001446157 000__ 05077cam\a2200565Ii\4500
001446157 001__ 1446157
001446157 003__ OCoLC
001446157 005__ 20230310003942.0
001446157 006__ m\\\\\o\\d\\\\\\\\
001446157 007__ cr\un\nnnunnun
001446157 008__ 220426s2022\\\\sz\a\\\\o\\\\\000\0\eng\d
001446157 020__ $$a9783030964290$$q(electronic bk.)
001446157 020__ $$a3030964299$$q(electronic bk.)
001446157 020__ $$z9783030964283$$q(print)
001446157 0247_ $$a10.1007/978-3-030-96429-0$$2doi
001446157 035__ $$aSP(OCoLC)1312645494
001446157 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001446157 049__ $$aISEA
001446157 050_4 $$aQ342
001446157 08204 $$a006.3$$223/eng/20220426
001446157 24500 $$aComputational intelligence techniques for green smart cities /$$cMohamed Lahby, Ala Al-Fuqaha, Yassine Maleh, editors.
001446157 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001446157 300__ $$a1 online resource (xii, 419 pages) :$$billustrations (some color).
001446157 336__ $$atext$$btxt$$2rdacontent
001446157 337__ $$acomputer$$bc$$2rdamedia
001446157 338__ $$aonline resource$$bcr$$2rdacarrier
001446157 4901_ $$aGreen energy and technology,$$x1865-3537
001446157 5050_ $$aRecent 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.
001446157 506__ $$aAccess limited to authorized users.
001446157 520__ $$aThis 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.
001446157 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 26, 2022).
001446157 650_0 $$aComputational intelligence.
001446157 650_0 $$aSmart cities.
001446157 650_0 $$aMachine learning.
001446157 650_0 $$aInternet of things.
001446157 650_6 $$aIntelligence informatique.
001446157 650_6 $$aVilles intelligentes.
001446157 650_6 $$aApprentissage automatique.
001446157 650_6 $$aInternet des objets.
001446157 655_0 $$aElectronic books.
001446157 7001_ $$aLahby, Mohamed,$$eeditor.$$0(orcid)0000-0002-8272-0487$$1https://orcid.org/0000-0002-8272-0487
001446157 7001_ $$aAl-Fuqaha, Ala,$$eeditor.$$1https://orcid.org/0000-0002-0903-1204
001446157 7001_ $$aMaleh, Yassine,$$d1987-$$eeditor.$$1https://orcid.org/0000-0003-4704-5364
001446157 830_0 $$aGreen energy and technology,$$x1865-3537
001446157 852__ $$bebk
001446157 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-96429-0$$zOnline Access$$91397441.1
001446157 909CO $$ooai:library.usi.edu:1446157$$pGLOBAL_SET
001446157 980__ $$aBIB
001446157 980__ $$aEBOOK
001446157 982__ $$aEbook
001446157 983__ $$aOnline
001446157 994__ $$a92$$bISE