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250110t20242024sz |||||o|||||||||||eng d |
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|a EIZ
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|d 2025-01-10
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|a 978-3-031-46238-2
|q (electronic bk.)
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|a (MiAaPQ)EBC31200851
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|x Umělá inteligence
|2 Konspekt
|9 23
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|a Applications of generative AI /
|c Zhihan Lyu, editor
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|a Cham :
|b Springer International Publishing AG,
|c 2024
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|c ©2024
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|a 1 online zdroj (vii, 617 stran)
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|a text
|b txt
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|a počítač
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|a online zdroj
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|a Obsahuje bibliografie
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|a Intro -- Contents -- Generative AI as a Supportive Tool for Scientific Research -- 1 Generative AI History and Evolution -- 2 How Does GAI Models Work?-Training GAI Models: Data Sources and Techniques -- 3 The Difference Between Conventional ML and Deep Learning (DL) Systems, Generative AI Models and Prompt Based ML (PML) -- 4 GAI Model Usages and Tools -- 4.1 Pre-writning Stage -- 4.2 Writing Stage -- 4.3 Post-writing Stage -- 5 Optimizing and Utilizing GAI Models Through Prompt Engineering -- 6 Generative Models Evaluation and Metrics -- 7 Does Chat GAI Models Cheat? - GAI Pitfalls and Their Proposed Solutions -- 8 Generative AI the Next Generation: Challenges and Opportunities -- 9 Conclusions -- References -- Creating Ad Campaigns Using Generative AI -- 1 Introduction -- 2 Foundations -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Encoder-Decoder Architecture -- 4 Empirical Results -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 The Effect of Attention -- 4.4 The Effect of Reinforcement -- 4.5 The Effect of Pre-training -- 5 Conclusion -- References -- Unlocking the Potential of Generative Artificial Intelligence in Drug Discovery -- 1 Introduction -- 2 The Generative Model's Toolkit -- 2.1 The Power of Big Data in Drug Discovery -- 2.2 Molecular Representations -- 3 Generative Models: A Brief Overview -- 3.1 Artificial Neural Networks -- 3.2 (RNN)-Based Models -- 3.3 (AE)-Based Models -- 3.4 GAN-Based Models -- 3.5 Transfer Learning -- 3.6 Reinforcement Learning -- 3.7 Evaluation Metrics -- 4 Applications of Generative Models in Drug Design -- 4.1 3D Generative Models -- 4.2 Success Stories in Early Drug Discovery -- 5 Concluding Remarks and Future Perspectives -- References -- Privacy in Generative Models: Attacks and Defense Mechanisms -- 1 Introduction -- 2 Generative Adversarial Network -- 3 Privacy Attacks in Generative Models.
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|a 3.1 Membership Inference Attack -- 3.2 Model Inversion Attacks -- 3.3 Future Directions -- 4 Privacy-Preserving Mechanisms in GANs -- 4.1 Differential Privacy -- 4.2 Privacy-Preserving Approaches in GANs -- 4.3 Future Directions -- 5 Summary -- References -- Generative Adversarial Network for Synthetic Image Generation Method: Review, Analysis, and Perspective -- 1 Introduction -- 2 Generative Adversarial Networks (GAN) Model -- 3 Formula of Various GANs Model -- 4 Review of the Applications of GANs to Synthetic Image Generation Studies -- 5 Conclusions -- References -- Image Rendering with Generative Adversarial Networks -- 1 Introduction -- 2 Neural Rendering -- 2.1 Image-Based Neural Rendering -- 2.2 Geometric Neural Rendering -- 3 Generative Models -- 3.1 Brief History of Generative Adversarial Networks -- 3.2 Basic GAN Architecture -- 3.3 GAN-Based Methods for Image Synthesis -- 4 Applications of Generative Models for Image Rendering -- 5 Challenges and Future Directions -- 6 Conclusion -- References -- Dsmk-DcSeg-Lap, a Generative Adversarial Network Guided by Dark-Chanel and Segmentation to Smoke Removal in Laparoscopic Images -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Artificial Neural Network -- 3.2 U-Net -- 3.3 Conditional GAN y Pix2Pix -- 4 Dark-Channel and Segmentation -- 4.1 Hazy Image Formation -- 4.2 Dark Channel Prior (DCP) -- 4.3 Segmentation -- 5 GAN Powered by Dark-Channel and Segmentation -- 5.1 Training -- 6 Metrics -- 7 Results and Discussion -- 7.1 Quantitative Analysis -- 7.2 Qualitative Analysis -- 8 Conclusion -- References -- Generative AI Use in the Construction Industry -- 1 A General Overview for Generative AI -- 2 Technological Requirements for Generative AI Application in the AEC-FM Industry -- 2.1 Internet of Things (IoT) -- 2.2 Distributed Ledger Technology -- 2.3 Computing Technologies.
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|a 2.4 Deep Learning -- 2.5 Natural Language Processing -- 2.6 Knowledge Graph -- 2.7 Computer Vision -- 2.8 Immersive Technologies -- 3 Generative AI Applications in the AEC-FM Industry -- 4 BIM as a Generative AI Facilitator for the AEC-FM Industry Applications -- 5 Gaps and Trends for Generative AI Research in the AEC-FM Industry -- 5.1 Keyword Co-occurrence Analysis for Generative AI Use in the AEC-FM Industry -- 6 Future Prospects for Generative AI in the AEC-FM Activities -- References -- Generative AI Applications in the Health and Well-Being Domain: Virtual and Robotic Assistance and the Need for Niche Language Models (NLMs) -- 1 Applications -- 2 Recent Advances -- 3 Bots-Non-embodied, Virtual Health Assistants -- 4 Robotic Assistants and Companions -- 5 Advantages and Challenges-The Case of Nursing -- 5.1 Adults -- 5.2 Elders -- 5.3 Children ---6 Summary-Limitations and Future Challenges -- References -- Generative Adversarial Network Based Deep Learning Method for Machine Vision Inspection -- 1 Introduction -- 2 Automatic Generation Method of Image Samples Besed on Generative Adversarial Network -- 2.1 Schematic Diagram of CycleGAN Method -- 3 Automatic Defect Image Inspection Based on Generative Adversarial Networks -- 3.1 Unsupervised Generative Adversarial Network Model -- 4 Experimental Results and Analysis -- 4.1 Experiments and Results Foe CycleGan Based Dataset Augmentation -- 4.2 Experimental Results and Analysis for Automatic Defect Image Inspection Based on Generative Adversarial Networks -- 5 Conclusion -- References -- Generative Adversarial Networks for Stain Normalisation in Histopathology -- 1 Histopathology -- 2 Style Transfer -- 2.1 Generative Models -- 2.2 Similarity Metrics -- 2.3 Loss Functions -- 2.4 Multi-generator Methods ---3 Stain Normalisation -- 3.1 Traditional Normalisation -- 4 Generative Stain Normalisation.
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|a 4.1 Single Generator Normalisation Approaches -- 4.2 Multi-generator Normalisation Approaches -- 5 Augmentation and Synthesis in Histopathology -- 6 Conclusion -- References -- Augmenting Data from Epileptic Brain Seizures Using Deep Generative Networks -- 1 Introduction -- 2 Characterization of Brain Seizures in Vitro -- 3 Capturing Epileptic Waves with a Deep GAN Model -- 4 Training a Convolutional Network on Augmented Seizure Data -- 5 Discussion -- 6 Conclusions -- References -- Can Generative Artificial Intelligence Foster Belongingness, Social Support, and Reduce Loneliness? A Conceptual Analysis -- 1 Introduction -- 2 Literature -- 2.1 Artificial Intelligence -- 2.2 Sense of Belonging, Loneliness, and Social Support -- 3 Short-Term Artificial Intelligence Social Gains? -- 4 The Dark Side of Chatbot Social Interaction -- 5 Conclusion -- References -- The SEARCH for AI-Informed Wellbeing Education: A Conceptual Framework -- 1 Introduction -- 2 Why Use Generative AI in Wellbeing Education? -- 3 The SEARCH for AI-Driven Wellbeing Education -- 3.1 Strengths -- 3.2 Emotional Management -- 3.3 Attention and Awareness -- 3.4 Relationships -- 3.5 Coping -- 3.6 Habits and Goals -- 3.7 Summary of AI-Enhanced SEARCH Framework -- 4 Conclusion -- References -- Generative AI to Understand Complex Ecological Interactions -- 1 Introduction -- 2 Biodiversity in Restoration Ecology -- 3 Plant Interactions -- 4 Distribution Learning for Vegetation Patches -- 5 Transfer Learning in Community Ecology -- 6 Data Augmentation for Plant Communities -- 7 Synthetic Data for Successional Trajectories -- 8 Summary -- References -- On the Effect of Loss Function in GAN Based Data Augmentation for Fault Diagnosis of an Industrial Robot -- 1 Introduction -- 2 Methodology -- 2.1 On GAN -- 2.2 On Wasserstein GAN and Conditional Wasserstein GAN -- 2.3
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|a 2.4 On SW-CycleGAN -- 3 Experiments -- 3.1 The Industrial Robot Test Rig and Data Set -- 3.2 Experimental Settings of VGAN -- 3.3 Experimental Settings of SW-CycleGAN -- 4 Results and Discussion -- 4.1 Results on VGAN -- 4.2 Results on SW-CycleGAN -- 5 Conclusion -- References -- Underwater Acoustic Noise Modeling Based on Generative-Adversarial- Network -- 1 Introduction -- 2 Analysis of Real Underwater Noise -- 3 GAN-Based Underwater Noise Simulator -- 3.1 Introduction of GAN -- 3.2 GAN Structure for Noise Simulations -- 4 Numerical Simulations and Discussions -- 4.1 Parametric Configuration of GAN-Based Noise Simulator -- 4.2 Accuracy Analysis for GAN and Traditional Non-gaussian Models -- 4.3 Complexity Analysis -- 5 Conclusions -- References -- How Generative AI Is Transforming Medical Imaging: A Practical Guide -- 1 Generative AI for Images: What It Is and How It Works ---2 GANs, VAEs and Diffusion Models: What Are They? -- 3 How Can Generative AI Help Medical Imaging? -- 4 What Are the Pros and Cons of Generative AI for Medical Imaging? -- 5 How Generative AI Has Improved Medical Imaging: A Showcase of Successful Applications -- 5.1 Generating Mammogram Images with Contextual Information Using GANs -- 5.2 Augmenting Medical Images Using Semi-supervised GANs and Attention Mechanism -- 5.3 EndoVAE: An Innovative Variational Autoencoder for Endoscopic Image Generation -- 5.4 Restoring Medical Images with Variational Autoencoders -- 5.5 Brain Imaging Made Easy with Latent Diffusion Models -- 5.6 Creating Realistic 3D Medical Images with Denoising Diffusion Models -- 6 Generative AI Outlook: A Game-Changer for Healthcare and Biomedical Research -- 6.1 Key Takeaways and Findings of the Chapter ---6.2 Recommendations for Generative AI Users and Stakeholders -- 6.3 Future Directions for Generative AI Research for Medical Imaging -- References.
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|a Generative AI in Medical Imaging and Its Application in Low Dose Computed Tomography (CT) Image Denoising.
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| 533 |
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|a Elektronická reprodukce.
|b Ann Arbor, Michigan :
|c ProQuest Ebook Central,
|d 2024.
|n Přístup pouze pro oprávněné uživatele
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| 650 |
0 |
7 |
|a umělá inteligence
|7 ph116536
|2 czenas
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|7 ko2008435111
|4 pbl
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| 776 |
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|i Tištěná verze:
|t Applications of generative AI
|d Cham : Springer International Publishing AG, c2024
|z 978-3-031-46237-5
|
| 856 |
4 |
1 |
|z Elektronická verze přístupná pouze pro studenty a pracovníky MU
|u https://ebookcentral.proquest.com/lib/masaryk-ebooks/detail.action?docID=31200851
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