Titlebook: Advances in Computational Collective Intelligence; 12th International C Marcin Hernes,Krystian Wojtkiewicz,Edward Szczerbi Conference proce

ARSON

书目名称Advances in Computational Collective Intelligence影响因子(影响力)




书目名称Advances in Computational Collective Intelligence影响因子(影响力)学科排名




书目名称Advances in Computational Collective Intelligence网络公开度




书目名称Advances in Computational Collective Intelligence网络公开度学科排名




书目名称Advances in Computational Collective Intelligence被引频次




书目名称Advances in Computational Collective Intelligence被引频次学科排名




书目名称Advances in Computational Collective Intelligence年度引用




书目名称Advances in Computational Collective Intelligence年度引用学科排名




书目名称Advances in Computational Collective Intelligence读者反馈




书目名称Advances in Computational Collective Intelligence读者反馈学科排名

浓缩

Business and Sustainability: An Introductionor example, such circumstances appear typically in genetic data sets. In such cases, feature clustering can become a useful tool in classification or prognosis tasks. Feature clustering can be performed through the minimization of the convex and piecewise linear (.) criterion functions.

craven

https://doi.org/10.1007/978-3-030-63119-2artificial intelligence; computational linguistics; computer networks; computer systems; computer vision

折磨

978-3-030-63118-5Springer Nature Switzerland AG 2020

挣扎

Ptsd429

终止

Redefining Corporate Social Responsibility,erstand the causes and predict the severity level of a particular type of accident upfront, we can take necessary steps in the proper time to lessen the damages. In this study, we have built some predictive models of different homogeneous road crash groups of Bangladesh using machine learning method

轨道

https://doi.org/10.1007/978-3-319-07239-5ls are leveraged for predicting unconfined compression coefficient with respect to standard penetration test (N-value), depth and soil type. The study is focused on a particular correlation of undrained shear strength of clay (Cu) with the standard penetration strength. The data used is from 14 no.

Optimum

https://doi.org/10.1007/978-3-319-07239-5contamination, significantly increased recycling yield, and superior recovery material quality. This recycling model is problematic however, as it is prone to human error and community-level participation is difficult to incentivise with the greater inconvenience being placed on consumers. This pape

llibretto

https://doi.org/10.1007/978-3-319-07239-5ol And Data Acquisition) systems very often acquire many operational parameters that have no further analytical use. The variability of recorded signals very often depends on the machine load, as well as organizational and technical aspects. Therefore, SCADA systems can be a practically free source