{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":26,"n_cols":5,"rows_kept":26,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[11,2,11,3],[12,2,12,3],[13,2,13,3],[14,2,14,3],[16,2,16,3],[17,2,17,3],[19,2,19,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911019579803321"]
[null,null,"Autore",null,"Nidhya R"]
[null,null,"Titolo",null,"Generative Artificial Intelligence : Concepts and Applications"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"9781394209835"]
[{"$nulls":4},"1394209835"]
[{"$nulls":4},"9781394209811"]
[{"$nulls":4},"1394209819"]
[{"$nulls":4},"9781394209828"]
[{"$nulls":4},"1394209827"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (0 pages)"]
[null,null,"Collana",null,"Industry 5. 0 Transformation Applications Series"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Generative programming (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Exploring the Creative Frontiers: Generative AI Unveiled -- 1.1 Introduction -- 1.1.1 Definition and Significance of Generative AI -- 1.1.2 Historical Overview and Development -- 1.2 Foundational Concepts -- 1.2.1 Neural Networks and Generative Models -- 1.2.2 Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) -- 1.3 Applications Across Domains -- 1.3.1 Creative Arts: Music, Visual Arts, Literature -- 1.3.2 Content Generation: Text, Images, Videos -- 1.3.3 Scientific Research and Data Augmentation -- 1.3.4 Healthcare and Drug Discovery -- 1.3.5 Gaming and Virtual Environments -- 1.4 Ethical Considerations -- 1.5 Future Prospects and Challenges -- 1.6 Conclusion -- Reference -- Chapter 2 An Efficient Infant Cry Detection System Using Machine Learning and Neuro Computing Algorithms -- 2.1 Introduction -- 2.2 Literature Survey -- 2.3 Methodology -- 2.3.1 Database -- 2.3.2 Feature Extraction -- 2.3.2.1 Short-Term Energy -- 2.3.2.2 Mel-Frequency Cepstral Coefficients -- 2.3.2.3 Spectrograms -- 2.3.3 Classification -- 2.3.4 Convolutional Neural Network (CNN) -- 2.3.5 Recurrent Neural Network (RNN) -- 2.3.6 Regularized Discriminant Analysis (RDA) -- "]
[{"$nulls":4},"2.3.7 Multi-Layer Perceptron (MLP) -- 2.4 Experimental Results -- 2.5 Conclusion -- References -- Chapter 3 Improved Brain Tumor Segmentation Utilizing a Layered CNN Model -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Methodology -- 3.4 Numerical Results -- 3.5 Conclusion -- References -- Chapter 4 Natural Language Processing in Generative Adversarial Network -- 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)."]
[{"$nulls":4},"4.3.2 Working of Generative Adversarial Transformer (GAT) -- 4.3.2.1 Steps to Incorporate NLP in GAN -- 4.3.3 Implementation of NLP in GAN -- 4.3.4 Generate the Image Using Textual Description -- 4.3.5 Text Summarization -- 4.3.5.1 Graph-Based Summarization -- 4.4 Conclusion -- References -- Chapter 5 Modeling A Deep Learning Network Model for Medical Image Panoptic Segmentation -- 5.1 Introduction -- 5.2 Related Works -- 5.3 Methodology -- 5.3.1 Deep Masking Convolutional Model (DMCM) -- 5.4 Numerical Results and Discussion -- 5.5 Conclusion -- References -- Chapter 6 A Hybrid DenseNet Model for Dental Image Segmentation Using Modern Learning Approaches -- 6.1 Introduction -- 6.2 Related Works -- 6.3 Methodology -- 6.3.1 Dataset -- 6.3.2 Dense Transformer Model -- 6.3.3 DenseNet Model -- 6.4 Numerical Results and Discussion -- 6.4.1 Discussion -- 6.5 Conclusion -- References -- Chapter 7 Modeling A Two-Tier Network Model for Unconstraint Video Analysis Using Deep Learning -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Methodology -- 7.4 Numerical Results and Discussion -- 7.5 Conclusion -- References -- Chapter 8 Detection of Peripheral Blood Smear Malarial Parasitic Microscopic Images Utilizing Convolutional Neural Network -- 8.1 Introduction -- 8.2 Malaria -- 8.2.1 Malaria-Infected Red Blood Cells with Types -- 8.3 Literature Survey -- 8.4 Proposed Methodology and Algorithm -- 8.4.1 Proposed Algorithm -- 8.5 Result Analysis -- 8.5.1 Dataset -- 8.5.2 Preprocessing of Data -- 8.5.3 Splitting of Dataset -- 8.5.4 Classification -- 8.5.5 Model Prediction and Performance Metrics -- 8.5.6 CNN Learning Curves -- 8.6 Discussion -- 8.7 Conclusion -- 8.8 Future Scope -- References -- Chapter 9 Exploring the Efficacy of Generative AI in Constructing Dynamic Predictive Models for Cybersecurity Threats: A Research Perspective -- 9.1 Introduction."]
[{"$nulls":4},"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 -- Chapter 10 Poultry Disease Detection: A Comparative Analysis of CNN, SVM, and YOLO v3 Algorithms for Accurate Diagnosis -- 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 -- Chapter 11 Generative AI-Enhanced Deep Learning Model for Crop Type Analysis Based on Clustered Feature Vectors and Remote Sensing Imagery -- 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 -- Chapter 12 Cardiovascular Disease "]
[{"$nulls":4},"Prediction with Machine Learning: An Ensemble-Based Regressive Neighborhood Model -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 Pre-Processing -- 12.3.2 Feature Selection -- 12.3.3 Classification -- 12.4 Numerical Results and Discussion -- 12.5 Conclusion -- References -- Chapter 13 Detection of IoT Attacks Using Hybrid RNN-DBN Model -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Methodology -- 13.3.1 Dataset Used -- 13.3.2 Data Preprocessing -- 13.3.3 Data Normalization -- 13.3.4 Multi-Class Classification."]
[{"$nulls":4},"13.3.5 Splitting Dataset -- 13.3.6 RNN-DBN -- 13.4 Experiments and Results -- 13.5 Conclusion and Future Scope -- References -- Chapter 14 Identification of Foliar Pathologies in Apple Foliage Utilizing Advanced Deep Learning Techniques -- 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 -- Chapter 15 Enhancing Cloud Security Through AI-Driven Intrusion Detection Utilizing Deep Learning Methods and Autoencoder Technology -- 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 -- Chapter 16 YouTube Comment Analysis Using LSTM Model -- 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 -- Also of Interest -- EULA."]
[null,null,null,"Sommario/riassunto","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."]