Big Data, Machine, and Deep Learning (eBook)

Recent Progress, Key Applications, and Future Directions

Rajesh Kumar Mishra, Divyansh Mishra, Rekha Agarwal (Autoren)

eBook Download: EPUB

2025 | 1. Auflage
52 Seiten
GRIN Verlag
978-3-389-12249-5 (ISBN)

Infos

Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible. The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.

Dr. Rajesh Kumar Mishra, ICFRE-Tropical Forest Research Institute, Jabalpur (MP), India is an internationally acclaimed researcher in the field of astrophysics, cosmic rays, space science, electronics, computer science and forestry. He has a postgraduate degree in Physics and Mathematics. He has done his doctoral degree in space science. Dr. Mishra has more than 30 years experience in various branches of astrophysics, cosmic rays, space science, climate change, computer applications, web application, network application and database development, and forestry science. He has more than 450 research publications in International and Nations journals in his credit. He is a life member of Indian Physics Association and Plasma Space science of India and member of Division of Plasma Physics, Association of Asia Pacific Physical Societies (AAPPS-DPP). He isworking as Assistant Editor for the online open access magazine "Van Sangyan" and Executive Board Member and Editorial Board Member for the journal Indian Journal of Tropical Biodiversity published by Society for Promotion of Tropical Biodiversity.

Produkt-Details

• Erscheint lt. Verlag: 11.4.2025
• Verlagsort: München
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: Artificial Intelligence Applications. • AutoML • Big Data • Cloud Computing • data privacy • Deep learning • edge computing • ethical AI • Explainable AI • federated learning • generative models • machine learning • Scalable Data Processing • self-supervised learning • Transformer Models
• ISBN-10: 3-389-12249-4 / 3389122494
• ISBN-13: 978-3-389-12249-5 / 9783389122495