Rana Dipti (31 results)

Condition: New. 1st edition NO-PA16APR2015-KAP.

Paperback. Condition: Brand New. 168 pages. 8.66x5.91x0.38 inches. In Stock.

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Paperback. Condition: New. Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Condition: New. pp. 336.

Paperback. Condition: New. Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world application areas like finance, health, network, news, online advertisement, social network media, and weather have imbalanced data, which emphasizes the research necessity for real-time implications of precise fraud/defaulter detection, rare disease/reaction prediction, network intrusion detection, fake news detection, fraud advertisement detection, cyber bullying identification, disaster events prediction, and more. Machine learning algorithms are based on the heuristic of equally-distributed balanced data and provide the biased result towards the majority data class, which is not acceptable considering imbalanced data is omnipresent in real-life scenarios and is forcing us to learn from imbalanced data for foolproof application design. Imbalanced data is multifaceted and demands a new perception using the novelty at sampling approach of data preprocessing, an active learning approach, and a cost perceptive approach to resolve data imbalance. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance offers new aspects for imbalanced data learning by providing the advancements of the traditional methods, with respect to big data, through case studies and research from experts in academia, engineering, and industry. The chapters provide theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches. This book is ideal for data scientists, data analysts, engineers, practitioners, researchers, academicians, and students looking for more information on imbalanced data characteristics and solutions using varied approaches.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -From the starting era of computers, computerized systems are extensively utilized in weather forecasting. Due to the boom of internet and online environment of sensors and technology, the huge and variety of weather data is available for prediction. Traditional weather prediction approaches are no longer adequate with respect to time. Thus research work for weather attracts new generation of researchers to achieve a new metric of success by the fast, accurate and efficient solution by inclusion of indirect weather data, novelty and automation. This book, therefore, provides a new metric of success for temporal weather data prediction and supplemental parameters to consider in the traditional weather analysis for accurate prediction. The book covers the analysis and explanation to prepare the models gradually using geo-spatial temporal weather data. The analysis should help shed some light on exciting weather environment and mining. The book should be especially useful to professionals in weather forecasting and researchers, or anyone else who may be considering the research on online weather data using data mining, machine learning, etc 168 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Prakashkumar Rana DiptiDr. Dipti Prakashkumar Rana, is a faculty member at Computer Engineering Department, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, India. She is having teaching experience of more than 17 .

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. Temporal Association Rule Based Models for Weather Prediction | Concept and Stepwise Model Preparation | Dipti Prakashkumar Rana (u. a.) | Taschenbuch | 168 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202076166 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -From the starting era of computers, computerized systems are extensively utilized in weather forecasting. Due to the boom of internet and online environment of sensors and technology, the huge and variety of weather data is available for prediction. Traditional weather prediction approaches are no longer adequate with respect to time. Thus research work for weather attracts new generation of researchers to achieve a new metric of success by the fast, accurate and efficient solution by inclusion of indirect weather data, novelty and automation. This book, therefore, provides a new metric of success for temporal weather data prediction and supplemental parameters to consider in the traditional weather analysis for accurate prediction. The book covers the analysis and explanation to prepare the models gradually using geo-spatial temporal weather data. The analysis should help shed some light on exciting weather environment and mining. The book should be especially useful to professionals in weather forecasting and researchers, or anyone else who may be considering the research on online weather data using data mining, machine learning, etcBooks on Demand GmbH, Überseering 33, 22297 Hamburg 168 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - From the starting era of computers, computerized systems are extensively utilized in weather forecasting. Due to the boom of internet and online environment of sensors and technology, the huge and variety of weather data is available for prediction. Traditional weather prediction approaches are no longer adequate with respect to time. Thus research work for weather attracts new generation of researchers to achieve a new metric of success by the fast, accurate and efficient solution by inclusion of indirect weather data, novelty and automation. This book, therefore, provides a new metric of success for temporal weather data prediction and supplemental parameters to consider in the traditional weather analysis for accurate prediction. The book covers the analysis and explanation to prepare the models gradually using geo-spatial temporal weather data. The analysis should help shed some light on exciting weather environment and mining. The book should be especially useful to professionals in weather forecasting and researchers, or anyone else who may be considering the research on online weather data using data mining, machine learning, etc.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides theoretical frameworks and the latest empirical research findings that help to improve the understanding of the impact of imbalanced data and its resolving techniques based on data preprocessing, active learning, and cost perceptive approaches.

HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand pp. 336.

Taschenbuch. Condition: Neu. Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance | Dipti P. Rana (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Engineering Science Reference | EAN 9781799873723 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Condition: New. PRINT ON DEMAND pp. 336.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers new aspects for imbalanced data learning by providing the advancements of the traditional methods with respect to big data through case studies and research. The book provides theoretical frameworks and the latest empirical research findings that hel.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'This edited book provides a selection of chapters to improve the understanding of the impact of imbalanced data and its resolving techniques based on the Data Preprocessing, Active Learning, and Cost Perceptive Approaches'.