Weifeng Liu^1,2, Xin Yu^1,*, 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 - 31 October 2022

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 >

Guoqing Xu¹, Changsen Xia¹, Jun Qian¹, Guo Ran³, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066 - 28 March 2023

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 >

Savita Khurana¹, Gaurav Sharma², Neha Miglani³, Aman Singh⁴, Abdullah Alharbi⁵, Wael Alosaimi⁵, Hashem Alyami⁶, Nitin Goyal^7,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 629-649, 2022, DOI:10.32604/cmc.2022.021884 - 03 November 2021

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 >

Mashael Khayyat^1,*, 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 - 01 March 2021

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 >

Jingjian Chen¹, Gang Xu^{1, *}, Fengqi Wei¹, Liqiang He²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1845-1858, 2020, DOI:10.32604/cmc.2020.010010 - 30 June 2020

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 >

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 >

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 >