Focuses on natural language processing (NLP), artificial intelligence (AI), and allied areas, discussing theoretical work and advanced applications, approaches, and techniques for computational models of information and how they are presented by language (artificial, human, or natural in other ways).
Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This new volume, Natural Language Processing in Artificial Intelligence, focuses on natural language processing (NLP), artificial intelligence (AI), and allied areas, discussing theoretical work and advanced applications, approaches, and techniques for computational models of information and how they are presented by language (artificial, human, or natural in other ways). It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It also explores difficult problems and challenges related to partiality, under specification, and context-dependency, which are signature features of information in nature and natural languages.
Topics include the process of business intelligence and how this platform is used, the concepts of information retrieval systems, the neural machine translation (NMT) process, the choice of words and text in natural language processing, embedded traffic control and management systems, a technique for generating ontology by adopting the fruit fly optimization algorithm, POS labeling using the Viterbi algorithm, how natural language processing techniques can be used to prevent phishing attacks, and more.
Key features:
- Addresses the functional frameworks and workflow that are trending in NLP and AI
- Explores basic and high-level concepts, thus serving as a resource for those in the industry while also helping beginners to understand both basic and advanced aspects
- Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI
- Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world
- Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP
This volume will be a useful and informative resource for faculty, advanced-level students, and professionals in the field of artificial intelligence, natural language processing, and other areas.
1. Natural Language Processing and Artificial Intelligence: A
Perspective Towards Current Trends, Challenges, and Applications
2.
Artificial Intelligence-Based NLP: Methods, Trends, and Challenges
3.
Exploring the Synergy of Machine Learning and Natural Language Processing
4.
Navigating the AI Landscape: Architectures and Algorithms for Natural
Language Processing
5. Bridging Language Structure and Deep Learning Models:
A Comprehensive Exploration of Natural Language Processing
6. Artificial
Intelligence Architectures and Algorithms in Natural Language Processing
Ecosystems
7. Classification of Real and Fake News Using Machine Learning and
Deep Learning Techniques
8. Unleashing the Power of Big Data: Information
Retrieval and Text Mining Strategies
9. Reconciliation of Emotional
Intelligence with Artificial Intelligence for Augmenting Organizational
Effectiveness: A Conceptual Model
10. Innovations in Future Mobility and
Intelligent Virtual Assistants: Lensing Machine Learning AI-Based Chatbots
for Digital Users
11. FedNLP: Secure and Efficient Federated Learning Using
NLP: Architecture, Challenges, and Solutions
12. LLMS-Based Human-Like
Content Creation, Including Articles, Social Media Posts, and Marketing
Content
13. LLMS-Powered Chatbots and Virtual Assistants for Interactive and
Human-Like Interactions
14. TrOCR-Med: Revolutionizing Medical Data Handling
with Transformer-Based Handwritten Optical Character Recognition and
Extraction
15. Speech Emotion Detection Using Deep Learning
Vikas Khullar, PhD, is an Associate Professor of Computer Science and Engineering at Chitkara University, India. During his doctorate, he developed new and unique assistive technologies for neurological disorders with the use of computer vision, machine learning, and deep learning along with IoT and embedded hardware. He has published more than 80 publications and has filed 50 design and utility patents to tackle several social and commercial problems. Dr. Khullar has worked on two innovative project grants, one on cyber security and another on vehicular security. He has authored and edited six books at various levels.
Aryan Chaudhary is the Chief Scientific Advisor at BioTech Sphere Research, India. He was previously the Research Head at Nijji HealthCare Pvt Ltd. He has authored several influential academic papers on public health and digital health and has been a keynote speaker at numerous international and national conferences. He is also a book series editor and editor of several books on biomedical science. Recognized for his contributions, he has received prestigious accolades, including being named the Most Inspiring Young Leader in Healthtech Space 2022 by Business Connect and the title of the best project leader at Global Education and Corporate Leadership.
Sunil Kumar, PhD, is an Associate Professor in the School of Information Technology at AURO University, India. He is an academician and collaborative researcher with over 16 years of teaching and research experience in computer science and engineering. Dr. Kumar has delivered and conducted many expert talks, guest lectures, and workshops on cyber security, machine learning, and data analytics and has received best paper awards. He has published two textbooks on artificial intelligence and one on cyber security and has been granted five national and international patents.
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