Generative Artificial Intelligence (AI) Approaches for Industrial Applications

A Volume in Information Systems Engineering & Management (ISEM) book series, Springer-Verlag

Prof. Dr. Sanjiban Sekhar Roy
School of Computer Science and Engineering.
Vellore Institute of Technology
India

Assoc. Prof. Narasimha Rao Vajjhala
Dean, Faculty of Engineering and Architecture,
University of New York Tirana

Assoc. Prof. Burak Taşcı

Firat University Vocational School of Technical Sciences Elazig, Turkey

Dr. Muhammad E.H. Chowdhury
Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar.

About the Book

Generative AI Approaches for Industrial Applications will be a groundbreaking exploration of generative artificial intelligence's transformative role in modern industry. In particular, Generative AI-powered Large Language Model (LLM) has made news in recent times with disruptive applications like ChatGPT and BARD. This book will explore the applications, challenges, and future potential of generative AI technologies across various sectors, offering an invaluable resource for academics, professionals, and researchers. Generative AI, a subset of artificial intelligence focused on creating new content and solutions, is rapidly evolving and finding its place in diverse industrial domains. This book will provide a comprehensive overview of these applications, emphasizing practical implementations and theoretical underpinnings. This book will combine in-depth technical discussions with case studies and real-world examples, making it accessible to a broad audience, from AI practitioners to business leaders seeking to understand how generative AI can revolutionize their operations. This book will explore the mechanisms behind generative models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, illustrating their potential applications in various industry domains. Furthermore, this book will critically examine the ethical, social, and practical challenges posed by generative AI while shedding light on its transformative potential. This book should interest administrators, AI enthusiasts, and researchers in the nexus of technology and business.

Note: There are NO submission or acceptance fees for manuscripts submitted to this book publication. This book will be submitted for possible indexation to Scopus, Web of Science, and major indices.

Topics (Suggestive but not limited to)

The proposed tentative content for this book is given below:

Foundations of Generative AI
- Basics of Generative Models
- GANs, VAEs, and Transformers
- The Mathematical Principles Behind Generative Models
- Training and Evaluation of Generative Models
Generative AI in Manufacturing
- Predictive Maintenance and Anomaly Detection
- Automated Design and Prototyping
- Optimization of Manufacturing Processes
- Customization and Personalization in Production
Generative AI in Healthcare
- Drug Discovery and Development
- Personalized Treatment Planning
- Medical Imaging and Diagnosis
- Predictive Health Analytics and Risk Assessment
Generative AI in Creative Industries
- Automated Content Generation in Media and Entertainment
- AI-driven Music Composition and Sound Design
- AI in Visual Arts and Graphic Design
- Generative AI in Film and Video Production
Applications of Generative AI in Energy and Environment
- Smart Grid Optimization and Energy Management
- Predictive Maintenance in Renewable Energy Systems
- Climate Modelling and Environmental Impact Analysis
- Resource Exploration and Sustainable Resource Management
Generative AI in Logistics and Supply Chain
- Demand Forecasting and Inventory Management
- Route Optimization and Fleet Management
- Automated Warehousing and Material Handling
- Supplier Selection and Risk Management
Challenges and Ethical Considerations in Generative AI Applications
- Data Privacy and Security Concerns
- Bias and Fairness in AI Algorithms
- Regulatory and Compliance Issues
- Ethical Implications of Autonomous Decision-Making
Text Generation with Large Language Models
- Summary Generation
- Text Generation
- Various Applications of Pre-Trained Transformers
Case Studies and Practical Examples
- Key Takeaways and Lessons Learned
- Action Points for Administrators and Technologists

Submission Guidelines

All papers must be original and not simultaneously submitted to another book, journal, or conference.

Kindly note the following when submitting your book chapter:

Submit an initial proposal through the EquinOCS system, including the chapter title and the problem/purpose statement explaining the proposed chapter:

https://equinocs.springernature.com/service/GENAI-2024

The length of the chapter should be 6,000 - 8,000 words.
Paper should be formatted as per the template provided.
Please note that Information Systems Engineering and Management follows the Reference Style Basic , Citation Style Numbered and Numbering Style Content only.
Ensure that the paper adheres to Springer’s book chapter formatting guidelines. All submissions should be made in Word format only (.DOCX):

https://www.springer.com/gp/authors-editors/book-authors-editors/your-publication-journey/manuscript-preparation

All papers will be checked for plagiarism.
The chapter template is given below:

Download Word Template

Sample Book Chapter

Feel free to email us if you have questions or want feedback on your proposed research question (narasimharaonarasimha@gmail.com, sanjibanroy09@gmail.com, btasci@firat.edu.tr, mchowdhury@qu.edu.qa). You may also contact us via professional social media at:

https://www.linkedin.com/in/drvajjhala/

https://www.researchgate.net/profile/Narasimha-Vajjhala

https://research.vit.ac.in/researcher/sanjiban-sekhar-roy

https://www.linkedin.com/in/burak-taşcı-ph-d-1157a268/

https://orcid.org/0000-0003-0744-8206

Important Dates

Expression of Interest - March 1, 2024

Draft Chapter Due Date - May 30, 2024

Peer Review Period - June 1 - June 30, 2024

Finalized Chapter Due - July 15, 2024

Final Acceptance Notification - July 30, 2024

Book Publication Date - October 2024