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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	März 2025
Genre:	EDV / Informatik
	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 mehr...
ISBN:	9783389122501
EAN-Code:	9783389122501
Verlag:	Grin Verlag
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 210 mm / B 148 mm / D 5 mm
Gewicht:	96 gr
Seiten:	56
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.