capabilities essential for VPP operations. However, LiBs are prone to various abnormal states like overcharging, over-discharging, and internal short circuits, which impede power transmission efficiency. Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and… More >

  • Open Access


    Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

    Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955

    Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

  • Open Access


    A New Malicious Code Classification Method for the Security of Financial Software

    Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849

    Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

  • Open Access


    Spatial and Contextual Path Network for Image Inpainting

    Dengyong Zhang1,2, Yuting Zhao1,2, Feng Li1,2, Arun Kumar Sangaiah3,4,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 115-133, 2024, DOI:10.32604/iasc.2024.040847

    Abstract Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area. Image feature extraction is the core of image restoration. Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting. However, in the process of feature extraction, it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time. In order to obtain more spatial information and a larger receptive field at the same time, we put forward… More >

  • Open Access


    Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

    Zhenxiang He*, Zhenyu Zhao, Ke Chen, Yanlin Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

    Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… More >

  • Open Access


    Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

    Arnab Dey1,*, Samit Biswas1, Dac-Nhuong Le2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3067-3087, 2024, DOI:10.32604/cmc.2024.049512

    Abstract Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers the likelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in video streams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enable instant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing action datasets often lack diversity and specificity for workout actions, hindering the development of accurate recognition models. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significant… More >

  • Open Access


    Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid (MHAVH) Model

    Hina Naz1, Zuping Zhang1,*, Mohammed Al-Habib1, Fuad A. Awwad2, Emad A. A. Ismail2, Zaid Ali Khan3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2673-2696, 2024, DOI:10.32604/cmc.2024.049186

    Abstract Cardiovascular disease is the leading cause of death globally. This disease causes loss of heart muscles and is also responsible for the death of heart cells, sometimes damaging their functionality. A person’s life may depend on receiving timely assistance as soon as possible. Thus, minimizing the death ratio can be achieved by early detection of heart attack (HA) symptoms. In the United States alone, an estimated 610,000 people die from heart attacks each year, accounting for one in every four fatalities. However, by identifying and reporting heart attack symptoms early on, it is possible to… More >

  • Open Access


    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

  • Open Access


    RepBoTNet-CESA: An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

    Xiabin Zhang1,2, Zhongyi Hu1,2,*, Lei Xiao1,2, Hui Huang1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2879-2905, 2024, DOI:10.32604/cmc.2024.048725

    Abstract Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease (AD). Most studies predominantly employ Convolutional Neural Networks (CNNs), which focus solely on local features, thus encountering difficulties in handling global features. In contrast to natural images, Structural Magnetic Resonance Imaging (sMRI) images exhibit a higher number of channel dimensions. However, during the Position Embedding stage of Multi Head Self Attention (MHSA), the coded information related to the channel dimension is disregarded. To tackle these issues, we propose the RepBoTNet-CESA network, an advanced AD-aided diagnostic model that is capable… More >

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