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, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world;
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to focus on data analytics and image processing to develop good intelligent systems. To this end, readers of this volume will find quality;
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This book is a collection of best selected research papers presented at International Conference on Intelligent and Smart Computing in Data;
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Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required;
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Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine;
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By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately;
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applications in intelligent computing, cloud storage, data mining and software analysis. It presents the outcomes of the third International Conference;
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reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine;
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Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;
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Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;
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by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data;
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databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare;
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algorithms; hybrid intelligent systems for data mining and applications; classification and cluster analysis, HAIS applications.;
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Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis;
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computing; learning algorithms; visual analysis and advanced data processing techniques; data mining applications; and hybrid intelligent;
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computation; learning algorithms; visual analysis and advanced data processing techniques; data mining applications; and hybrid intelligent;
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Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and;
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mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intelligent;
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mining, data science, intelligence for security, intelligent decision making systems, intelligent information processing, intelligent;
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assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and;
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assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and;
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and the Automated Modeler to the Data Manager and Social Network Analysis. Get predictive analysis working for you. Highlights include;
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and automation; intelligent data analysis and prediction; gene expression array analysis; gene regulation modeling and analysis; protein;
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and automation; intelligent data analysis and prediction; gene expression array analysis; gene regulation modeling and analysis; protein;
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Tools Algorithms for Big Data Big Data Management and Frameworks Big Data Search and Mining Methods Security and Privacy in the Era of Big;
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analysis and build predictive models on huge datasets that leverage Apache Spark * Learn to integrate data science algorithms and techniques with;
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intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine;
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