Generative Artificial Intelligence
Description
This book is a comprehensive overview of AI fundamentals and applications to drive creativity, innovation, and industry transformation.
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.
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.
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Persons
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.
Content
Preface xiii
1 Exploring the Creative Frontiers: Generative AI Unveiled 1
Generated Using ChatGPT
1.1 Introduction 1
1.1.1 Definition and Significance of Generative AI 1
1.1.2 Historical Overview and Development 2
1.2 Foundational Concepts 4
1.2.1 Neural Networks and Generative Models 4
1.2.2 Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) 5
1.3 Applications Across Domains 7
1.3.1 Creative Arts: Music, Visual Arts, Literature 7
1.3.2 Content Generation: Text, Images, Videos 8
1.3.3 Scientific Research and Data Augmentation 9
1.3.4 Healthcare and Drug Discovery 10
1.3.5 Gaming and Virtual Environments 12
1.4 Ethical Considerations 13
1.5 Future Prospects and Challenges 15
1.6 Conclusion 16
Reference 17
2 An Efficient Infant Cry Detection System Using Machine Learning and Neuro Computing Algorithms 19
Swarna Kuchibhotla, Kantheti Mohana, Alapati Yomitha, Sruthi Yedavalli, Hima Deepthi Vankayalapati and Kyamakya Kyandoghere
2.1 Introduction 20
2.2 Literature Survey 21
2.3 Methodology 23
2.3.1 Database 24
2.3.2 Feature Extraction 25
2.3.2.1 Short-Term Energy 25
2.3.2.2 Mel-Frequency Cepstral Coefficients 26
2.3.2.3 Spectrograms 27
2.3.3 Classification 29
2.3.4 Convolutional Neural Network (CNN) 29
2.3.5 Recurrent Neural Network (RNN) 31
2.3.6 Regularized Discriminant Analysis (RDA) 31
2.3.7 Multi-Layer Perceptron (MLP) 33
2.4 Experimental Results 33
2.5 Conclusion 35
References 35
3 Improved Brain Tumor Segmentation Utilizing a Layered CNN Model 39
Bilal Hikmat Rasheed and P. Sudhakaran
3.1 Introduction 40
3.2 Related Works 41
3.3 Methodology 42
3.4 Numerical Results 45
3.5 Conclusion 49
References 49
4 Natural Language Processing in Generative Adversarial Network 53
P. Dhivya, A. Karthikeyan, S. Pradeep and H. Umamaheswari
4.1 Introduction 54
4.2 Literature Survey 57
4.3 The Implementation of NLP in GAN for Generating Images and Summaries 61
4.3.1 Working of Sequence Generative Adversarial Network (SeqGAN) 61
4.3.2 Working of Generative Adversarial Transformer (GAT) 63
4.3.2.1 Steps to Incorporate NLP in GAN 64
4.3.3 Implementation of NLP in GAN 65
4.3.4 Generate the Image Using Textual Description 68
4.3.5 Text Summarization 69
4.3.5.1 Graph-Based Summarization 71
4.4 Conclusion 77
References 77
5 Modeling A Deep Learning Network Model for Medical Image Panoptic Segmentation 81
Jyothsna Devi Koppagiri and Gouranga Mandal
5.1 Introduction 81
5.2 Related Works 84
5.3 Methodology 85
5.3.1 Deep Masking Convolutional Model (DMCM) 85
5.4 Numerical Results and Discussion 87
5.5 Conclusion 91
References 91
6 A Hybrid DenseNet Model for Dental Image Segmentation Using Modern Learning Approaches 93
Pulipati Nagaraju and S. V. Sudha
6.1 Introduction 94
6.2 Related Works 95
6.3 Methodology 96
6.3.1 Dataset 96
6.3.2 Dense Transformer Model 97
6.3.3 DenseNet Model 100
6.4 Numerical Results and Discussion 100
6.4.1 Discussion 103
6.5 Conclusion 104
References 104
7 Modeling A Two-Tier Network Model for Unconstraint Video Analysis Using Deep Learning 107
P. Naga Bhushanam and Selva Kumar S.
7.1 Introduction 108
7.2 Related Works 109
7.3 Methodology 110
7.4 Numerical Results and Discussion 113
7.5 Conclusion 117
References 118
8 Detection of Peripheral Blood Smear Malarial Parasitic Microscopic Images Utilizing Convolutional Neural Network 121
Tamal Kumar Kundu, Smritilekha Das and R. Nidhya
8.1 Introduction 122
8.2 Malaria 124
8.2.1 Malaria-Infected Red Blood Cells with Types 124
8.3 Literature Survey 125
8.4 Proposed Methodology and Algorithm 130
8.4.1 Proposed Algorithm 135
8.5 Result Analysis 135
8.5.1 Dataset 135
8.5.2 Preprocessing of Data 135
8.5.3 Splitting of Dataset 137
8.5.4 Classification 137
8.5.5 Model Prediction and Performance Metrics 137
8.5.6 CNN Learning Curves 138
8.6 Discussion 139
8.7 Conclusion 139
8.8 Future Scope 139
References 140
9 Exploring the Efficacy of Generative AI in Constructing Dynamic Predictive Models for Cybersecurity Threats: A Research Perspective 143
T. Manasa and K. Padmanaban
9.1 Introduction 144
9.2 Related Works 145
9.3 Methodology 146
9.3.1 Pre-Processing 147
9.3.2 Classifier 147
9.3.3 Optimization 148
9.4 Numerical Results and Discussion 149
9.5 Conclusion 152
References 152
10 Poultry Disease Detection: A Comparative Analysis of CNN, SVM, and YOLO v3 Algorithms for Accurate Diagnosis 155
Spoorthi Shetty and Mangala Shetty
10.1 Introduction 156
10.2 Literature Review 157
10.3 Objectives 158
10.3.1 Accurate Disease and Early Disease Identification 158
10.3.2 Multi-Class Disease Identification 158
10.3.3 Automation and Real-Time Disease Monitoring 159
10.3.4 Better Accuracy 159
10.4 Methodology 159
10.4.1 Dataset 159
10.4.2 Data Preprocessing 160
10.4.3 Image Preprocessing 161
10.4.4 Data Augmentation 161
10.4.5 Extracting Region of Interest 162
10.5 Results and Discussion 165
10.6 Conclusion 169
References 170
11 Generative AI-Enhanced Deep Learning Model for Crop Type Analysis Based on Clustered Feature Vectors and Remote Sensing Imagery 173
B. Bazeer Ahamed, D. Yuvaraj and Saif Saad Alnuaimi
11.1 Introduction 174
11.2 Related Works 176
11.3 Methodology 178
11.3.1 Saliency Analysis 180
11.3.2 Saliency Region Analysis with Belief Networking 181
11.3.3 Group Analysis 182
11.3.4 Classification 183
11.3.5 Parameter Setup 183
11.4 Numerical Results and Discussion 184
11.4.1 Dataset 186
11.4.2 Classification Results and Discussions 187
11.5 Conclusion 190
References 193
12 Cardiovascular Disease Prediction with Machine Learning: An Ensemble-Based Regressive Neighborhood Model 197
Yuvaraj Duraisamy, Salar Faisal Noori and Shakir Mahoomed Abas
12.1 Introduction 197
12.2 Related Works 200
12.3 Methodology 200
12.3.1 Pre-Processing 200
12.3.2 Feature Selection 202
12.3.3 Classification 202
12.4 Numerical Results and Discussion 203
12.5 Conclusion 206
References 207
13 Detection of IoT Attacks Using Hybrid RNN-DBN Model 209
Pavithra D., Bharathraj R., Poovizhi P., Libitharan K. and Nivetha V.
13.1 Introduction 210
13.2 Related Work 212
13.3 Methodology 216
13.3.1 Dataset Used 216
13.3.2 Data Preprocessing 217
13.3.3 Data Normalization 217
13.3.4 Multi-Class Classification 218
13.3.5 Splitting Dataset 219
13.3.6 RNN-DBN 219
13.4 Experiments and Results 221
13.5 Conclusion and Future Scope 224
References 224
14 Identification of Foliar Pathologies in Apple Foliage Utilizing Advanced Deep Learning Techniques 227
Tamal Kumar Kundu, Smritilekha Das and R. Nidhya
14.1 Introduction 228
14.2 Literature Survey 229
14.2.1 Disease Detection Using Machine and Deep Learning Techniques (2015-2021) 229
14.2.2 Disease Detection Using Transfer Learning (2015-2021) 232
14.3 Different Diseases of Leaves 233
14.4 Dataset 236
14.5 Proposed Methodology 239
14.6 Data Analysis 240
14.7 Pre-Processing Technique 241
14.8 Data Visualization 242
14.9 Evolutionary Progression and Genesis of Model 242
14.9.1 Evolution Model 243
14.9.2 Model Performance 244
References 246
15 Enhancing Cloud Security Through AI-Driven Intrusion Detection Utilizing Deep Learning Methods and Autoencoder Technology 249
P.V. Sivarambabu, Richa Agrawal, Arepalli Tirumala, Shaik Mahaboob Subani, Veeraswamy Parisae and S. V. L. Sowjanya Nukala
15.1 Introduction 250
15.2 Related Work 251
15.3 Proposed Methodology 253
15.3.1 DL-Based IDS for Cloud Security 253
15.4 Results and Discussion 254
15.4.1 Performance Analysis 258
15.4.1.1 Accuracy 259
15.4.1.2 Precision 260
15.4.1.3 Recall 260
15.4.1.4 F1 Score 261
15.4.1.5 AUC-Area Under the Curve 261
15.5 Conclusion 262
References 262
16 YouTube Comment Analysis Using LSTM Model 265
Pavithra D., Poovizhi P., Rokeshkumar G., Bharathvaj T. and Mageshkumar M.
16.1 Introduction 266
16.2 Related Work 266
16.3 Literature Survey 267
16.4 Existing System 272
16.5 Methodology 273
16.6 Result and Discussion 275
16.7 Conclusion 280
References 280
Index 283
1
Exploring the Creative Frontiers: Generative AI Unveiled
Generated Using ChatGPT*
Abstract
Generative artificial intelligence (AI) stands at the forefront of technological innovation, captivating minds and pushing the boundaries of creativity. This paper delves into the realm of generative AI, exploring its foundational concepts, applications across diverse domains, ethical considerations, and the future implications it holds. The study navigates through the evolution of generative AI, highlighting its capabilities in generating content, fostering artistic expression, aiding in problem-solving, and revolutionizing various industries. Additionally, it examines the ethical implications associated with generative AI's advancements, shedding light on concerns regarding bias, authenticity, and societal impact. The paper concludes by envisioning future prospects and challenges that lie ahead in the fascinating landscape of generative AI.
1.1 Introduction
1.1.1 Definition and Significance of Generative AI
Generative artificial intelligence (AI) refers to a subset of AI that focuses on creating, generating, or producing new content, information, or data that mimics human-like creativity and innovation. Unlike traditional AI, which typically focuses on analyzing existing data to make predictions or decisions, generative AI is designed to generate new content autonomously.
The significance of generative AI lies in its ability to simulate humanlike creativity, enabling machines to produce content that ranges from text, images, and music to videos and more. This technology has far-reaching implications across various domains:
Creative Expression: Generative AI allows for the creation of original artworks, music compositions, and literary pieces. This facilitates new forms of artistic expression and exploration.
Content Generation: It helps in automating the generation of content for various purposes, such as writing news articles, producing marketing materials, or creating realistic images and videos.
Problem-solving and Innovation: Generative AI aids in exploring new solutions to complex problems by generating diverse hypotheses and scenarios, contributing to innovation in research and development.
Personalization and Customization: It enables personalized content creation, catering to individual preferences and needs, thereby enhancing user experiences in various applications.
Data Augmentation and Simulation: In scientific research and data analysis, generative models assist in generating synthetic data, facilitating better analysis and understanding of complex systems.
Enhanced Realism in Virtual Environments: In gaming and virtual reality (VR), generative AI enhances the realism of virtual worlds, creating more immersive experiences for users.
Generative AI's significance also lies in its potential to transform industries by automating content creation, aiding in decision-making processes, and driving innovation. However, its development raises ethical concerns related to authenticity, bias, privacy, and ownership of generated content. Understanding and addressing these ethical challenges are crucial for responsible deployment and usage of generative AI in various fields.
1.1.2 Historical Overview and Development
The history of generative AI is an evolutionary journey marked by significant milestones in the field of AI and machine learning. Here is a brief historical overview:
Early Concepts (1950s - 1960s): The roots of generative AI can be traced back to the early days of AI research. Pioneers like Alan Turing laid the theoretical groundwork for machine intelligence and the concept of machines exhibiting creative behavior.
Rule-based Systems (1970s - 1980s): Early AI systems relied on rulebased approaches, where experts codified explicit rules for the computer to follow. While these systems were not inherently generative, they formed the basis for later developments.
Probabilistic Models (1990s - 2000s): Bayesian networks and probabilistic graphical models emerged, allowing machines to model uncertainty and generate probabilistic outputs. This era saw advancements in probabilistic modeling for tasks like speech recognition and natural language processing.
Rise of Neural Networks (2010s): With the resurgence of neural networks and deep learning, generative AI witnessed a significant leap. Variational Autoencoders (VAEs) and generative adversarial networks (GANs) became prominent; VAEs focused on learning latent representations of data for generative purposes, while GANs introduced a novel adversarial training framework for generating realistic data.
Recent Advancements (2010s - 2020s): The latter half of the 2010s and early 2020s marked rapid progress in generative AI. OpenAI's generative pre-trained transformer (GPT) models, starting from GPT-1 and evolving into larger versions like GPT-2 and GPT-3, showcased the power of large-scale language generation.
Diversification of Generative Models: The development of various architectures and techniques, such as transformer models, attention mechanisms, reinforcement learning, and fine-tuning strategies, expanded the capabilities of generative AI beyond language to images, music, and multimodal generation.
Ethical and Social Implications: Alongside technological advancements, discussions about the ethical implications of generative AI, including issues of bias, manipulation, and misinformation, gained prominence. Researchers and policymakers began addressing these concerns to ensure responsible use of generative technologies.
The historical progression of generative AI highlights the iterative nature of technological development, driven by advancements in algorithms, computational power, and data availability. This evolution laid the foundation for contemporary generative AI models and their diverse applications across industries, setting the stage for further innovation and ethical considerations in the field.
1.2 Foundational Concepts
1.2.1 Neural Networks and Generative Models
Neural networks serve as the backbone for many generative models within the realm of generative AI. These models leverage neural network architectures to facilitate the generation of new content or data. Here is an overview of how neural networks are used in generative models:
Feedforward Neural Networks (FNNs):
- FNNs consist of interconnected layers of nodes, passing information in a unidirectional flow from input to output.
- While not inherently generative, FNNs can be used in generative settings, especially in early rule-based approaches where explicit instructions guide the generation process.
Recurrent Neural Networks (RNNs):
- RNNs are designed to process sequential data by utilizing loops within the network, allowing information to persist over time.
- RNNs are employed in language generation tasks where context and sequence are crucial, but they face challenges in modeling long-range dependencies due to vanishing/exploding gradient problems.
Variational Autoencoders (VAEs) with Neural Networks:
- VAEs employ neural networks as encoders and decoders. These networks learn to encode input data into a latent space representation and decode it back to generate output data.
- The encoder and decoder networks in VAEs are typically composed of feedforward or convolutional neural networks.
Generative Adversarial Networks (GANs) with Neural Networks:
- GANs consist of two neural networks-the generator and the discriminator-working in tandem.
- The generator uses neural networks to transform random noise into data that resembles real data, while the discriminator uses neural networks to distinguish between real and generated data.
Transformer Models:
- Transformer models, like the ones used in GPT series, employ attention mechanisms and self-attention layers to process and generate sequences.
- These models leverage neural networks to generate text, allowing for longer-range dependencies and capturing intricate patterns in the data.
Neural networks serve as the computational backbone for various generative models, allowing for the creation of sophisticated architectures capable of generating diverse and high-quality content across different modalities-such as text, images, audio, and more. The advancements in neural network architectures and training methodologies continue to drive innovation in generative AI, enabling the development of more powerful and versatile generative models.
1.2.2 Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs)
Variational autoencoders (VAEs) and generative adversarial networks (GANs) represent two powerful paradigms in generative AI, each with unique approaches to generating new content. Here's an exploration of their key characteristics and differences:
- Variational Autoencoders (VAEs):
- Objective: VAEs aim to learn a latent representation of data by mapping it to a lower-dimensional space and reconstructing it back to the original form.
- Architecture: VAEs consist of an encoder and a decoder. The encoder maps input data to a latent space, producing mean and variance parameters that describe a probabilistic distribution. The decoder generates data from samples drawn from...
