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  • Open Access

    ARTICLE

    Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Ayesha Atta2, 3, Allah Ditta4, Hani Alquhayz5, Muhammad Farhan Khan6, Atta-ur-Rahman7, Rizwan Ali Naqvi8

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 139-151, 2020, DOI:10.32604/cmc.2020.011416

    Abstract The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using… More >

  • Open Access

    ARTICLE

    An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

    Bingjie Yan1, Jun Wang1, Zhen Zhang2, Xiangyan Tang1, *, Yize Zhou1, Guopeng Zheng1, Qi Zou1, Yao Lu1, Boyi Liu3, Wenxuan Tu4, Neal Xiong5

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317

    Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More >

  • Open Access

    ARTICLE

    Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

    Bolun Chen1, 2, Guochang Zhu1, *, Min Ji1, Yongtao Yu1, Jianyang Zhao1, Wei Liu3

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1039-1049, 2020, DOI:10.32604/cmc.2020.010583

    Abstract Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding… More >

  • Open Access

    ARTICLE

    Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory

    Hui Li1, Cailin Shi2, 3, Xin Liu2, 3, Aziguli Wulamu2, 3, *, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 987-1004, 2020, DOI:10.32604/cmc.2020.09812

    Abstract The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid. The three-phase unbalanced is closely related to power planning and load distribution. When the unbalance occurs, the safe operation of the electrical equipment will be seriously jeopardized. This paper proposes a Hierarchical Temporal Memory (HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding, the spatial pooler for frequency pattern learning, the temporal pooler for pattern sequence learning, and the sparse distributed representations classifier for unbalance prediction. Following the feasibility of spatialtemporal streaming data analysis, we adopted… More >

  • Open Access

    ARTICLE

    GACNet: A Generative Adversarial Capsule Network for Regional Epitaxial Traffic Flow Prediction

    Jinyuan Li1, Hao Li1, Guorong Cui1, Yan Kang1, *, Yang Hu1, Yingnan Zhou2

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

    Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >

  • Open Access

    ARTICLE

    Modeling Tracer Flow Characteristics in Different Types of Pores: Visualization and Mathematical Modeling

    Tongjing Liu1, 2, *, Weixia Liu3, Pengxiang Diwu4, Gaixing Hu5, 6, Ting Xu1, 7, Yuqi Li2, Zhenjiang You8, Runwei Qiao2, Jia Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1205-1222, 2020, DOI:10.32604/cmes.2020.08961

    Abstract Structure of porous media and fluid distribution in rocks can significantly affect the transport characteristics during the process of microscale tracer flow. To clarify the effect of micro heterogeneity on aqueous tracer transport, this paper demonstrates microscopic experiments at pore level and proposes an improved mathematical model for tracer transport. The visualization results show a faster tracer movement into movable water than it into bound water, and quicker occupancy in flowing pores than in storage pores caused by the difference of tracer velocity. Moreover, the proposed mathematical model includes the effects of bound water and flowing porosity by applying interstitial… More >

  • Open Access

    ARTICLE

    FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

    Yang Yang1,*, Jing Dong1, Chao Fang2, Ping Xie3, Na An3

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1015-1031, 2020, DOI:10.32604/cmes.2020.09404

    Abstract The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services. Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service. Failure prediction is an important means of ensuring service availability. Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics, and the distribution imbalance between the failure sample and the normal sample is widespread, resulting in inaccurate failure prediction. Targeting these challenges, this paper proposes a novel failure prediction method FP-STE (Failure Prediction… More >

  • Open Access

    ARTICLE

    Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

    Pingping Xia1, *, Aihua Xu2, Tong Lian1

    Journal of Quantum Computing, Vol.2, No.1, pp. 25-32, 2020, DOI:10.32604/jqc.2019.09232

    Abstract Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the gray correlation analysis method is… More >

  • Open Access

    ARTICLE

    KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117

    Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme… More >

  • Open Access

    ARTICLE

    A New Sequential Image Prediction Method Based on LSTM and DCGAN

    Wei Fang1, 2, Feihong Zhang1, *, Yewen Ding1, Jack Sheng3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06395

    Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More >

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