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Añadir al carritoPaperback. Condición: Brand New. 216 pages. 8.66x5.91x0.49 inches. In Stock.
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Añadir al carritoTaschenbuch. Condición: Neu. Machine Learning Algorithms for Prediction of Indian Summer Monsoon | Moumita Saha (u. a.) | Taschenbuch | 216 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659956522 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
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Publicado por LAP Lambert Academic Publishing Okt 2016, 2016
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Indian summer monsoon is a complex and non-linear climatic phenomenon. India being an agricultural country, the monsoon governs the pulse of life for its mankind along with the flora-fauna existing in the subcontinent. It has large impact on agriculture, renewal of fresh-water, and generation of hydro-electricity power. Monsoon is the driving force for development of economy of the country. An attempt is made to study, analyse, and predict Indian summer monsoon. Different machine learning algorithms are used for these purposes. Focus is towards identification of new predictors from different climatic variables influencing the monsoon of the sub-continent and improvement of monsoon prediction models to forecast monsoon with greater accuracy. Various clustering, community detection, and deep learning approaches are utilised to attain the goal. 216 pp. Englisch.
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Publicado por LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Saha MoumitaMoumita Saha has completed her PhD degree from Computer Science & Engineering at Indian Institute of Technology Kharagpur in 2016. She has received her M.Tech in Computer Science & Technology from BESU Shibpur in 2012 and.
Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
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Publicado por LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Indian summer monsoon is a complex and non-linear climatic phenomenon. India being an agricultural country, the monsoon governs the pulse of life for its mankind along with the flora-fauna existing in the subcontinent. It has large impact on agriculture, renewal of fresh-water, and generation of hydro-electricity power. Monsoon is the driving force for development of economy of the country. An attempt is made to study, analyse, and predict Indian summer monsoon. Different machine learning algorithms are used for these purposes. Focus is towards identification of new predictors from different climatic variables influencing the monsoon of the sub-continent and improvement of monsoon prediction models to forecast monsoon with greater accuracy. Various clustering, community detection, and deep learning approaches are utilised to attain the goal.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 216 pp. Englisch.
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Publicado por LAP Lambert Academic Publishing, 2016
ISBN 10: 365995652X ISBN 13: 9783659956522
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Indian summer monsoon is a complex and non-linear climatic phenomenon. India being an agricultural country, the monsoon governs the pulse of life for its mankind along with the flora-fauna existing in the subcontinent. It has large impact on agriculture, renewal of fresh-water, and generation of hydro-electricity power. Monsoon is the driving force for development of economy of the country. An attempt is made to study, analyse, and predict Indian summer monsoon. Different machine learning algorithms are used for these purposes. Focus is towards identification of new predictors from different climatic variables influencing the monsoon of the sub-continent and improvement of monsoon prediction models to forecast monsoon with greater accuracy. Various clustering, community detection, and deep learning approaches are utilised to attain the goal.