Название: Artificial Intelligence and Multimedia Data Engineering
Автор: Suman Kumar Swarnkar, Sapna Singh Kshatri, Virendra Kumar Swarnkar
Издательство: Bentham Science
Год: December 19, 2023
Страниц: 134
Язык: английский
Формат: pdf (true), epub
Размер: 28.8 MB

Welcome to "Artificial Intelligence and Multimedia Data Engineering Vol. 1". In this book, we embark on a captivating journey through the cutting-edge realms of Artificial Intelligence (AI) and multimedia data engineering, exploring the remarkable synergies that exist between these two rapidly evolving fields. This fusion of AI and multimedia data engineering has opened up unprecedented opportunities for innovation and has profoundly impacted various industries, making it essential for researchers, practitioners, and enthusiasts alike to stay at the forefront of this dynamic landscape.

Advancements in AI, coupled with the explosive growth of multimedia data, have revolutionized the way we interact with technology and perceive the world around us. From Computer Vision and natural language processing (NLP) to Deep Learning and intelligent systems, AI has become an indispensable part of our lives, shaping our experiences in ways we could have only imagined a few decades ago. Furthermore, multimedia data, including images, videos, audio, and other sensor-generated content, has become an integral part of our digital existence, leading to the creation of a vast ocean of information that needs to be efficiently processed and harnessed.

The fastest-growing field in the world today is Machine Learning, which is a subset of both Data Science and Artificial Intelligence. Machine Learning facilitates the development of learning through its experience. The challenge of improving some element of performance when engaged in certain activities through some sort of training is a simple description of machine learning. Machine learning techniques can be categorised into a variety of groups according on the tasks they are used for. These groups include Classification (supervised learning), Clustering (unsupervised learning), Reinforcement learning (semi-supervised learning), Ensemble learning (Bagging and Boosting algorithms), active learning, and transfer learning. The most widely used form of Machine Learning is supervised learning. When the labeling of all the primary data is known, supervised learning can be used to create a model that can predict outcomes from incoming data. Classification and regression approaches, such as KNN (K nearest neighbour), SVM (support vector machine) and LDA (linear discriminative analysis) are commonly used in supervised learning tasks.

The primary aim of this book is to present a comprehensive overview of the interdisciplinary domain that intertwines AI and multimedia data engineering. Our endeavor is to provide a well-rounded understanding of the fundamental concepts, techniques, and applications that form the bedrock of this exciting field. Whether you are a seasoned professional seeking to expand your knowledge or a newcomer eager to explore the frontiers of AI and multimedia
data engineering, this book caters to a wide audience with diverse interests and backgrounds.

The book presents seven chapters which present use-cases for AI engineering that can be applied in many fields. The book concludes with a final chapter that summarizes emerging AI trends in intelligent and interactive multimedia systems.

Key features:
- A concise yet diverse range of AI applications for multimedia data engineering
- Covers both supervised and unsupervised Machine Learning techniques
- Summarizes emerging AI trends in data engineering
- Simple structured chapters for quick reference and easy understanding
- References for advanced readers

This book is a primary reference for Data Science and engineering students, researchers and academicians who need a quick and practical understanding of AI supplications in multimedia analysis for undertaking or designing courses. It also serves as a secondary reference for IT and AI engineers and enthusiasts who want to grasp advanced applications of the basic Machine Learning techniques in everyday applications