國立虎尾科技大學 |

Computational Intelligence Techniques for Green Smart Cities

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational Intelligence Techniques for Green Smart Cities/ edited by Mohamed Lahby, Ala Al-Fuqaha, Yassine Maleh.
其他作者:	Lahby, Mohamed.
面頁冊數:	XII, 419 p. 203 illus., 172 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Sustainability. -
電子資源:	https://doi.org/10.1007/978-3-030-96429-0
ISBN:	9783030964290

Computational Intelligence Techniques for Green Smart Cities
Computational Intelligence Techniques for Green Smart Cities[electronic resource] /edited by Mohamed Lahby, Ala Al-Fuqaha, Yassine Maleh. - 1st ed. 2022. - XII, 419 p. 203 illus., 172 illus. in color.online resource. - Green Energy and Technology,1865-3537. - Green Energy and Technology,.

Recent trends of Artificial Intelligence Techniques -- An Overview of Smart Green Cities Based on XAI Machine Learning for Green Smart Health -- Deep Learning Models for Green Smart Health -- On natural language processing to attack COVID-19 pandemic -- Evolutionary Algorithms for Smart Green Transportation -- Analysis and design of the Bus Transport Network -- Traffic sign detection system for Smart City Transportation -- Green Smart Transportation solutions for combating Covid-19 -- The Utilization of Forecasting Methods of Solar Radiation -- Machine learning for Green Smart Environment -- Deep learning for green smart environment -- Machine Learning & Fuzzy Technique for Environmental Time Series -- A new Fuzzy Clustering Algorithm based on Maximum Likelihood Estimation -- Optimal Environmental-Economic Scheduling of a smart home -- Smart Home Application based on Evolutionary Algorithm: a Transfer Learning Approach -- Machine learning for Green Home -- Machine learning for green smart video surveillance -- Green Learning Solutions for Human Trafficking Victims in Rural Communities During the COVID-19 -- A comparative analysis on object detection accuracy of cloud-based image processing services.

This book contains high-quality and original research on computational intelligence for green smart cities research. In recent years, the use of smart city technology has rapidly increased through the successful development and deployment of Internet of Things (IoT) architectures. The citizens' quality of life has been improved in several sensitive areas of the city, such as transportation, buildings, health care, education, environment, and security, thanks to these technological advances Computational intelligence techniques and algorithms enable a computational analysis of enormous data sets to reveal patterns that recur. This information is used to inform and improve decision-making at the municipal level to build smart computational intelligence techniques and sustainable cities for their citizens. Machine intelligence allows us to identify trends (patterns). The smart city could better integrate its transportation network, for example. By offering a better public transportation network adapted to the demand, we could reduce personal vehicles and energy consumption. A smart city could use models to predict the consequences of a change, such as pedestrianizing a street or adding a bike lane. A city can even create a 3D digital twin to test hypothetical projects. This book comprises many state-of-the-art contributions from scientists and practitioners working in machine intelligence and green smart cities. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances in machine intelligence for green and sustainable smart city applications.

ISBN: 9783030964290

Standard No.: 10.1007/978-3-030-96429-0doiSubjects--Topical Terms:

793436
Sustainability.

LC Class. No.: GE196

Dewey Class. No.: 304.2

Computational Intelligence Techniques for Green Smart Cities
LDR:04348nam a22004095i 4500 001 1093119
003 DE-He213
005 20220422100609.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030964290 $9 978-3-030-96429-0
024 7 $a 10.1007/978-3-030-96429-0 $2 doi
035 $a 978-3-030-96429-0
050 4 $a GE196
072 7 $a RNU $2 bicssc
072 7 $a SCI026000 $2 bisacsh
072 7 $a RNU $2 thema
082 0 4 $a 304.2 $2 23
245 1 0 $a Computational Intelligence Techniques for Green Smart Cities $h [electronic resource] / $c edited by Mohamed Lahby, Ala Al-Fuqaha, Yassine Maleh.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XII, 419 p. 203 illus., 172 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Green Energy and Technology, $x 1865-3537
505 0 $a Recent trends of Artificial Intelligence Techniques -- An Overview of Smart Green Cities Based on XAI Machine Learning for Green Smart Health -- Deep Learning Models for Green Smart Health -- On natural language processing to attack COVID-19 pandemic -- Evolutionary Algorithms for Smart Green Transportation -- Analysis and design of the Bus Transport Network -- Traffic sign detection system for Smart City Transportation -- Green Smart Transportation solutions for combating Covid-19 -- The Utilization of Forecasting Methods of Solar Radiation -- Machine learning for Green Smart Environment -- Deep learning for green smart environment -- Machine Learning & Fuzzy Technique for Environmental Time Series -- A new Fuzzy Clustering Algorithm based on Maximum Likelihood Estimation -- Optimal Environmental-Economic Scheduling of a smart home -- Smart Home Application based on Evolutionary Algorithm: a Transfer Learning Approach -- Machine learning for Green Home -- Machine learning for green smart video surveillance -- Green Learning Solutions for Human Trafficking Victims in Rural Communities During the COVID-19 -- A comparative analysis on object detection accuracy of cloud-based image processing services.
520 $a This book contains high-quality and original research on computational intelligence for green smart cities research. In recent years, the use of smart city technology has rapidly increased through the successful development and deployment of Internet of Things (IoT) architectures. The citizens' quality of life has been improved in several sensitive areas of the city, such as transportation, buildings, health care, education, environment, and security, thanks to these technological advances Computational intelligence techniques and algorithms enable a computational analysis of enormous data sets to reveal patterns that recur. This information is used to inform and improve decision-making at the municipal level to build smart computational intelligence techniques and sustainable cities for their citizens. Machine intelligence allows us to identify trends (patterns). The smart city could better integrate its transportation network, for example. By offering a better public transportation network adapted to the demand, we could reduce personal vehicles and energy consumption. A smart city could use models to predict the consequences of a change, such as pedestrianizing a street or adding a bike lane. A city can even create a 3D digital twin to test hypothetical projects. This book comprises many state-of-the-art contributions from scientists and practitioners working in machine intelligence and green smart cities. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances in machine intelligence for green and sustainable smart city applications.
650 0 $a Sustainability. $3 793436
650 0 $a Electric power distribution. $3 572880
650 0 $a Architecture. $3 555123
650 0 $a Renewable energy sources. $3 558536
650 0 $a Telecommunication. $3 568341
650 2 4 $a Energy Grids and Networks. $3 1387642
650 2 4 $a Cities, Countries, Regions. $3 676394
650 2 4 $a Renewable Energy. $3 1151178
650 2 4 $a Communications Engineering, Networks. $3 669809
700 1 $a Lahby, Mohamed. $e editor. $1 https://orcid.org/0000-0002-8272-0487 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1399480
700 1 $a Al-Fuqaha, Ala. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1254581
700 1 $a Maleh, Yassine. $e editor. $1 https://orcid.org/0000-0003-4704-5364 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1349665
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030964283
776 0 8 $i Printed edition: $z 9783030964306
776 0 8 $i Printed edition: $z 9783030964313
830 0 $a Green Energy and Technology, $x 1865-3529 $3 1254274
856 4 0 $u https://doi.org/10.1007/978-3-030-96429-0
912 $a ZDB-2-ENE
912 $a ZDB-2-SXEN
950 $a Energy (SpringerNature-40367)
950 $a Energy (R0) (SpringerNature-43717)