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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 More >

  • Open Access


    Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

    Weifeng Liu1,2, Xin Yu1,*, Qinyang Zhao3, Guang Cheng2, Xiaobing Hou1, Shengqi He4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3199-3219, 2023, DOI:10.32604/cmc.2023.032595

    Abstract Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission… More >

  • Open Access


    An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

    Savita Khurana1, Gaurav Sharma2, Neha Miglani3, Aman Singh4, Abdullah Alharbi5, Wael Alosaimi5, Hashem Alyami6, Nitin Goyal7,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 629-649, 2022, DOI:10.32604/cmc.2022.021884

    Abstract COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases… More >

  • Open Access


    Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction

    Mashael Khayyat1,*, Kaouther Laabidi2, Nada Almalki1, Maysoon Al-zahrani1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3781-3793, 2021, DOI:10.32604/cmc.2021.014918

    Abstract COVID-19 comes from a large family of viruses identified in 1965; to date, seven groups have been recorded which have been found to affect humans. In the healthcare industry, there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict confirmed cases, recovered cases, and deaths. Many researchers and scientists in the field of machine learning are also involved in solving this dilemma, seeking to understand the patterns and characteristics of virus attacks, so scientists may make the right decisions and take specific actions. Furthermore,… More >

  • Open Access


    Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration

    Jingjian Chen1, Gang Xu1, *, Fengqi Wei1, Liqiang He2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1845-1858, 2020, DOI:10.32604/cmc.2020.010010

    Abstract Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical… More >

  • Open Access


    Making a PROPHET

    Conor Rafferty1, R. Kent Smith

    CMES-Computer Modeling in Engineering & Sciences, Vol.1, No.1, pp. 151-160, 2000, DOI:10.3970/cmes.2000.001.151

    Abstract The PROPHET simulator is a software system for solving partial differential equations (PDEs) in time and 1,2 or 3 space dimensions. When equipped with appropriate modules, it can be configured as a process simulator or a device simulator, with application to modeling semiconductor fabrication processes and transistor behavior. The simulator is designed with three main goals: efficiency, geometric flexibility, equation extensibility. The first two distinguish it from canned packages such as Mathematica, which do not easily allow the use of arbitrary shapes or grids and are not tuned to solve systems with 105 or 106 unknowns. More >

  • Open Access


    Multidimensional Semiconductor Device and Micro-Scale Thermal Modeling Using the PROPHET Simulator with Dial-an-Operator Framework

    Anand L. Pardhanani1, Graham F. Carey1

    CMES-Computer Modeling in Engineering & Sciences, Vol.1, No.1, pp. 141-150, 2000, DOI:10.3970/cmes.2000.001.141

    Abstract Rapid prototyping tools that combine powerful numerics with a flexible applications interface can play a significant role in micro-scale modeling and simulation. We demonstrate this idea using the PROPHET simulator. In the first part of the investigations we extend the simulator's capability to allow analysis of carrier transport in deep submicron MOSFETs using a hydrodynamic model. The model is numerically implemented within PROPHET's dial-an-operator framework by adding certain "flux'' routines. Once implemented, the model becomes available for use in any number of spatial dimensions. We present results for MOSFET type test problems in one and More >

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