Название: Computational Statistical Methodologies and Modeling for Artificial Intelligence Автор: Priyanka Harjule, Azizur Rahman, Basant Agarwal Издательство: CRC Press Серия: Edge AI in Future Computing Год: 2023 Страниц: 389 Язык: английский Формат: pdf (true) Размер: 23.1 MB
This book covers computational statistics-based approaches for Artificial Intelligence (AI). The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of Data Science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering.
Computational statistical science is a progressive field that employs advanced computing strategies to solve and understand real- life problems. In widespread usage, computational models are integrated with Artificial Intelligence (AI) algorithms, which is the soul of the digital computer or computer-controlled robot to perform intelligent tasks. AI is an interdisciplinary field and is frequently applied to developing systems endowed with humans’ intellectual processes, including the ability to reason, identify meaning, generalize, or learn from memories. AI has multiple approaches, and advancements in Machine Learning and Deep Learning are creating a metamorphosis in every industry sector.
Applications of computational methods for AI are all around us, such as personal assistants, search engines, image reorganization tools, etc. In the last decade, AI has seen innumerable successes with significant societal benefits and have contributed to the dynamic economy of the world. Deep learning is another Machine Learning dimension that runs inputs through biologically inspired neural network architectures. With the availability of lots of data all around us, computational statistical techniques have evolved and become an integral part of AI. The research trends in AI are highly influenced by mathematical modelling and computational statistics modifications. Hence, the editors and authors aim to bring together the recent research on the computational statistical methods and models applied to new datasets through this book.
In recent years, powerful computational models based on Deep Learning and Machine Learning approaches have shown significant success in dealing with a massive amount of data in unsupervised settings. This book is a culmination of efforts from researchers and academicians across the globe to present various computational and statistical methods and models for the field of AI.
AI has seen tremendous growth in its impact on human life over the past decades. With each passing day, AI’s sphere of influence is expanding, and today it is ubiquitous. The editors and contributors have considered the dynamically changing and ever-evolving nature of AI while compiling this book. This book provides a dynamic perspective on computational statistics for data analysis and applications in intelligent systems to ensure sustainability issues for the ease of different stakeholders in various fields. An in- depth explanation and description of several methodologies are also covered in the book. It is curated to cater to the needs of both beginners and advanced learners.
The book mainly focuses on the five major themes: Theme 1: Statistics and AI methods with applications, Theme 2: Machine Learning-adopted models, Theme 3: Development of the forecasting component of decision support tools, Theme 4: Socio-economic and environmental modelling, and Theme 5: Healthcare and mental disorder detection with AIs. Each of these themes is covered by a range of relevant chapters.
The key features of this book are:
Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
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