Textbook in PDF format

This book comprehensively discusses the role of cloud computing in Artificial Intelligence-based data-driven systems, and hybrid cloud computing for large data-driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides internet of things-based frameworks and advanced computing techniques to deal with online/virtual systems.
The resource?constrained nature of IoT devices leads to not only the challenges of privacy and autonomy but also the major challenge of implementing Machine Learning models for IoT devices. The implementation of Machine Learning models on IoT devices in real?time scenarios poses a major challenge that attracts researchers to work in this domain. To make the IoT ecosystems intelligent, these resource?constrained devices need to be analysed locally. As of now, all sensed data are being processed and analysed in clouds. The small IoT devices may not afford Machine Learning algorithms because of their limited computational power and memory requirements. This involves issues like low bandwidth, high latency, privacy, security and others. Also, there are several Machine Learning algorithms that can be applied for IoT data analytics especially for data?driven systems. Therefore, choosing the best model which is application specific is great work of thought. This chapter is divided into a number of sections that mention different types of Machine Learning models which can be applied, as well as the role of IoT and Machine Learning in data?driven systems specifically.
This book:
• Covers aspects of security, authentication, and prediction for data-driven systems in heterogeneous environments.
• Provide data-driven frameworks in combination with the Internet of Things, Artificial Intelligence, and computing to provide critical insights and decision-making for real-time problems.
• Showcases Deep Learning-based computer vision algorithms for enhanced pattern detection in different domains based on data-centric approaches.
• Examines the role of the Internet of Things and Machine Learning algorithms for data-driven systems.
• Highlights the applications of data-driven systems and cloud computing in enhancing network performance.
1 Artificial intelligence and IoT: challenges and future directions for data‑driven system
2 Cloud computing in AI‑based data‑driven systems: opportunities and challenges
3 Study on the detection of potato diseases using deep learning network
4 Leveraging cloud computing for efficient AI‑based data‑driven systems
5 Analyzing and contrasting the outcomes of performance‑based plagiarism detection methods
6 Machine learning algorithms for data‑driven systems in IoT
7 Improving classification accuracy of diabetes mellitus prediction using ensemble techniques
8 Machine learning models for IoT botnet attack detection
9 Blockchain‑based identity authentication for Internet of Things systems: a comprehensive survey
10 Connected healthcare—the impact of Internet of Things on medical services: merits, limitations, future insights, case studies, and open research questions
11 Data‑driven early detection of livestock diseases using IoT‑enabled smart collars
12 Exploring the performance improvement and skill set transformations in sheet metal operations through digital technology
13 Crowd‑sourced‑based emergency response on the Internet of Vehicles (IOV): harnessing strengths and limitations