Название: Cybernetics, Human Cognition, and Machine Learning in Communicative Applications
Автор: Vinit Kumar Gunjan, Sabrina Senatore, Amit Kumar
Издательство: Springer
Год: 2025
Страниц: 424
Язык: английский
Формат: pdf (true)
Размер: 16.3 MB

This book presents the fascinating intersection of human cognition and Artificial Intelligence (AI). Written by leading experts in the fields of cybernetics, cognitive science, and Machine Learning, this book seeks to bridge the gap between these disciplines and explores the synergies that emerge when humans and machines work together. The book examines the challenges posed by biased data, lack of transparency, and the "black box" nature of some Machine Learning algorithms. It proposes novel ways to address these issues and foster greater trust and accountability in AI systems. Drawing on cutting-edge research and real-world case studies, it presents a comprehensive and forward-looking perspective on the future of AI and its impact on society. In conclusion, this book offers a compelling exploration of the synergy between human cognition and Machine Learning, providing insights that are relevant to scholars, researchers, policymakers, and anyone interested in the transformative potential of Artificial Intelligence.

Neuro-fuzzy systems represent a powerful paradigm in soft computing by integrating the strengths of neural networks and fuzzy logic to address complex and uncertain problems. This integration combines the learning capabilities of neural networks with the reasoning abilities of fuzzy logic, enabling effective handling of imprecise and uncertain data. In this paper, we provide an outline of neuro-fuzzy systems, including their architecture, learning algorithms, and applications. We discuss the principles underlying neuro-fuzzy systems, highlighting their ability to capture and represent linguistic variables, fuzzy sets, and fuzzy rules in a computationally efficient manner. Furthermore, we explore various learning algorithms used in neuro-fuzzy systems, such as backpropagation, gradient descent, and hybrid approaches. We also present a survey of applications where neuro-fuzzy systems have been successfully applied, including control systems, pattern recognition, forecasting, decision support systems, fault diagnosis, intelligent transportation systems, medical diagnosis, and energy management. Finally, we discuss current research trends and future directions in the field of neuro-fuzzy systems, emphasizing the importance of further advancements in learning algorithms, optimization techniques, and real-world applications.

The integration of Machine Learning (ML) and Artificial Intelligence (AI) in Internet of Things (IoT) systems has revolutionized various industries by enabling intelligent decision-making and automation. This paper presents an overview of integration techniques and applications of ML and AI in IoT environments. We discuss the challenges and opportunities associated with the fusion of these technologies, including data management, computational complexity, and privacy concerns. Furthermore, we explore various integration approaches such as edge computing, Federated Learning, and distributed intelligence architectures to address these challenges. We also highlight a range of IoT applications leveraging ML and AI techniques, including predictive maintenance, anomaly detection, smart healthcare, and environmental monitoring. Finally, we identify future research directions and emerging trends in this rapidly evolving field.