Titlebook: Data Science; 6th International Co Jing He,Philip S. Yu,Fu Xiao Conference proceedings 2020 Springer Nature Singapore Pte Ltd. 2020 artific

circuit

Prologue

https://doi.org/10.1007/978-3-031-44355-8k in multi-project environments. This paper proposes an integrated and efficient computational method based on multi-objective particle swarm optimization to solve these two interdependent problems simultaneously. Minimizing the project duration, cost and maximizing the quality of resource allocatio

吹牛大王

Aidan Murphy,Anthony Ventresque,Conor Ryano bicycle to borrow” and “no land to return”. Existing research, in response to the problem of unbalanced site demand, most scholars predict the demand for bicycle sites. In this study, the use of public bicycles at the site is analyzed from the perspective of simulation. The Arena simulation softwa

Organonitrile

Complex Computational Ecosystems models is also increasing at the same time, which brings great pressure to data storage and visualization. Therefore, it is necessary to simplify 3D models. In this paper, a three-step simplification method is proposed. Firstly, the geometric features of the building are used to extract the walls a

Odyssey

Conference proceedings 20061st editionnew products every month, so it is crucial for them to make decisions quickly and conveniently. In order to give advices to convenience stores, a comprehensive evaluation model of new product introduction in convenience stores based on multi-dimensional data is proposed. Firstly, based on theories o

Intruder

思想上升

搏斗

亞當心理陰影

H. Qudrat-Ullah,J.M. Spector,P.I. Davidsenrgy consumption, this paper applies a new model, NEWARMA model, which means to add the variable’s own medium- and long-term cyclical fluctuations item to the basic ARMA model, and the prediction accuracy will be significantly improved. This paper also compares fitting result of NEWARMA to neural net

忘川河

H. Qudrat-Ullah,J.M. Spector,P.I. Davidsenited by multiple factors, making it difficult to accurately identify poor targets in a timely manner. The development of power big data provides the possibility to use energy consumption data to locate and identify poor areas. Therefore, this article takes Jiangxi Province as an example to analyze 2