This book discusses the rainfall prediction. Rainfall prediction is an active topic for researchers to facilitate taking decision about crop and irrigation cycles in understanding weather and climate patterns. In the context of India's frequent changing climatic conditions the research on the rainfall prediction has gained lot of importance in recent days. In recent past rainfall prediction models using soft computing techniques have been very popular among the researchers however these models found to be complex and present poor generalization ability and generates less accuracy in prediction. This motivated to further investigate and improve the model development. In this work few machine learning techniques and evolutionary techniques are effectively used to obtain competitive results for long range rainfall prediction.
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Dr. Kavitha Rani is presently working as professor in Department of CSE at CMR Technical Campus, Hyderabad, India. She has published 15 papers in reputed National, International journals and conferences. Her area of interests is Data Mining, Machine learning, Deep Learning and Data Analytics.
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book discusses the rainfall prediction. Rainfall prediction is an active topic for researchers to facilitate taking decision about crop and irrigation cycles in understanding weather and climate patterns. In the context of India's frequent changing climatic conditions the research on the rainfall prediction has gained lot of importance in recent days. In recent past rainfall prediction models using soft computing techniques have been very popular among the researchers however these models found to be complex and present poor generalization ability and generates less accuracy in prediction. This motivated to further investigate and improve the model development. In this work few machine learning techniques and evolutionary techniques are effectively used to obtain competitive results for long range rainfall prediction. 132 pp. Englisch. Bestandsnummer des Verkäufers 9786139449446
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Anbieter: moluna, Greven, Deutschland
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Balmuri Kavitha RaniDr. Kavitha Rani is presently working as professor in Department of CSE at CMR Technical Campus, Hyderabad, India. She has published 15 papers in reputed National, International journals and conferences. Her area . Bestandsnummer des Verkäufers 280826897
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Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book discusses the rainfall prediction. Rainfall prediction is an active topic for researchers to facilitate taking decision about crop and irrigation cycles in understanding weather and climate patterns. In the context of India's frequent changing climatic conditions the research on the rainfall prediction has gained lot of importance in recent days. In recent past rainfall prediction models using soft computing techniques have been very popular among the researchers however these models found to be complex and present poor generalization ability and generates less accuracy in prediction. This motivated to further investigate and improve the model development. In this work few machine learning techniques and evolutionary techniques are effectively used to obtain competitive results for long range rainfall prediction.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Bestandsnummer des Verkäufers 9786139449446
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Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. New Approach Using Machine Learning Techniques for Rainfall Prediction | Kavitha Rani Balmuri (u. a.) | Taschenbuch | 132 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139449446 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 115846850
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book discusses the rainfall prediction. Rainfall prediction is an active topic for researchers to facilitate taking decision about crop and irrigation cycles in understanding weather and climate patterns. In the context of India's frequent changing climatic conditions the research on the rainfall prediction has gained lot of importance in recent days. In recent past rainfall prediction models using soft computing techniques have been very popular among the researchers however these models found to be complex and present poor generalization ability and generates less accuracy in prediction. This motivated to further investigate and improve the model development. In this work few machine learning techniques and evolutionary techniques are effectively used to obtain competitive results for long range rainfall prediction. Bestandsnummer des Verkäufers 9786139449446
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Anbieter: Buchpark, Trebbin, Deutschland
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book discusses the rainfall prediction. Rainfall prediction is an active topic for researchers to facilitate taking decision about crop and irrigation cycles in understanding weather and climate patterns. In the context of Indiäs frequent changing climatic conditions the research on the rainfall prediction has gained lot of importance in recent days. In recent past rainfall prediction models using soft computing techniques have been very popular among the researchers however these models found to be complex and present poor generalization ability and generates less accuracy in prediction. This motivated to further investigate and improve the model development. In this work few machine learning techniques and evolutionary techniques are effectively used to obtain competitive results for long range rainfall prediction. Bestandsnummer des Verkäufers 34027404/1
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