lokesh kumar sharma (30 resultados)

Condición: New. pp. 268.

Condición: New. pp. 268.

Condición: New. pp. 268.

Condición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Hardcover. Condición: New. ISBN:9789390425464.

Paperback. Condición: Brand New. 116 pages. 8.58x5.83x0.31 inches. In Stock.

Taschenbuch. Condición: Neu. Descriptive Modelling and Pattern Discovery in Spatial Data Mining | Regionalisation and Association Rule Mining | Lokesh Kumar Sharma | Taschenbuch | 116 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846592151 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced.

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Hardcover. Condición: new. Hardcover. OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application;emphasizes validating and evaluating predictive models;provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare;highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condición: new.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Hardcover. Condición: new. Hardcover. OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application;emphasizes validating and evaluating predictive models;provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare;highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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

Condición: New. 2024. 1st Edition. hardcover. . . . . .

Hardcover. Condición: Brand New. 350 pages. 9.33x9.02x0.94 inches. In Stock.

Condición: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condición: new. Hardcover. OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application;emphasizes validating and evaluating predictive models;provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare;highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book includes selected papers presented at 4th International Conference on Intelligent Vision and Computing (ICIVC 2024), held at National Institute of Technology, Agartala, India, during 23 24 November 2024. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, and processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human-computer interaction.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced. 116 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma Lokesh KumarDr. Sharma received his Ph. D. degree from Pt. Ravishankar Shukla University, Raipur-India. Dr. Sharma is a DAAD Fellow and Former member of Knowledge Discovery Department, Fraunhofer IAIS St. Augustin Germany. He .

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The explosive growth of spatial data and the widespread use of spatial databases put emphasis on the extraction of interesting and implicit knowledge such as the spatial pattern or other significant mode not explicitly stored in the spatial databases. Knowledge discovery in large spatial database is important for the extraction of implicit knowledge. Spatial relations or other patterns are not explicitly stored in spatial database. Traditional Data mining techniques are not efficient and effective to mine the spatial data due to its unique features such as spatial dependency, heterogeneity, spatially aggregated data etc. Thus, new and efficient mining methods are needed to discover knowledge from large spatial databases. A descriptive modeling technique for georeferenced data is discussed and it is also used to solve the regionalization problem. Multi Level Multi Dimensional is an important aspect for Spatial Data. The Multi Level Multi Dimensional pattern discovery on spatial data is presented here. Mining the trajectory data or mobility data is an emerging area of research. The Trajectory data classifier which is based on the Nearest Neighbor is introduced.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book includes selected papers presented at 4th International Conference on Intelligent Vision and Computing (ICIVC 2024), held at National Institute of Technology, Agartala, India, during 23 24 November 2024. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, and processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human-computer interaction. 364 pp. Englisch.

Buch. Condición: Neu. Intelligent Vision and Computing | Proceedings of ICIVC 2024 | Apu Kumar Saha (u. a.) | Buch | xviii | Englisch | 2025 | Springer | EAN 9789819647217 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book includes selected papers presented at 4th International Conference on Intelligent Vision and Computing (ICIVC 2024), held at National Institute of Technology, Agartala, India, during 2324 November 2024. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, and processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human-computer interaction.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 364 pp. Englisch.