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Author:
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Category :
Languages : en
Pages : 0
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Author:
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ISBN:
Category :
Languages : en
Pages : 0
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Author:
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ISBN: 9781450390248
Category : Computer science
Languages : en
Pages : 481
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ISBN: 9781665498685
Category :
Languages : en
Pages : 0
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Author: IEEE Staff
Publisher:
ISBN: 9781665498692
Category :
Languages : en
Pages : 0
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Book Description
Databases,Big Data,Data Mining,Educational Data Mining,Parallel and Distributed Data mining Algorithms,Data Streams Mining,Graph Mining,Spatial Data Mining,Multimedia Data Mining,Data and Information Networks,Data and Information Privacy and Security,Data and Information Semantics,Data Management in Grid,Data Mining Algorithms,Data Mining Systems, Data Warehousing,Data Structures and Data Management Algorithms,Database and Information System Architecture,Sensor Data Management,Statistical and Scientific Databases,Temporal, Spatial, and High Dimensional Databases,Data and Information Quality,Data and Information Streams,Deductive Databases,Database Technology,Data Modeling and Engineering,IT Applications for Agriculture Management,IT Software Development and Methodology,Information Retrieval and Database Systems,Security and Information Assurance,Information Technology Applications
Author: Dr.M.Thangaraj
Publisher: Shanlax Publications
ISBN: 9391373046
Category : Computers
Languages : en
Pages : 168
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Book Description
Author: Aboul Ella Hassanien
Publisher: Springer Nature
ISBN: 9813343672
Category : Technology & Engineering
Languages : en
Pages : 922
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Book Description
This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of things (IoT) and information security.
Author: Prasant Kumar Pattnaik
Publisher: Springer Nature
ISBN: 9819705738
Category :
Languages : en
Pages : 642
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Book Description
Author: I. Jeena Jacob
Publisher: Springer Nature
ISBN: 9811960046
Category : Technology & Engineering
Languages : en
Pages : 901
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Book Description
The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 6–7, 2022. This book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems.
Author: Lin Zhang
Publisher: Springer Nature
ISBN: 9819739489
Category :
Languages : en
Pages : 295
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Book Description
Author: Sandeep Kumar
Publisher: John Wiley & Sons
ISBN: 1394174624
Category : Computers
Languages : en
Pages : 388
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Book Description
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.
