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The Data mining technique can be used in collaboration With a K-NN classifiers, Naïve Bayes, Random Forest, Support Vector Machine algorithms which used in diagnosing Swine flu disease virus affected across different persons based on their Symptoms. This proposed approach showed some Promising results which may lead to further attempts to utilize information technology for diagnosing virus from which patients are suffering from Swine flu. Here we used K-NN Classification, Naïve Bayes, Random Forest, SVM rules which are easy to interpret. In future, we will try to get more the accuracy results for the swine flu disease which helps to find different parameters using different data mining techniques as per suggested by doctors.
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ANALYSIS ON PREDICTION OF SWINE FLU USING MACHINE LEARNING ALGORITHMS
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LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202672390
ISBN 13: 9786202672399
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Y. DeepikaMs.Y.Deepika had completed her Under Graduation recently at Vignan s Institute of Information Technology, Visakhapatnam. Dr.N.Thirupathi Rao is currently associated with Vignan s Institute of Information Technology, Visakha. Codice articolo 494630694
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ANALYSIS ON PREDICTION OF SWINE FLU USING MACHINE LEARNING ALGORITHMS
Editore:
LAP LAMBERT Academic Publishing Jun 2020, 2020
ISBN 10: 6202672390
ISBN 13: 9786202672399
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Data mining technique can be used in collaboration With a K-NN classifiers, Naïve Bayes, Random Forest, Support Vector Machine algorithms which used in diagnosing Swine flu disease virus affected across different persons based on their Symptoms. This proposed approach showed some Promising results which may lead to further attempts to utilize information technology for diagnosing virus from which patients are suffering from Swine flu. Here we used K-NN Classification, Naïve Bayes, Random Forest, SVM rules which are easy to interpret. In future, we will try to get more the accuracy results for the swine flu disease which helps to find different parameters using different data mining techniques as per suggested by doctors. 76 pp. Englisch. Codice articolo 9786202672399
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ANALYSIS ON PREDICTION OF SWINE FLU USING MACHINE LEARNING ALGORITHMS
Editore:
LAP LAMBERT Academic Publishing Jun 2020, 2020
ISBN 10: 6202672390
ISBN 13: 9786202672399
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -The Data mining technique can be used in collaboration With a K-NN classifiers, Naïve Bayes, Random Forest, Support Vector Machine algorithms which used in diagnosing Swine flu disease virus affected across different persons based on their Symptoms. This proposed approach showed some Promising results which may lead to further attempts to utilize information technology for diagnosing virus from which patients are suffering from Swine flu. Here we used K-NN Classification, Naïve Bayes, Random Forest, SVM rules which are easy to interpret. In future, we will try to get more the accuracy results for the swine flu disease which helps to find different parameters using different data mining techniques as per suggested by doctors.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Codice articolo 9786202672399
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ANALYSIS ON PREDICTION OF SWINE FLU USING MACHINE LEARNING ALGORITHMS
Editore:
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202672390
ISBN 13: 9786202672399
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Data mining technique can be used in collaboration With a K-NN classifiers, Naïve Bayes, Random Forest, Support Vector Machine algorithms which used in diagnosing Swine flu disease virus affected across different persons based on their Symptoms. This proposed approach showed some Promising results which may lead to further attempts to utilize information technology for diagnosing virus from which patients are suffering from Swine flu. Here we used K-NN Classification, Naïve Bayes, Random Forest, SVM rules which are easy to interpret. In future, we will try to get more the accuracy results for the swine flu disease which helps to find different parameters using different data mining techniques as per suggested by doctors. Codice articolo 9786202672399
Quantità: 1 disponibili