Xiaochun Cheng – författare

This book provides a thorough and comprehensive introduction to natural language processing (NLP), a critical field at the intersection of artificial intelligence and computational linguistics. It explores key techniques such as sentiment analysis, which enables the detection of emotional tone in text, machine translation, facilitating the conversion of text between languages, and named entity recognition (NER), which identifies and classifies entities like names, dates, and locations within text data.

The book delves into deep learning advancements, particularly the use of neural networks such as transformers and recurrent models, which have revolutionized NLP applications. Readers will gain insights into how these models drive innovations in areas such as text classification, language generation, and speech recognition.

In addition to technical concepts, the book also addresses the ethical considerations surrounding NLP, emphasizing the responsible use of AI technologies to mitigate issues like bias, misinformation, and privacy concerns. Practical case studies and real-world examples are included to illustrate how NLP is applied in various sectors, including healthcare, finance, and customer service.

This book is an invaluable resource for students, researchers, and industry professionals seeking to understand the foundational concepts, cutting-edge advancements, and broader implications of NLP, equipping them with the knowledge to innovate and apply these technologies effectively in their respective fields.

Explainable IoT Application: A Demystification is an in-depth guide that examines the intersection of the Internet of Things (IoT) with AI and Machine Learning, focusing on the crucial need for transparency and interpretability in IoT systems. As IoT devices become more integrated into daily life, from smart homes to industrial automation, it is increasingly important to understand and trust the decisions they make. The book starts by covering the basics of IoT, highlighting its importance in modern technology and its wide-ranging applications in fields such as healthcare, transportation, and smart cities. It then delves into the concept of explainability, stressing the need to prevent IoT systems from being perceived as opaque, black-box operations. The authors explore various techniques and methods for achieving explainability, including rule-based systems and machine learning models, while also addressing the challenge of balancing explainability with performance. Through practical examples, the book shows how explainability can be successfully implemented in IoT applications, such as in smart healthcare systems.

Furthermore, the book addresses the significant challenges of securing IoT systems in an increasingly connected world. It examines the unique vulnerabilities that come with the widespread use of IoT devices, such as data breaches, cyberattacks, and privacy issues, and discusses the complexities of managing these risks. The authors emphasize the importance of implementing security strategies that strike a balance between fostering innovations and protecting user data. The book concludes with a comprehensive exploration of the challenges and opportunities in making IoT systems more transparent and interpretable, offering valuable insights for researchers, developers, and decision-makers aiming to create IoT applications that are both trustworthy and understandable.

This two-volume set, LNICST 601 and LNICST 602, constitutes the refereed post-conference proceedings of the 14th International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2024, held in Hyderabad, India, in February 16–17, 2024.

The 49 full papers presented here were carefully reviewed and selected from 122 submissions. These papers have been organized under the following topical sections in the two volumes: -

Part I: Communications, Networks and Architectures; Smart City Smart Grid; Communication-inspired Machine Learning (ML) for 5G/6G. 

Part II: Wireless Network Security and Privacy; AI applications for 5G/6G.

This two-volume set, LNICST 601 and LNICST 602, constitutes the refereed post-conference proceedings of the 14th International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2024, held in Hyderabad, India, in February 16–17, 2024.

The 49 full papers presented here were carefully reviewed and selected from 122 submissions. These papers have been organized under the following topical sections in the two volumes: -

Part I: Communications, Networks and Architectures; Smart City Smart Grid; Communication-inspired Machine Learning (ML) for 5G/6G. 

Part II: Wireless Network Security and Privacy; AI applications for 5G/6G.