Название: Convergence of Cloud with AI for Big Data Analytics: Foundations and InnovationАвтор: Danda B. Rawat, Lalit K. Awasthi
Издательство: Wiley-Scrivener
Год: 2023
Страниц: 610
Язык: английский
Формат: epub (true)
Размер: 10.2 MB
This book was written to discuss the milestones in the development of three recent domains in Computer Science engineering - Cloud Computing, Artificial Intelligence and Big Data Analytics - and to analyse the convergence of cloud computing with Artificial Intelligence (AI) for Big Data analytics. Despite the fact that all three domains work separately, they can be linked in interesting ways. However, even though AI and Big Data can be easily linked, because AI needs a huge amount of data to train the model, they still suffer from a data storage issue. This drawback can be addressed with the help of Cloud Computing, which makes it possible to provide on- demand services to the client in terms of computer resources, such as storage and computing power, without the need for user management. This book aims to provide the scope of research on the discussed technologies.
The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of Artificial Intelligence (AI), cloud computing, and Big Data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework.
A chapter-wise breakdown of the contents of the book follows:
• Chapter 1 discusses the integration of Artificial Intelligence, big data and cloud computing with the internet of things (IoT).
• Chapter 2 discusses cloud computing and virtualization.
• Chapter 3 presents a time and cost-effective multi-objective scheduling technique for cloud computing environment.
• Chapter 4 discusses cloud-based architecture for effective surveillance and diagnosis.
• Chapter 5 presents smart agriculture applications using cloud and the IoT.
• Chapter 6 presents applications of Federated Learning in computing technologies.
• Chapter 7 analyzes the application of edge computing in smart healthcare.
• Chapter 8 discusses a smart agriculture application using Fog-IoT.
• Chapter 9 presents a systematic study of the global impact of COVID-19 on the IoT.
• Chapter 10 discusses efficient solar energy management using IoT-enabled Arduino-based MPPT techniques.
• Chapter 11 presents an axiomatic analysis of pre-processing methodologies using Machine Learning in text mining from the perspective of social media in the IoT.
• Chapter 12 presents an app-based agriculture information system for rural farmers.
• Chapter 13 provides a systematic survey on AI-enabled cyber-physical systems in healthcare.
• Chapter 14 discusses an artificial neural network (ANN) aware methanol detection approach with CuO-doped SnO2 in gas sensor.
• Chapter 15 describes how to detect heart arrhythmias using Deep Learning algorithms.
• Chapter 16 presents an Artificial Intelligence approach for signature detection.
• Chapter 17 compares various classification models using Machine Learning to predict the price range of mobile phones.
Audience:
Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.
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