Titlebook: Machine Learning Techniques for Smart City Applications: Trends and Solutions; D. Jude Hemanth Book 2022 The Editor(s) (if applicable) and

Bunion

書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions影響因子(影響力)




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions影響因子(影響力)學(xué)科排名




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions網(wǎng)絡(luò)公開度




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions被引頻次




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions被引頻次學(xué)科排名




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions年度引用




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions年度引用學(xué)科排名




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions讀者反饋




書目名稱Machine Learning Techniques for Smart City Applications: Trends and Solutions讀者反饋學(xué)科排名

largesse

小歌劇

albuminuria

D. Jude HemanthPresents an in-depth coverage on different variety of applications related with smart cities.Maximizes reader’s insights about the role of machine learning in different applications of smart cities.In

失望昨天

Advances in Science, Technology & Innovationhttp://image.papertrans.cn/m/image/620427.jpg

神經(jīng)

Eulogy

2522-8714 achine learning in different applications of smart cities.In.This book discusses the application of different machine learning techniques to the sub-concepts of smart cities such as smart energy, transportation, waste management, health, infrastructure, etc. The focus of this book is to come up with

Infuriate

fatty-streak

Applying Deep Learning to Predict Civic Purpose Development: Within the Smart City Context,model the non-linear relationship between the predictors and outcome variables. Based on the findings, we discussed the implications of the present study within the context of improving citizens’ lives in smart cities.

habitat

Emergency Department Management Using Regression Models,ctor regression are considered for the prediction of the patient flow in Emergency Departments. The hazardous COVID-19 pandemic and its impact on the mounting crisis in the EDs is also discussed. The challenges and suggestive methods are also discussed here.