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  • Open Access


    A Network Traffic Prediction Algorithm Based on Prophet-EALSTM-GPR

    Guoqing Xu1, Changsen Xia1, Jun Qian1, Guo Ran3, Zilong Jin1,2,*

    Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066

    Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and non-periodic parts. The periodic term… More >

  • Open Access


    Motor Torque Measurement Using Dual-Function Radar Polarized Signals of Flux

    B. Chinthamani1,*, N. S. Bhuvaneswari2, R. Senthil Kumar3, N. R. Shanker4

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 515-530, 2022, DOI:10.32604/iasc.2022.025410

    Abstract Motor Torque (MT) measurement plays a vital role for evaluating the performance of squirrel cage induction motor during operating conditions. Accurate and continuous measurements of MT provide information regarding driving load capacity, performance degradation of motor, reduces downtime and increases the efficiency. Traditional inline torque sensors-based measurement becomes inaccurate during abrupt change in load during starting condition of motor due to torque spikes. Mounting of torque sensor on motor is a major problem during torque measurement. Improper mounting of sensor acquires signals from other inefficient driveline components such as gearbox, couplings, and bearing. In this paper, we propose a non-contact… More >

  • Open Access


    Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN

    R. Amirthavalli1,*, S. Thanga Ramya2, N. R. Shanker3

    Computer Systems Science and Engineering, Vol.41, No.2, pp. 829-847, 2022, DOI:10.32604/csse.2022.020307

    Abstract In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected in each node by various water column environmental factors such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity. High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predicts… More >

  • Open Access


    Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

    Annan Jiang*, Xinping Guo, Shuai Zheng, Mengfei Xu

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199

    Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock. The calculation process of parameters… More >

  • Open Access


    Prediction of COVID-19 Cases Using Machine Learning for Effective Public Health Management

    Fahad Ahmad1,*, Saleh N. Almuayqil2, Mamoona Humayun2, Shahid Naseem3, Wasim Ahmad Khan4, Kashaf Junaid5

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2265-2282, 2021, DOI:10.32604/cmc.2021.013067

    Abstract COVID-19 is a pandemic that has affected nearly every country in the world. At present, sustainable development in the area of public health is considered vital to securing a promising and prosperous future for humans. However, widespread diseases, such as COVID-19, create numerous challenges to this goal, and some of those challenges are not yet defined. In this study, a Shallow Single-Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used for the classification and prediction of confirmed COVID-19 cases in five geographically distributed regions of Asia with diverse settings and environmental conditions: namely, China, South Korea,… More >

  • Open Access


    Prediction of Fracture Parameters of High Strength and Ultra-high Strength Concrete Beam using Gaussian Process Regression and Least Squares

    Shantaram Parab1, Shreya Srivastava2, Pijush Samui3, A. Ramachandra Murthy4

    CMES-Computer Modeling in Engineering & Sciences, Vol.101, No.2, pp. 139-158, 2014, DOI:10.3970/cmes.2014.101.139

    Abstract This paper studies the applicability of Gaussian Process Regression (GPR) and Least Squares Support Vector Machines (LSSVM) to predict fracture parameters and failure load (Pmax) of high strength and ultra-high strength concrete beams. Fracture characteristics include fracture energy (GF), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODC) Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split tensile strength, notch depth, modulus of elasticity and output fracture parameters. Four GPR and four LSSVM models have been developed using MATLAB software for training… More >

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