About the BookThe book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trendsTarget Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.The book offers a comprehensive coverage of some of the most essential topics: It provides an insight on building smart sustainable solutions. Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing. Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research. Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains. Encompasses comparison and analysis of recent technologies and trends. Includes development of smart models for information gain and effective decision making with visualization.The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.The book offers a comprehensive coverage of some of the most essential topics: It provides an insight on building smart sustainable solutions. Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing. Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research. Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains. Encompasses comparison and analysis of recent technologies and trends. Includes development of smart models for information gain and effective decision making with visualization.The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

About the Book

The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.

Salient Features of the Book

Exhaustive coverage of Data Analysis using R

Real-life healthcare models for:

Visually Impaired

Disease Diagnosis and Treatment options

Applications of Big Data and Deep Learning in Healthcare

Drug Discovery

Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications

Compare and analyze recent healthcare technologies and trends

Target Audience

This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

About the Book

The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.

Salient Features of the Book

Exhaustive coverage of Data Analysis using R

Real-life healthcare models for:

Visually Impaired

Disease Diagnosis and Treatment options

Applications of Big Data and Deep Learning in Healthcare

Drug Discovery

Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications

Compare and analyze recent healthcare technologies and trends

Target Audience

This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.

The book offers a comprehensive coverage of some of the most essential topics:

It provides an insight on building smart sustainable solutions.

Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing.

Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research.

Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains.

Encompasses comparison and analysis of recent technologies and trends.

Includes development of smart models for information gain and effective decision making with visualization.

The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.

The book offers a comprehensive coverage of some of the most essential topics:

It provides an insight on building smart sustainable solutions.

Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing.

Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research.

Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains.

Encompasses comparison and analysis of recent technologies and trends.

Includes development of smart models for information gain and effective decision making with visualization.

The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readersProvides complete code solutions to every problem presented, including detailed answers to even the most complex questionsPresents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contextsOffers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketingFocuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readersProvides complete code solutions to every problem presented, including detailed answers to even the most complex questionsPresents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contextsOffers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketingFocuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.

Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readers Provides complete code solutions to every problem presented, including detailed answers to even the most complex questions Presents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contexts Offers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketing Focuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

About the BookThe book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trendsTarget Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.The book offers a comprehensive coverage of some of the most essential topics: It provides an insight on building smart sustainable solutions. Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing. Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research. Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains. Encompasses comparison and analysis of recent technologies and trends. Includes development of smart models for information gain and effective decision making with visualization.The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.The book offers a comprehensive coverage of some of the most essential topics: It provides an insight on building smart sustainable solutions. Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing. Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research. Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains. Encompasses comparison and analysis of recent technologies and trends. Includes development of smart models for information gain and effective decision making with visualization.The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

About the Book

The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.

Salient Features of the Book

Exhaustive coverage of Data Analysis using R

Real-life healthcare models for:

Visually Impaired

Disease Diagnosis and Treatment options

Applications of Big Data and Deep Learning in Healthcare

Drug Discovery

Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications

Compare and analyze recent healthcare technologies and trends

Target Audience

This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

About the Book

The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.

Salient Features of the Book

Exhaustive coverage of Data Analysis using R

Real-life healthcare models for:

Visually Impaired

Disease Diagnosis and Treatment options

Applications of Big Data and Deep Learning in Healthcare

Drug Discovery

Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications

Compare and analyze recent healthcare technologies and trends

Target Audience

This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.

The book offers a comprehensive coverage of some of the most essential topics:

It provides an insight on building smart sustainable solutions.

Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing.

Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research.

Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains.

Encompasses comparison and analysis of recent technologies and trends.

Includes development of smart models for information gain and effective decision making with visualization.

The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

Advanced Computational Techniques for Sustainable Computing is considered multi-disciplinary field encompassing advanced computational techniques across several domain, including, Computer Science, Statistical Computation and Electronics Engineering. The core idea of sustainable computing is to deploy algorithms, models, policies and protocols to improve energy efficiency and management of resources, enhancing ecological balance, biological sustenance and other services on societal contexts.

The book offers a comprehensive coverage of some of the most essential topics:

It provides an insight on building smart sustainable solutions.

Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing.

Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research.

Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains.

Encompasses comparison and analysis of recent technologies and trends.

Includes development of smart models for information gain and effective decision making with visualization.

The readers would get acquainted with the utilization of massive data sets for intelligent mining and processing. It includes the integration of data mining techniques for effective decision-making in the social, economic, and global environmental domains to achieve sustainability. The implementation of computational frameworks can be accomplished using open-source software for the building of resource-efficient models. The content of the book demonstrates the usage of data science and the internet of things for the advent of smart and realistic solutions for attaining sustainability.

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readersProvides complete code solutions to every problem presented, including detailed answers to even the most complex questionsPresents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contextsOffers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketingFocuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readersProvides complete code solutions to every problem presented, including detailed answers to even the most complex questionsPresents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contextsOffers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketingFocuses on best practices and efficient coding techniques, improving productivity and maintainability of R code

The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R.

Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readers Provides complete code solutions to every problem presented, including detailed answers to even the most complex questions Presents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contexts Offers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketing Focuses on best practices and efficient coding techniques, improving productivity and maintainability of R code