Avita Katal – författare

A database management system (DBMS) is a collection of programs that enable users to create and maintain a database; it also consists of a collection of interrelated data and a set of programs to access that data. Hence, a DBMS is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications.

The primary goal of a DBMS is to provide an environment that is both convenient and efficient to use in retrieving and storing database information. It is an interface between the user of application programs, on the one hand, and the database, on the other.

The objective of Database Management System: An Evolutionary Approach, is to enable the learner to

This book is intended to give under- and postgraduate students a fundamental background in DBMSs. The book follows an evolutionary learning approach that emphasizes the basic concepts and builds a strong foundation to learn more advanced topics including normalizations, normal forms, PL/SQL, transactions, concurrency control, etc.

This book also gives detailed knowledge with a focus on entity-relationship (ER) diagrams and their reductions into tables, with sufficient SQL codes for a more practical understanding.

A database management system (DBMS) is a collection of programs that enable users to create and maintain a database; it also consists of a collection of interrelated data and a set of programs to access that data. Hence, a DBMS is a general-purpose software system that facilitates the processes of defining, constructing, and manipulating databases for various applications.

The primary goal of a DBMS is to provide an environment that is both convenient and efficient to use in retrieving and storing database information. It is an interface between the user of application programs, on the one hand, and the database, on the other.

The objective of Database Management System: An Evolutionary Approach, is to enable the learner to

This book is intended to give under- and postgraduate students a fundamental background in DBMSs. The book follows an evolutionary learning approach that emphasizes the basic concepts and builds a strong foundation to learn more advanced topics including normalizations, normal forms, PL/SQL, transactions, concurrency control, etc.

This book also gives detailed knowledge with a focus on entity-relationship (ER) diagrams and their reductions into tables, with sufficient SQL codes for a more practical understanding.

Fog Computing: Concepts, Frameworks, and Applications is arranged in such a way that readers with no prior experience in Fog Computing may explore this domain. It is an accessible source of information for distributed computing researchers as well as professionals looking to improve their security and connectivity understanding in Internet of Things (IoT) devices. This book is also useful for researchers and professionals working in the field of wireless communication security and privacy research. This book is intended for students, professionals, researchers, and developers who are working in or interested in the field of Fog Computing. One of the book''s distinguishing aspects is that it covers a variety of case studies and future possibilities in the field of Fog Computing.

This book:

Fog Computing: Concepts, Frameworks, and Applications is arranged in such a way that readers with no prior experience in Fog Computing may explore this domain. It is an accessible source of information for distributed computing researchers as well as professionals looking to improve their security and connectivity understanding in Internet of Things (IoT) devices. This book is also useful for researchers and professionals working in the field of wireless communication security and privacy research. This book is intended for students, professionals, researchers, and developers who are working in or interested in the field of Fog Computing. One of the book''s distinguishing aspects is that it covers a variety of case studies and future possibilities in the field of Fog Computing.

This book:

Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, cloud-based analytics tools, and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics, and e-commerce. It also addresses security challenges, privacy concerns, and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics.

This book is an essential resource for students, researchers, and professionals who seek to understand and apply ML-driven cloud analytics in real-world scenarios.

Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, cloud-based analytics tools, and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics, and e-commerce. It also addresses security challenges, privacy concerns, and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics.

This book is an essential resource for students, researchers, and professionals who seek to understand and apply ML-driven cloud analytics in real-world scenarios.

The book “Machine Learning for Disease Detection, Prediction, and Diagnosis” can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies.

This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further.

Overall, this book serves as a great source of information about the latest advancements made in the field of Machine Learning & Artificial Intelligence towards efficient building systems capable enough detecting & diagnosing diseases automatically while avoiding human errors resulting due manual intervention at any stage along process pipeline thus ensuring better outcomes overall. Moreover, it helps readers understand the underlying principles behind each technique discussed so that they may apply them according to their own application scenarios efficiently without worrying much about the implementation details required to get the job done the right way the first time around itself!