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Generative Artificial Intelligence

Concepts and Applications

Edited by R. Nidhya, D. Pavithra, Manish Kumar, A Dinesh Kumar and S. Balamurugan
Series: Industry 5.0 Transformation Applications
Copyright: 2025   |   Expected Pub Date:2025/03/30
ISBN: 9781394209224  |  Hardcover  |  
302 pages

One Line Description
This book is a comprehensive overview of AI fundamentals and applications to drive creativity, innovation, and industry transformation.

Audience
The book will be read by researchers, engineers, and students working in artificial intelligence, computer science, and electronics and communication engineering as well as industry application areas.

Description
Generative AI stands at the forefront of artificial intelligence innovation, redefining the capabilities of machines to create, imagine, and innovate. GAI explores the domain of creative production with new and original content across various forms, including images, text, music, and more. In essence, generative AI stands as evidence of the boundless potential of artificial intelligence, transforming industries, sparking creativity, and challenging conventional paradigms. It represents not just a technological advancement but a catalyst for reimagining how machines and humans collaborate, innovate, and shape the future.
The book examines real-world examples of how generative AI is being used in a variety of industries. The first section explores the fundamental concepts and ethical considerations of generative AI. In addition, the section also introduces machine learning algorithms and natural language processing. The second section introduces novel neural network designs and convolutional neural networks, providing dependable and precise methods. The third section explores the latest learning-based methodologies to help researchers and farmers choose optimal algorithms for specific crop and hardware needs. Furthermore, this section evaluates significant advancements in revolutionizing online content analysis, offering real-time insights into content creation for more interactive processes.

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Author / Editor Details
R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated with Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research papers in international journals, and her research interests include wireless body area networks, network security, and data mining.

D. Pavithra, PhD, is an assistant professor at Dr. NGP Institute of Technology, Coimbatore, Tamil Nadu, India. Her current research interests include autism, machine learning, and deep learning.

Manish Kumar, PhD, is an assistant professor at The School of Computer Science & Engineering, VIT, Chennai, India. His research interests include soft computing applications for bioinformatics problems and computational intelligence.

A. Dinesh Kumar, PhD, is an associate professor at KL (Deemed to be University), Vijayawada, Andhra Pradesh, India. His current research interests include wireless body area networks, wireless sensor networks, network security, and artificial intelligence.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamil Nadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman of the Renewable Energy Society of India (RESI), India. He has published 50+ books, 200+ international journals/conferences, and 35 patents.

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Table of Contents
Preface
1. Exploring the Creative Frontiers: Generative AI Unveiled
Generated Using ChatGPT
1.1 Introduction
1.1.1 Definition and Significance of Generative AI
1.1.2 Historical Overview and Development
1.2 Foundational Concepts
1.2.1 Neural Networks and Generative Models
1.2.2 Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs)
1.3 Applications Across Domains
1.3.1 Creative Arts: Music, Visual Arts, Literature
1.3.2 Content Generation: Text, Images, Videos
1.3.3 Scientific Research and Data Augmentation
1.3.4 Healthcare and Drug Discovery
1.3.5 Gaming and Virtual Environments
1.4 Ethical Considerations
1.5 Future Prospects and Challenges
1.6 Conclusion
Reference
2. An Efficient Infant Cry Detection System Using Machine Learning and Neuro Computing Algorithms
Swarna Kuchibhotla, Kantheti Mohana, Alapati Yomitha, Sruthi Yedavalli, Hima Deepthi Vankayalapati and Kyamakya Kyandoghere
2.1 Introduction
2.2 Literature Survey
2.3 Methodology
2.3.1 Database
2.3.2 Feature Extraction
2.3.2.1 Short-Term Energy
2.3.2.2 Mel-Frequency Cepstral Coefficients
2.3.2.3 Spectrograms
2.3.3 Classification
2.3.4 Convolutional Neural Network (CNN)
2.3.5 Recurrent Neural Network (RNN)
2.3.6 Regularized Discriminant Analysis (RDA)
2.3.7 Multi-Layer Perceptron (MLP)
2.4 Experimental Results
2.5 Conclusion
References
3. Improved Brain Tumor Segmentation Utilizing a Layered CNN Model
Bilal Hikmat Rasheed and P. Sudhakaran
3.1 Introduction
3.2 Related Works
3.3 Methodology
3.4 Numerical Results
3.5 Conclusion
References
4. Natural Language Processing in Generative Adversarial Network
P. Dhivya, A. Karthikeyan, S. Pradeep and H. Umamaheswari
4.1 Introduction
4.2 Literature Survey
4.3 The Implementation of NLP in GAN for Generating Images and Summaries
4.3.1 Working of Sequence Generative Adversarial Network (SeqGAN)
4.3.2 Working of Generative Adversarial Transformer (GAT)
4.3.2.1 Steps to Incorporate NLP in GAN
4.3.3 Implementation of NLP in GAN
4.3.4 Generate the Image Using Textual Description
4.3.5 Text Summarization
4.3.5.1 Graph-Based Summarization
4.4 Conclusion
References
5. Modeling A Deep Learning Network Model for Medical Image Panoptic Segmentation
Jyothsna Devi Koppagiri and Gouranga Mandal
5.1 Introduction
5.2 Related Works
5.3 Methodology
5.3.1 Deep Masking Convolutional Model (DMCM)
5.4 Numerical Results and Discussion
5.5 Conclusion
References
6. A Hybrid DenseNet Model for Dental Image Segmentation Using Modern Learning Approaches
Pulipati Nagaraju and S. V. Sudha
6.1 Introduction
6.2 Related Works
6.3 Methodology
6.3.1 Dataset
6.3.2 Dense Transformer Model
6.3.3 DenseNet Model
6.4 Numerical Results and Discussion
6.4.1 Discussion
6.5 Conclusion
References
7. Modeling A Two-Tier Network Model for Unconstraint Video Analysis Using Deep Learning
P. Naga Bhushanam and Selva Kumar S.
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.4 Numerical Results and Discussion
7.5 Conclusion
References
8. Detection of Peripheral Blood Smear Malarial Parasitic Microscopic Images Utilizing Convolutional Neural Network
Tamal Kumar Kundu, Smritilekha Das and R. Nidhya
8.1 Introduction
8.2 Malaria
8.2.1 Malaria-Infected Red Blood Cells with Types
8.3 Literature Survey
8.4 Proposed Methodology and Algorithm
8.4.1 Proposed Algorithm
8.5 Result Analysis
8.5.1 Dataset
8.5.2 Preprocessing of Data
8.5.3 Splitting of Dataset
8.5.4 Classification
8.5.5 Model Prediction and Performance Metrics
8.5.6 CNN Learning Curves
8.6 Discussion
8.7 Conclusion
8.8 Future Scope
References
9. Exploring the Efficacy of Generative AI in Constructing Dynamic Predictive Models for Cybersecurity Threats: A Research Perspective
T. Manasa and K. Padmanaban
9.1 Introduction
9.2 Related Works
9.3 Methodology
9.3.1 Pre-Processing
9.3.2 Classifier
9.3.3 Optimization
9.4 Numerical Results and Discussion
9.5 Conclusion
References
10. Poultry Disease Detection: A Comparative Analysis of CNN, SVM, and YOLO v3 Algorithms for Accurate Diagnosis
Spoorthi Shetty and Mangala Shetty
10.1 Introduction
10.2 Literature Review
10.3 Objectives
10.3.1 Accurate Disease and Early Disease Identification
10.3.2 Multi-Class Disease Identification
10.3.3 Automation and Real-Time Disease Monitoring
10.3.4 Better Accuracy
10.4 Methodology
10.4.1 Dataset
10.4.2 Data Preprocessing
10.4.3 Image Preprocessing
10.4.4 Data Augmentation
10.4.5 Extracting Region of Interest
10.5 Results and Discussion
10.6 Conclusion
References
11. Generative AI-Enhanced Deep Learning Model for Crop Type Analysis Based on Clustered Feature Vectors and Remote Sensing Imagery
B. Bazeer Ahamed, D. Yuvaraj and Saif Saad Alnuaimi
11.1 Introduction
11.2 Related Works
11.3 Methodology
11.3.1 Saliency Analysis
11.3.2 Saliency Region Analysis with Belief Networking
11.3.3 Group Analysis
11.3.4 Classification
11.3.5 Parameter Setup
11.4 Numerical Results and Discussion
11.4.1 Dataset
11.4.2 Classification Results and Discussions
11.5 Conclusion
References
12. Cardiovascular Disease Prediction with Machine Learning: An Ensemble-Based Regressive Neighborhood Model
Yuvaraj Duraisamy, Salar Faisal Noori and Shakir Mahoomed Abas
12.1 Introduction
12.2 Related Works
12.3 Methodology
12.3.1 Pre-Processing
12.3.2 Feature Selection
12.3.3 Classification
12.4 Numerical Results and Discussion
12.5 Conclusion
References
13. Detection of IoT Attacks Using Hybrid RNN-DBN Model
Pavithra D., Bharathraj R., Poovizhi P., Libitharan K. and Nivetha V.
13.1 Introduction
13.2 Related Work
13.3 Methodology
13.3.1 Dataset Used
13.3.2 Data Preprocessing
13.3.3 Data Normalization
13.3.4 Multi-Class Classification
13.3.5 Splitting Dataset
13.3.6 RNN-DBN
13.4 Experiments and Results
13.5 Conclusion and Future Scope
References
14. Identification of Foliar Pathologies in Apple Foliage Utilizing Advanced Deep Learning Techniques
Tamal Kumar Kundu, Smritilekha Das and R. Nidhya
14.1 Introduction
14.2 Literature Survey
14.2.1 Disease Detection Using Machine and Deep Learning Techniques (2015–2021)
14.2.2 Disease Detection Using Transfer Learning (2015–2021)
14.3 Different Diseases of Leaves
14.4 Dataset
14.5 Proposed Methodology
14.6 Data Analysis
14.7 Pre-Processing Technique
14.8 Data Visualization
14.9 Evolutionary Progression and Genesis of Model
14.9.1 Evolution Model
14.9.2 Model Performance
References
15. Enhancing Cloud Security Through AI-Driven Intrusion Detection Utilizing Deep Learning Methods and Autoencoder Technology
P.V. Sivarambabu, Richa Agrawal, Arepalli Tirumala, Shaik Mahaboob Subani, Veeraswamy Parisae and S. V. L. Sowjanya Nukala
15.1 Introduction
15.2 Related Work
15.3 Proposed Methodology
15.3.1 DL-Based IDS for Cloud Security
15.4 Results and Discussion
15.4.1 Performance Analysis
15.4.1.1 Accuracy
15.4.1.2 Precision
15.4.1.3 Recall
15.4.1.4 F1 Score
15.4.1.5 AUC - Area Under the Curve
15.5 Conclusion
References
16. YouTube Comment Analysis Using LSTM Model
Pavithra D., Poovizhi P., Rokeshkumar G., Bharathvaj T. and Mageshkumar M.
16.1 Introduction
16.2 Related Work
16.3 Literature Survey
16.4 Existing System
16.5 Methodology
16.6 Result and Discussion
16.7 Conclusion
References
Index

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