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Generative Artificial Intelligence : Concepts and Applications
| Generative Artificial Intelligence : Concepts and Applications |
| Autore | Nidhya R |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (0 pages) |
| Altri autori (Persone) |
PavithraD
KumarManish Dinesh KumarA BalamuruganS |
| Collana | Industry 5. 0 Transformation Applications Series |
| Soggetto topico |
Artificial intelligence
Generative programming (Computer science) |
| ISBN |
9781394209835
1394209835 9781394209811 1394209819 9781394209828 1394209827 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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) -- 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).
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. 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 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. 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. |
| Record Nr. | UNINA-9911019579803321 |
Nidhya R
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Tele-Healthcare : Applications of Artificial Intelligence and Soft Computing Techniques
| Tele-Healthcare : Applications of Artificial Intelligence and Soft Computing Techniques |
| Autore | Nidhya R |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (418 pages) |
| Altri autori (Persone) |
KumarManish
BalamuruganS |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-84193-3
1-119-84192-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910585797203321 |
|
Nidhya R
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||