Highlights the importance and applications of Swarm Intelligence and Machine learning in Healthcare industry. Elaborates Swarm Intelligence and Machine Learning for Cancer Detection. Focuses on applying Swarm Intelligence and Machine Learning for Heart Disease detection and diagnosis. Explores of the concepts of machine learning along with swarm intelligence techniques, along with recent research developments in healthcare sectors. Investigates how healthcare companies can leverage the tapestry of big data to discover new business values. Provides a strong foundation for Diabetic Retinopathy detection using Swarm and Evolutionary algorithms.

This book is a collection of peer-reviewed best selected research papers presented at the First International Conference on Machine Intelligence and Smart Systems 2020 (MISS 2020), organized during September 24-25, 2020, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation.

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

The book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications.

This book is a collection of peer-reviewed best selected research papers presented at the Second International Conference on Machine Intelligence and Smart Systems (MISS 2021), organized during September 24-25, 2021, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation. Scientists, engineers, academicians, technology developers, researchers, students and government officials will find this book useful in handling their complicated real-world issues by using machine intelligence methodologies.

Association rule mining is the most popular data mining techniques to find association among items in a set by mining necessary patterns in a large database, frequently used in marketing, advertising and inventory control. Typically association rules consider only items enumerated in transactions, referred as positive association rules but not consider negative occurrence of attributes that are also useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Also for mining those positive rules that qualify the user specified threshold criteria, algorithm generates too many candidate itemsets by scanning database multiple times. In order to resolve all the bottleneck of association rule mining algorithm, in this we propose an algorithm SARIC which implements Set Particle Swarm Optimization heuristic technique for generating association rules from a database that also consider negative occurrence of attribute along with positive occurrence. SARIC uses the concept of IR and Correlation Coefficient and there is no need to specify minimum support and confidence, it automatically determines them quickly and objectively

Data structure is a way of organizing data in a computer memory so that it can be used efficiently. Data structures can implement one or more particular abstract data types (ADT), which are the means of specifying the nature of operations and their complexity. This book focuses on the design and analysis of basic data structures and their implementation and intends to provide a strong conceptual and empirical understanding of data structures. The contents of this book will help the students to understand that how the data structures are implemented and why these implementations are vital. The initial chapters deal with introductory part of the subject and the later chapters focus on advanced and difficult problems and algorithms. The book explains how data is organized in computer memory; how it operates; and why it is important. Appendixes provide further information on algorithms along with their programming solutions. Readers of this book need only to be familiar with the basic syntax of C/C++ and similar languages. This book will be useful for B.Tech/M.Tech Computer Science and Engineering, IT Engineering, BSc (IT)/MSc (IT), and BCA/MCA students.