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Advances of machine learning in clean energy and the transportation industry /
"This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Nova Science Publishers,
[2020]
|
| Colección: | Computer science, technology and applications
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a22000008i 4500 | ||
|---|---|---|---|
| 001 | EBSCO_on1286676065 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 211030s2020 nju ob 001 0 eng | ||
| 010 | |a 2021051976 | ||
| 040 | |a DLC |b eng |e rda |c DLC |d OCLCO |d DLC |d OCLCF |d OCLCO |d N$T |d OCLCQ | ||
| 019 | |a 1286209845 | ||
| 020 | |a 9781685072117 |q (adobe pdf) | ||
| 020 | |a 1685072119 | ||
| 020 | |a 1685073034 | ||
| 020 | |a 9781685073039 |q (electronic bk.) | ||
| 035 | |a (OCoLC)1286676065 |z (OCoLC)1286209845 | ||
| 042 | |a pcc | ||
| 050 | 0 | 0 | |a TJ808 |
| 082 | 0 | 0 | |a 333.79/4 |2 23/eng/20211123 |
| 049 | |a UAMI | ||
| 245 | 0 | 0 | |a Advances of machine learning in clean energy and the transportation industry / |c Pandian Vasant, (editor), Valeriy Kharchenko, (editor), J. Joshua Thomas, (editor), Gerhard-Wilhelm Weber, (editor), Vladimir Panchenko, (editor). |
| 263 | |a 2201 | ||
| 264 | 1 | |a New York : |b Nova Science Publishers, |c [2020] | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Computer science, technology and applications | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a RES-based multipurpose plant for hydrogen production / Vytautas Adomavicius -- Developing a Bayesian network to model environmental, organizational, and human risk factors : a case study on wind turbines / Maryam Ashrafi -- Digital technologies for the implementation of intelligent diagnostics of the insulation of power supply systems with insulated neutral in operating mode / Svetlana Ovchukova, Nadezhda Kondrateva and Andrey Shishov -- Irrigation system of agricultural fields with the use of solar energy / Leonid Yuferev and Alexander Parakhnich -- Strategies hybrid simulation for regional market development of renewable energy / P.N. Kuznetsov, D. Yu. Voronin, L. Yu Yuferev, and V.P. Evstigneev -- RES-based power plants versus polluting power plants : pros and cons / Vytautas Adomavicius -- A comprehensive study of system building blocks for radio frequency energy harvesting / Bhuvnesh Khantwal, Reeta Verma and Paras -- The management of community participation in rural infrastructure development in the Mekong River Delta, Vietnam / Nguyen Xuan Quyet, Pham Thi My Dung, Duc Anh Nguyen, Dinh Tuan Hai and Nguyen Viet Luan -- Warning system for cracked pipes in autonomous vehicles / Yair Wiseman -- Contribution of machine learning to rail transport safety / Habib Hadj-Mabrouk -- The power of variable freeing and variable sum bounds in solving the linear knapsack problem / Elias Munapo. | |
| 520 | |a "This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe. Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data. The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change"-- |c Provided by publisher. | ||
| 588 | |a Description based on print version record and CIP data provided by publisher; resource not viewed. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Renewable energy sources |x Data processing. | |
| 650 | 0 | |a Clean energy industries |x Data processing. | |
| 650 | 0 | |a Transportation |x Data processing. | |
| 650 | 0 | |a Machine learning |x Industrial applications. | |
| 650 | 0 | |a Artificial intelligence |x Industrial applications. | |
| 650 | 6 | |a Énergies renouvelables |x Informatique. | |
| 650 | 6 | |a Énergies propres |x Industrie |x Informatique. | |
| 650 | 6 | |a Apprentissage automatique |x Applications industrielles. | |
| 650 | 6 | |a Intelligence artificielle |x Applications industrielles. | |
| 650 | 7 | |a Artificial intelligence |x Industrial applications. |2 fast |0 (OCoLC)fst00817262 | |
| 650 | 7 | |a Machine learning |x Industrial applications. |2 fast |0 (OCoLC)fst01004799 | |
| 650 | 7 | |a Renewable energy sources |x Data processing. |2 fast |0 (OCoLC)fst01094575 | |
| 650 | 7 | |a Transportation |x Data processing. |2 fast |0 (OCoLC)fst01155037 | |
| 700 | 1 | |a Vasant, Pandian, |e editor. | |
| 700 | 1 | |a Kharchenko, Valeriy, |d 1938- |e editor. | |
| 700 | 1 | |a Thomas, J. Joshua, |d 1973- |e editor. | |
| 700 | 1 | |a Weber, Gerhard-Wilhelm, |e editor. | |
| 700 | 1 | |a Panchenko, Vladimir |c (Professor of transportation engineering), |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Advances of machine learning in clean energy and the transportation industry |d New York : Nova Science Publishers, [2020] |z 9781685072117 |w (DLC) 2021051975 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3059661 |z Texto completo |
| 938 | |a EBSCOhost |b EBSC |n 3059661 | ||
| 994 | |a 92 |b IZTAP | ||