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Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises : Theoretical Insights with Practical Applications / / edited by Abhishek Kumar, Fernando Ortiz-Rodriguez, Jose Braga De Vasconcelos, Pushan Kumar Dutta, Hemant Kumar Saini, Pramod Singh Rathore
| Adversarial Deep Generative Techniques for Early Diagnosis of Neurological Conditions and Mental Health Practises : Theoretical Insights with Practical Applications / / edited by Abhishek Kumar, Fernando Ortiz-Rodriguez, Jose Braga De Vasconcelos, Pushan Kumar Dutta, Hemant Kumar Saini, Pramod Singh Rathore |
| Autore | Kumar Abhishek |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (474 pages) |
| Disciplina | 620.00285 |
| Altri autori (Persone) |
Ortiz-RodriguezFernando
De VasconcelosJose Braga Kumar DuttaPushan SainiHemant Kumar RathorePramod Singh |
| Collana | Information Systems Engineering and Management |
| Soggetto topico |
Engineering - Data processing
Biomedical engineering Computational intelligence Neurosciences Data Engineering Biomedical Engineering and Bioengineering Computational Intelligence Neuroscience |
| ISBN | 3-031-91147-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Virtual Ai Assistant Ai In Mental Healthcare -- Leveraging Deep Generative Models For Early Diagnosis And Personalized Care In Neurological And Mental Health Disorders -- A Comprehensive Review Of Deep Generative Techniques In The Study And Management Of Neurological Disorders -- Advancements In Neuroimaging And Deep Learning A Review Of Core Principles, Methodologies, And Emerging Applications -- Ethical Considerations And Regulatory Compliance In Ai Driven Diagnostics -- Neuro Imaging Based Alzheimerdisease Detection By Segmentation With Classification Using Machine Learning Algorithms -- Neuro Imaging Based Alzheimer Disease Detection Using Generative Adversarial Model With Deep Learning Algorithm -- Early Diagnosis Of Alzheimer’s Disease Using Adversarial Techniques -- Classification Of Mental Disorder With Deep Generative Models -- Practical Implementation And Integration Of Ai In Mental Healthcare. |
| Record Nr. | UNINA-9911015628503321 |
Kumar Abhishek
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI and Wind Power 1 : A Multifaceted Approach to Sustainable Energy
| AI and Wind Power 1 : A Multifaceted Approach to Sustainable Energy |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (299 pages) |
| Disciplina | 621.312136 |
| Altri autori (Persone) |
Kumar TAnanth
DubeyAshutosh Kumar SrivastavArun Lal Juarez-RamirezJ. Reyes |
| Collana | ISTE Invoiced Series |
| ISBN | 9781394464234 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Harnessing the Power of the Wind: A Detailed Exploration of Wind Energy Fundamentals and the Pivotal Role of Emerging AI Techniques -- 1.1. Introduction to wind energy -- 1.1.1. Overview of wind energy as a renewable resource -- 1.1.2. Historical development and global growth of wind energy -- 1.1.3. Key benefits and challenges of wind energy deployment -- 1.2. Fundamentals of wind energy generation -- 1.2.1. The science behind wind power -- 1.2.2. Wind turbine design and components -- 1.2.3. Energy conversion mechanisms -- 1.2.4. Factors influencing wind energy production -- 1.3. Wind energy systems and grid integration -- 1.3.1. Integration of wind power into the electrical grid -- 1.3.2. Challenges of intermittency and variability -- 1.3.3. Energy storage solutions and their role -- 1.3.4. Case studies in grid integration of wind energy -- 1.4. Emerging AI techniques in wind energy -- 1.4.1. Overview of AI in energy systems -- 1.4.2. Machine learning and data analytics for wind power -- 1.4.3. AI-based optimizations in wind turbine operation -- 1.4.4. Harnessing AI for enhanced wind farm efficiency -- 1.5. Predictive maintenance and performance monitoring -- 1.5.1. The need for predictive maintenance in wind energy -- 1.5.2. AI-driven maintenance models and algorithms -- 1.5.3. Real-time data monitoring and fault detection -- 1.5.4. Case studies on AI in predictive maintenance -- 1.6. AI in wind farm design and layout optimization -- 1.6.1. AI approaches in wind resource assessment -- 1.6.2. Optimizing wind farm layout using AI algorithms -- 1.6.3. Simulation and modeling tools for wind farm design -- 1.6.4. Performance enhancements through AI-optimized designs -- 1.7. Energy forecasting and demand response with AI.
1.7.1. The importance of accurate wind energy forecasting -- 1.7.2. AI-based forecasting models -- 1.7.3. Integration of AI in demand response systems -- 1.7.4. Impact of AI on wind energy market dynamics -- 1.8. Future trends and innovations in wind energy and AI -- 1.8.1. The role of AI in advancing wind energy technology -- 1.8.2. Innovations in smart wind turbines and sensors -- 1.8.3. The future of AI-enhanced wind farm operations -- 1.8.4. Prospects for wind energy in the global energy transition -- 1.9. Conclusion -- 1.9.1. Summary of key findings -- 1.9.2. Implications for the future of wind energy -- 1.9.3. Challenges and opportunities ahead -- 1.10. References -- Chapter 2. A Terrain-Fused Spatio-Temporal Deep Learning Framework for Accurate Wind Resource Assessment and Forecasting Using TFS-TWF -- 2.1. Introduction -- 2.2. Literature survey -- 2.3. Proposed work -- 2.4. TFS-TWF proposed architecture -- 2.5. Implementation and methodology -- 2.6. Results and discussion -- 2.7. Performance in Region C's hilly terrain -- 2.8. Projected horizon performance -- 2.9. Study of ablation -- 2.10. Estimating uncertainty and dependability -- 2.11. Summary of comparative performance -- 2.12. Prospects and remarks -- 2.13. Conclusion -- 2.14. References -- Chapter 3. Deep Fuzzy-Optimized CLSTM-BERT Algorithm with Adaptive Learning for Efficient Wind Farm Design and Energy Management -- 3.1. Introduction -- 3.2. Literature review -- 3.3. Proposed methodology -- 3.4. Results and discussion -- 3.5. Conclusion -- 3.6. References -- Chapter 4. Optimizing Wind Energy with AI -- 4.1. Introduction -- 4.2. Literature review -- 4.2.1. AI applications in renewable energy -- 4.2.2. Wind forecasting techniques -- 4.2.3. Turbine control systems -- 4.2.4. Predictive maintenance -- 4.2.5. Energy storage and grid integration -- 4.3. Methodology. 4.3.1. Research approach (qualitative and quantitative) -- 4.3.2. Data sources -- 4.3.3. AI models and algorithms used -- 4.4. AI applications in wind energy -- 4.4.1. Wind speed and power forecasting -- 4.4.2. Predictive maintenance and fault detection -- 4.4.3. Smart control systems -- 4.4.4. Site selection optimization -- 4.4.5. Integration with smart grids -- 4.5. Case studies and real-world applications -- 4.5.1. Siemens Gamesa: using AI for smart turbines -- 4.5.2. GE Renewable Energy: smart wind farms -- 4.5.3. DeepMind and Google: AI forecasting for wind energy -- 4.5.4. National wind projects using AI -- 4.6. AI versus traditional methods: what is the difference? -- 4.7. Challenges and limitations of using AI in wind energy -- 4.7.1. Data quality and availability -- 4.7.2. Interpretability of AI models -- 4.7.3. Cost and infrastructure -- 4.7.4. Cybersecurity concerns -- 4.8. Future outlook and recommendations -- 4.8.1. Innovations in AI for wind technology -- 4.8.2. Policy and investment recommendations -- 4.9. Scalability in developing countries -- 4.9.1. Use low-cost AI tools -- 4.9.2. Local language training -- 4.9.3. Mobile-based AI apps -- 4.9.4. Build community-based wind farms -- 4.9.5. Partner with global organizations -- 4.10. Conclusion -- 4.11. References -- Chapter 5. Integration of Artificial Intelligence and Wind Power: An Orientation Technique -- 5.1. Introduction -- 5.2. Introduction to wind power -- 5.2.1. Wind energy conversion technology -- 5.2.2. Permanent magnet alternator -- 5.2.3. Power point tracker in WECS -- 5.2.4. Optimization techniques for MPPT -- 5.3. Overview of AI in renewable energy -- 5.4. AI applications in wind power -- 5.4.1. Wind resource assessment and forecasting -- 5.4.2. Wind turbine monitor and error analysis -- 5.4.3. Control and optimization of wind turbines. 5.4.4. Energy management and grid integration -- 5.5. Popular AI techniques in wind power -- 5.5.1. Automated learning -- 5.5.2. Deep learning -- 5.5.3. Trial-and-error learning -- 5.5.4. Hybrid model -- 5.6. Challenges of AI in wind power -- 5.6.1. Data quality and readiness -- 5.6.2. Computational complexity and resources -- 5.6.3. Model interpretability and trustworthiness -- 5.6.4. Integration with existing systems -- 5.6.5. Managing uncertainty and non-stationary environments -- 5.7. Future trends and directions for research -- 5.7.1. An AI-enabled digital twin for wind farms -- 5.7.2. Integration of AI with IoT and edge computing -- 5.7.3. Explainable and trustworthy AI -- 5.7.4. AI for offshore wind and autonomous maintenance -- 5.7.5. Hybrid renewable energy systems and AI optimization -- 5.7.6. AI-enhanced atmospheric and aerodynamic modeling -- 5.8. Conclusion -- 5.9. References -- Chapter 6. Predictive Maintenance and Fault Diagnosis of Wind Turbines Using AI -- 6.1. Introduction -- 6.2. Related work -- 6.2.1. Early approaches: rule-based and statistical methods -- 6.2.2. Machine learning for condition monitoring -- 6.2.3. Deep learning for fault detection and prognostics -- 6.2.4. Hybrid and ensemble models -- 6.2.5. Transfer learning and domain adaptation -- 6.2.6. Digital twins and real-time systems -- 6.3. Methodology -- 6.3.1. Data acquisition -- 6.3.2. Data preprocessing -- 6.3.3. Feature engineering -- 6.3.4. Model selection and training -- 6.3.5. Anomaly detection and RUL prediction -- 6.3.6. Model evaluation -- 6.3.7. Real-time deployment and system integration -- 6.3.8. Visualization and decision support -- 6.4. Results and discussion -- 6.4.1. Classification model evaluation -- 6.4.2. Anomaly detection performance -- 6.4.3. RUL prediction -- 6.5. Conclusion and future work -- 6.6. References. Chapter 7. A Comprehensive Review of Digital Twin-Enabled AI Models for Interpretable and Scalable Fault Diagnosis in Wind Turbines -- 7.1. Introduction -- 7.2. Overview of wind turbine fault diagnosis -- 7.2.1. Fault types and operational complexity -- 7.2.2. Traditional monitoring approaches and limitations -- 7.3. Digital twin technology: concept and role in wind turbines -- 7.3.1. Understanding the digital twin paradigm -- 7.3.2. Applications and advantages in fault diagnosis -- 7.4. AI techniques in fault diagnosis -- 7.4.1. Overview of AI in the context of wind turbines -- 7.4.2. Machine learning methods for predictive maintenance -- 7.4.3. Deep learning for complex and multimodal data -- 7.4.4. Reinforcement learning and adaptive control -- 7.4.5. Hybrid and transfer learning approaches -- 7.5. Integration of digital twin and AI: synergies and architectures -- 7.5.1. Synergistic integration and benefits -- 7.5.2. Architectural patterns for DT-AI systems -- 7.5.3. Implementation considerations -- 7.6. Explainable AI for interpretability -- 7.6.1. The need for transparency in AI-based fault diagnosis -- 7.6.2. Methods for achieving explainability in wind turbine applications -- 7.7. Sensor fusion and multimodal data integration -- 7.7.1. Importance and techniques of data fusion -- 7.7.2. Practical challenges and considerations -- 7.8. Uncertainty quantification in AI models -- 7.8.1. Methods for capturing predictive uncertainty -- 7.8.2. Applications in wind turbine maintenance -- 7.9. Validation strategies and simulation frameworks -- 7.9.1. Simulation-based validation -- 7.9.2. Experimental and field validation -- 7.10. Scalability considerations for large wind farms -- 7.10.1. Challenges in scaling DT-AI systems -- 7.10.2. Solutions for scalable deployment -- 7.11. Open challenges and future directions. 7.11.1. Current limitations and research gaps. |
| Record Nr. | UNINA-9911117575803321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI and Wind Power 2 : Advancing Sustainability, Grid Integration, and Future Frameworks
| AI and Wind Power 2 : Advancing Sustainability, Grid Integration, and Future Frameworks |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (282 pages) |
| Disciplina | 621.042 |
| Collana | ISTE Invoiced Series |
| Soggetto topico |
Renewable energy sources
Artificial intelligence |
| ISBN | 9781394464258 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Table of Contents -- Title Page -- Copyright Page -- Preface -- 1 AI-Driven Advanced Smart Grid with Optimized Hybrid Renewable Energy Systems -- 2 Implementation of an AI-Driven Hybrid Renewable Energy Management System Using Deep Fuzzy-Based Particle Swarm Optimization (DFB-PSO) -- 3 Generative AI for Hybrid Renewable Energy Systems (Solar-Wind-Hydro Integration) -- 4 AI for Enhancing Sustainability in Wind Energy -- 5 Intelligent Energy with AI-Driven Innovations in Wind Power Systems -- 6 Economic and Market Impacts of AI in Wind Power -- 7 AI for Policy and Regulatory Frameworks in Wind Power -- 8 Implementation of an AI-Driven Wind Energy Sustainability Framework Using Reinforcement Learning-Optimized Deep Neuro-Fuzzy Controller (RL-DNFC) -- 9 AI in Offshore Wind Energy Systems -- 10 Emerging AI Innovations in Wind Power -- 11 Generative AI for Energy Consumption Behavior Analysis -- 12 Wind Power Forecasting for Grid Stability Enhancement with Effective Integration of AI Techniques -- List of Authors -- Index -- Other titles from iSTE in Computer Engineering -- End User License Agreement. |
| Record Nr. | UNINA-9911117575203321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI-Driven Healthcare Innovations : Applications in Neurology and Medicine
| AI-Driven Healthcare Innovations : Applications in Neurology and Medicine |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (395 pages) |
| Disciplina | 610.28563 |
| Collana | ISTE Invoiced Series |
| ISBN |
1-394-45203-9
1-394-45201-2 9781394452019 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Artificial Intelligence in Healthcare: Principles, Paradigms and Emerging Trends -- 1.1. Introduction -- 1.1.1. History of AI in healthcare -- 1.2. Principles of AI in healthcare -- 1.2.1. Foundational concepts -- 1.2.2. Core AI methodologies -- 1.2.3. Ethical and philosophical principles -- 1.3. Paradigms of AI in healthcare -- 1.3.1. Diagnostic paradigms -- 1.3.2. Therapeutic paradigms -- 1.3.3. Preventive and predictive paradigms -- 1.4. Emerging trends in AI-driven healthcare -- 1.4.1. Explainable AI (XAI) and interpretability -- 1.4.2. Federated and privacy-preserving learning -- 1.4.3. Multimodal and hybrid AI models -- 1.4.4. Blockchain and AI synergies -- 1.4.5. Generative AI in healthcare -- 1.5. Challenges and limitations -- 1.5.1. Bearing data quality and interoperability -- 1.5.2. Algorithmic bias and fairness -- 1.5.3. Regulatory/legal challenges -- 1.5.4. Clinical validation and barriers to adoption -- 1.5.5. Cost, scalability and infrastructure -- 1.6. Future directions -- 1.7. Conclusion -- 1.8. References -- Chapter 2. Machine Learning Models for Diagnostic Decision-Making in Neurology -- 2.1. Introduction -- 2.2. Overview of ML in healthcare -- 2.3. Supervised learning models in neurological diagnosis -- 2.3.1. SVMs -- 2.3.2. Decision trees and random forests -- 2.3.3. Logistic regression and linear models -- 2.3.4. Neural network-based supervised models -- 2.4. Unsupervised and semi-supervised approaches -- 2.4.1. Unsupervised learning in neurological diagnostics -- 2.4.2. Semi-supervised learning in neurology -- 2.4.3. Advantages in neurology -- 2.4.4. Limitations and challenges -- 2.4.5. Emerging trends -- 2.5. DL for neuroimaging and signal analysis -- 2.5.1. CNNs on neuroimaging -- 2.5.2. RNNs and time-varying data.
2.5.3. Transformer architectures in neurology -- 2.5.4. Multimodal DL -- 2.5.5. Explainability in DL models -- 2.6. Multimodal and integrative diagnostic models -- 2.6.1. Rationale for multimodal integration -- 2.6.2. Fusion strategies in multimodal ML -- 2.6.3. Applications in neurology -- 2.7. XAI and clinical interpretability -- 2.7.1. Importance of explainability in neurology -- 2.7.2. Approaches to XAI -- 2.7.3. Clinical applications of XAI in neurology -- 2.8. Future directions in ML for neurological diagnostics -- 2.8.1. Federating and cooperative learning -- 2.8.2. Genomic and molecular data -- 2.8.3. Self-supervised learning and few-shot learning -- 2.9. Conclusion -- 2.10. References -- Chapter 3. Deep Learning Approaches to Neuroimaging and Brain Mapping -- 3.1. Introduction -- 3.2. Deep learning fundamentals for neuroimaging -- 3.2.1. Convolutional neural networks (CNNs) -- 3.2.2. Recurrent neural networks (RNNs) and long short-term memory (LSTM) -- 3.2.3. Autoencoders and generative models -- 3.2.4. Transformer architectures -- 3.2.5. Training strategies and optimization -- 3.2.6. Interpretability and explainability -- 3.3. Applications in structural neuroimaging (MRI, CT) -- 3.3.1. Brain tissue segmentation and morphometric analysis -- 3.3.2. Cancer detection and classification -- 3.3.3. Lesion detection and abnormality detection -- 3.3.4. Diffusion MRI and microstructural analysis -- 3.3.5. CT imaging applications -- 3.3.6. Multimodal structural integration -- 3.4. Applications in functional neuroimaging (fMRI, PET, EEG/MEG) -- 3.4.1. Functional MRI (fMRI) -- 3.4.2. Positron emission tomography (PET) -- 3.4.3. Electroencephalography (EEG) -- 3.4.4. Magnetoencephalography (MEG) -- 3.4.5. Multimodal functional integration -- 3.5. Brain mapping and connectomics with deep learning -- 3.5.1. Structural connectomics. 3.5.2. Functional connectomics -- 3.5.3. Multiscale brain mapping -- 3.5.4. The research has a clinical/translational relevance -- 3.6. Clinical applications and translational potential -- 3.6.1. Neurodegenerative disorders -- 3.6.2. Neuro-oncology -- 3.6.3. Cerebrovascular disorders -- 3.6.4. Epilepsy and seizure prediction -- 3.7. Challenges, limitations and future directions -- 3.7.1. Data availability and quality -- 3.7.2. Generalizability and reproducibility -- 3.7.3. Interpretability and trust -- 3.7.4. Computational and resource demands -- 3.7.5. Future directions -- 3.8. Conclusion -- 3.9. References -- Chapter 4. Predictive Analytics for Early Detection of Neurodegenerative Disorders -- 4.1. Introduction -- 4.2. Predictive analytics framework for neurodegenerative disorders -- 4.2.1. Data acquisition -- 4.2.2. Data preprocessing and feature engineering -- 4.2.3. Model development -- 4.2.4. Clinical integration and decision support -- 4.2.5. Challenges and future directions -- 4.3. Applications of predictive analytics in specific neurodegenerative disorders -- 4.3.1. Alzheimer's disease -- 4.3.2. Parkinson's disease -- 4.3.3. Amyotrophic lateral sclerosis -- 4.3.4. Huntington's disease -- 4.4. Emerging trends and methodological advances -- 4.4.1. Multimodal data fusion -- 4.4.2. Longitudinal modeling -- 4.4.3. Deep learning and advanced AI techniques -- 4.4.4. Explainable artificial intelligence (XAI) -- 4.4.5. Federated learning and data privacy -- 4.4.6. Real-world digital health integration -- 4.5. Challenges, ethical considerations and future directions -- 4.5.1. Data-related challenges -- 4.5.2. Model-related challenges -- 4.5.3. Social and ethical exposures -- 4.5.4. Clinical translation and adoption -- 4.5.5. Future directions -- 4.6. Conclusion -- 4.7. References. Chapter 5. AI-Enhanced Stroke Diagnosis, Prognosis and Rehabilitation Pathways -- 5.1. Introduction -- 5.2. AI in stroke diagnosis -- 5.2.1. Importance of early and accurate diagnosis -- 5.2.2. AI applications based on neuroimaging -- 5.2.3. Stroke subtype differentiations -- 5.2.4. Incorporating multimodal biomarkers -- 5.2.5. The use of AI in rapid triage and workflow optimization -- 5.2.6. Challenges and limitations -- 5.3. AI in stroke prognosis -- 5.3.1. The need for accurate prognostic models -- 5.3.2. Predicting functional outcomes -- 5.3.3. Mortality and recurrence risk prediction -- 5.3.4. Treatment planning -- 5.3.5. Explainability and clinical trust -- 5.3.6. Integration with clinical workflow -- 5.4. AI in stroke rehabilitation pathways -- 5.4.1. Importance of personalized rehabilitation -- 5.4.2. Robotics-assisted rehabilitation -- 5.4.3. Wearable sensors and real-time monitoring -- 5.4.4. Neurofeedback and brain-computer interfaces -- 5.4.5. Virtual reality and game-based rehabilitation -- 5.4.6. The use of tele-rehabilitation and remote monitoring -- 5.4.7. Integration of multimodal data -- 5.5. Integration into clinical workflows -- 5.5.1. The need for seamless integration -- 5.5.2. Decision support systems in practice -- 5.5.3. Interoperability and data standards -- 5.5.4. Privacy and regulatory considerations -- 5.5.5. Acceptance and explainability of clinicians -- 5.6. Future directions -- 5.6.1. Understandable and reliable AI -- 5.6.2. Equity, bias mitigation and fairness -- 5.6.3. Federated and privacy-preserving learning -- 5.6.4. Multimodal and continuous prognostication -- 5.6.5. Scalable tele-rehabilitation and home-based therapy -- 5.6.6. Neurotechnology and advanced brain-computer interfaces -- 5.7. Conclusion -- 5.8. References -- Chapter 6. Computational Biomarker Discovery for Neurological and Psychiatric Disorders. 6.1. Introduction -- 6.1.1. Biomarkers defined -- 6.2. Computational approaches for biomarker discovery -- 6.2.1. Omics-based biomarker discovery -- 6.2.2. Imaging-based biomarkers -- 6.2.3. Electrophysiological signals and digital phenotyping -- 6.2.4. Multimodal data integration -- 6.3. Machine learning and AI in biomarker identification -- 6.3.1. Supervised learning approaches -- 6.3.2. Unsupervised learning approaches -- 6.3.3. Deep learning in biomarker discovery -- 6.3.4. Multimodal data fusion and integration -- 6.3.5. Explainable AI for biomarker discovery -- 6.3.6. Transfer learning and federated learning -- 6.4. Biomarkers in neurological disorders -- 6.4.1. Alzheimer's disease -- 6.4.2. Parkinson's disease -- 6.4.3. Epilepsy -- 6.5. Biomarkers in psychiatric disorders -- 6.5.1. Major depressive disorder -- 6.5.2. Schizophrenia -- 6.5.3. Bipolar disorder -- 6.5.4. Anxiety disorders -- 6.6. Challenges and future directions -- 6.6.1. Data heterogeneity and quality -- 6.6.2. Sample size and data scarcity -- 6.6.3. Interpretability and clinical adoption -- 6.6.4. Ethical, legal and privacy concerns -- 6.6.5. Future directions -- 6.7. Conclusion -- 6.8. References -- Chapter 7. Natural Language Processing for Clinical Narratives and Neurological Case Records -- 7.1. Introduction -- 7.2. NLP fundamentals in clinical narratives -- 7.3. Applications in neurology and case records -- 7.3.1. Stroke documentation and prognosis -- 7.3.2. Dementia and cognitive disorders -- 7.3.3. Epilepsy case records -- 7.3.4. Parkinson's disease and movement disorders -- 7.3.5. Uncommon and uncharacteristic neurological disorders -- 7.4. Advances in model architectures -- 7.4.1. Transformer models in clinical NLP -- 7.4.2. Large language models (LLMs) -- 7.4.3. Neurological multimodal NLP -- 7.4.4. Explainability and trustworthiness. 7.5. Clinical Utility: diagnosis, prognosis and treatment support. |
| Record Nr. | UNINA-9911073921703321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI-Driven Innovations in Physiotherapy and Oncology 3
| AI-Driven Innovations in Physiotherapy and Oncology 3 |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (425 pages) |
| Disciplina | 616.994062028563 |
| Collana | ISTE Invoiced Series |
| ISBN |
1-394-45222-5
1-394-45219-5 9781394452194 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. Reinforcement Learning Models for Adaptive Cancer Rehabilitation in Physiotherapy -- 1.1. Introduction -- 1.2. Fundamentals of reinforcement learning (RL) -- 1.2.1. Overview -- 1.2.2. Key components in cancer rehabilitation -- 1.3. Cancer rehabilitation needs and challenges -- 1.3.1. Functional deficits post-cancer treatment -- 1.3.2. Challenges in current rehabilitation protocols -- 1.4. RL-based framework for adaptive rehabilitation -- 1.4.1. System architecture -- 1.4.2. Data sources -- 1.4.3. Adaptive learning process -- 1.5. Benefits of RL in cancer physiotherapy -- 1.6. Limitations and ethical considerations -- 1.6.1. Privacy and security issues -- 1.6.2. Data bias -- 1.6.3. Interpretability -- 1.6.4. Dependency and dehumanization -- 1.7. Future directions -- 1.7.1. Multi-agent reinforcement learning (RL) -- 1.7.2. Transfer learning -- 1.7.3. Human-in-the-loop approaches -- 1.7.4. Integration with electronic health records (EHRs) -- 1.8. Conclusion -- 1.9. References -- Chapter 2. AI-Enabled Gait and Balance Assessment in Oncology Rehabilitation -- 2.1. Introduction -- 2.2. Clinical background: gait and balance in cancer survivors -- 2.3. AI-enabled gait and balance assessment: overview of technologies -- 2.3.1. Markerless computer vision/pose estimation -- 2.3.2. Wearable IMUs and smart sensing systems -- 2.3.3. Telehealth and mobile-based systems -- 2.3.4. Learnable and privacy-protective AI -- 2.3.5. VR and rehabilitation robotics are transforming -- 2.4. Evidence base and validation in the oncology setting -- 2.5. Core components of an AI-enabled oncology gaitâ€"balance platform -- 2.5.1. Non-intrusive and accessible data capture -- 2.5.2. AI models and analytics -- 2.5.3. Dashboard and reporting interface -- 2.5.4. Integration with rehabilitation prescription.
2.6. Implementation in oncology settings: use cases and workflow -- 2.6.1. Baseline and ongoing assessment -- 2.6.2. Personalized intervention and progress monitoring -- 2.6.3. Fall risk stratification and safety monitoring -- 2.6.4. Remote telerehabilitation -- 2.7. Challenges and considerations -- 2.7.1. Data quality and clinical validation -- 2.7.2. Bias and generalizability -- 2.7.3. Regulatory and integration hurdles -- 2.7.4. Patient engagement and equity. -- 2.8. Future directions and research needs -- 2.9. Conclusion -- 2.10. References -- Chapter 3. Deep Learning-Driven Fatigue Monitoring in Cancer Physiotherapy Programs -- 3.1. Introduction -- 3.2. Cancer-related fatigue and physiotherapy -- 3.3. Traditional and sensor-based monitoring -- 3.4. Deep learning models for fatigue monitoring -- 3.4.1. Forecasting symptom escalation -- 3.4.2. Sensor-based and multimodal fatigue -- 3.4.3. Assessing rehabilitation exercise -- 3.5. Integration into cancer physiotherapy -- 3.6. Applications and use cases -- 3.6.1. Real-time fatigue monitoring -- 3.6.2. Predictive warnings -- 3.6.3. Personalized therapy -- 3.7. Strengths, limitations and challenges -- 3.7.1. Strengths of DL-driven fatigue monitoring systems -- 3.7.2. Limitations and gaps in the current research -- 3.7.3. Ethical and operational challenges -- 3.8. Future directions -- 3.9. Conclusion -- 3.10. References -- Chapter 4. Predictive Modeling of Lymphedema Risk Using AI in Oncology Physiotherapy -- 4.1. Introduction -- 4.2. Clinical background: lymphedema in oncology -- 4.3. Rationale for predictive modeling -- 4.4. AI and ML overview -- 4.5. Model development approaches -- 4.5.1. Data collection -- 4.5.2. Feature engineering and selection -- 4.5.3. Model training and validation -- 4.6. Performance metrics and model comparisons -- 4.7. Explainability and clinical integration. 4.8. Role in oncology physiotherapy -- 4.9. Challenges and limitations -- 4.10. Future directions -- 4.11. Conclusion -- 4.12. References -- Chapter 5. AI-Based Movement Quality Scoring for Post-Chemotherapy Rehabilitation -- 5.1. Introduction -- 5.2. Impact of chemotherapy on physical function -- 5.2.1. Common side effects affecting mobility -- 5.2.2. Limitations of traditional movement assessment -- 5.2.3. Need for advanced assessment tools -- 5.3. AI technologies for movement quality assessment -- 5.3.1. Motion capture systems -- 5.3.2. Pose estimation and computer vision -- 5.3.3. Machine learning and deep learning models -- 5.3.4. Scoring metrics and interpretability -- 5.3.5. Real-time feedback systems -- 5.4. Clinical applications in post-chemotherapy rehabilitation -- 5.4.1. Gait analysis -- 5.4.2. Upper limb function assessment -- 5.4.3. Balance and fall risk detection -- 5.4.4. Remote rehabilitation and telehealth -- 5.5. Challenges and limitations -- 5.5.1. Data limitations -- 5.5.2. Interpatient variability -- 5.5.3. Sensor and environment constraints -- 5.5.4. Privacy and ethical issues -- 5.5.5. Clinical integration -- 5.6. Future directions and opportunities -- 5.6.1. Individualized rehabilitation programs -- 5.6.2. Multi-modal data fusion -- 5.6.3. Federated learning and data sharing -- 5.6.4. Gamification and engagement -- 5.6.5. Cross-domain application -- 5.7. Conclusion -- 5.8. References -- Chapter 6. Virtual Reality and AI for Pain Management in Cancer Physiotherapy -- 6.1. Introduction -- 6.2. Cancer pain: scope and challenges -- 6.2.1. Prevalence and impact -- 6.2.2. Traditional physiotherapy weakness -- 6.3. VR in pain management -- 6.3.1. What VR? -- 6.3.2. Mechanisms of VR for pain relief -- 6.3.3. Clinical applications of VR in cancer physiotherapy -- 6.3.4. Evidence-based observations -- 6.4. AI in pain management. 6.4.1. Role of AI in healthcare -- 6.4.2. AI in physiotherapy -- 6.4.3. AI-driven personalization -- 6.5. Integrating VR and AI: a synergistic approach -- 6.5.1. Real-time adaptive VR systems -- 6.5.2. Closed-loop feedback systems -- 6.5.3. Gamification and behavioral reinforcement -- 6.6. Case studies and clinical implementations -- 6.6.1. Use case I -- 6.6.2. Use case II -- 6.6.3. Pediatric cancer and VR distraction therapy -- 6.7. Technical and ethical considerations -- 6.7.1. Data privacy and consent -- 6.7.2. Bias in AI algorithms -- 6.7.3. Technology access and digital divide -- 6.8. Future directions -- 6.8.1. Multimodal integration -- 6.8.2. Remote and home-based rehabilitation -- 6.8.3. Prediction of chronic pain -- 6.8.4. AI sources and virtual therapists -- 6.9. Conclusion -- 6.10. References -- Chapter 7. Machine Learning for Optimizing Exercise Intensity in Oncology Rehabilitation -- 7.1. Introduction -- 7.2. Exercise intensity in oncology rehabilitation -- 7.2.1. Defining exercise intensity -- 7.2.2. Challenges in modulating intensity -- 7.3. Machine learning in healthcare and rehabilitation -- 7.3.1. Machine learning overview -- 7.3.2. Machine learning (ML) in broader rehabilitation contexts -- 7.4. ML techniques for exercise intensity optimization -- 7.4.1. Supervised learning for exercise intensity prediction -- 7.4.2. Unsupervised learning and clustering for patient stratification -- 7.4.3. Reinforcement learning (RL) for adaptive exercise prescription -- 7.5. Data sources for ML modeling -- 7.5.1. Wearable devices and biosensors -- 7.5.2. Patient-reported outcomes (PROs) -- 7.5.3. Electronic health records (EHRs) -- 7.6. Challenges and limitations -- 7.6.1. Data quality and heterogeneity -- 7.6.2. Model interpretability. -- 7.6.3. Ethical and legal issues -- 7.6.4. Clinical validation -- 7.7. Future directions. 7.7.1. Multimodal data fusion -- 7.7.2. Customizable digital twin -- 7.7.3. Federated learning -- 7.7.4. Integration with genomic data -- 7.8. Conclusion -- 7.9. References -- Chapter 8. AI-Driven Digital Twins for Simulating Physiotherapy Outcomes in Cancer Care -- 8.1. Introduction -- 8.2. Background and theoretical framework -- 8.2.1. Digital twins in healthcare -- 8.2.2. Musculoskeletal and physiotherapy digital twins -- 8.2.3. Virtual physiological human (VPH) and multiscale modeling -- 8.2.4. The role of artificial intelligence -- 8.3. Current research landscape -- 8.3.1. Bibliometric and thematic mapping -- 8.3.2. Oncology-focused DT research -- 8.3.3. Technology prototypes in rehabilitation -- 8.4. Framework for AI-driven DT in cancer physiotherapy -- 8.4.1. Data modalities and integration -- 8.4.2. Modeling and simulation architecture -- 8.4.3. Simulation of physiotherapy interventions -- 8.5. Use cases and scenario examples -- 8.5.1. Post-mastectomy shoulder rehabilitation -- 8.5.2. Cancer-related fatigue and gait training -- 8.5.3. Movement optimization and pain management -- 8.5.4. Telerehabilitation scaling -- 8.6. Evidence of effectiveness -- 8.6.1. Outcome results -- 8.6.2. Oncology DT impact -- 8.6.3. Analogous systems in MSK physiotherapy -- 8.7. Ethical, practical and regulatory challenges -- 8.7.1. Data privacy and sharing -- 8.7.2. Model explainability and clinician trust -- 8.7.3. Equity and accessibility -- 8.7.4. The computational and resource challenges represent -- 8.7.5. Legal liability -- 8.8. Future directions and research agenda -- 8.8.1. Clinical trials -- 8.8.2. Multi-mode data integration -- 8.8.3. Advanced AI methods -- 8.8.4. AR/VR and gamification -- 8.8.5. Regulatory frameworks -- 8.8.6. Interdisciplinary teamwork -- 8.9. Conclusion -- 8.10. References. Chapter 9. Natural Language Processing of Patient Feedback to Personalize Oncology Physiotherapy. |
| Record Nr. | UNINA-9911073921303321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI-Driven Innovations in Physiotherapy and Oncology, Volume 1
| AI-Driven Innovations in Physiotherapy and Oncology, Volume 1 |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Wiley, 2025 |
| Descrizione fisica | 1 online resource (319 pages) |
| Disciplina | 616.9940028563 |
| Collana | ISTE Invoiced Series |
| Soggetto topico |
COMPUTERS / Artificial Intelligence / General
TECHNOLOGY & ENGINEERING / Biomedical |
| ISBN |
1-394-42351-9
1-394-42352-7 1-394-42350-0 9781394423507 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Transforming Post-Operative Rehabilitation: AI-integrated Physiotherapy in Orthopedics and Oncology 2 AI-enhanced Virtual Rehabilitation: The Future of Cancer Recovery 3 Predictive Analytics in Physiotherapy and Oncology: Optimizing Treatment Plans 4 Integrating AI with Physiotherapy to Improve Quality of Life for Cancer Patients 5 AI-driven Imaging in Oncology and Physiotherapy: A Game-changer in Diagnostics 6 AI-driven Innovations in Biomaterial-based Physiotherapy and Oncology: Personalizing Patient Care Through Intelligent Monitoring and Predictive Rehabilitation 7 Telemedicine and AI in Physiotherapy and Oncology: Bridging Gaps in Care 8 Personalized AI-based Exercise Therapy for Oncology and Physiotherapy Patients 9 Neural Networks in Physiotherapy and Oncology: Enhancing Recovery Pathways 10 AI-enabled Chatbots and Virtual Assistants in Oncology and Physiotherapy 11 AI-driven Sensor Technologies in Physiotherapy and Oncology: Transforming Rehabilitation Through Intelligent Biomechanical Monitoring 12 Big Data and AI in Physiotherapy and Cancer Treatment: From Research to Practice 13 Emerging AI Technologies Shaping the Future of Cancer Care 14 AI-driven Motion Analysis for Physiotherapy in Cancer Survivors 15 AI-driven Innovations in Physiotherapy and Oncology: Advancing Postural Assessment, Rehabilitation and Patient-centered Care 16 Key Technologies of Artificial Intelligence: Robotics, Wearables and Big Data 17 Enhancing Health Data Security and Intelligence with Blockchain and Artificial Intelligence List of Authors Summary of Volume 2 Other titles from iSTE in Computer Engineering End User License Agreement |
| Record Nr. | UNINA-9911042411703321 |
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Kumar Abhishek
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| Wiley, 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI-Driven Innovations in Physiotherapy and Oncology, Volume 2
| AI-Driven Innovations in Physiotherapy and Oncology, Volume 2 |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2026 |
| Descrizione fisica | 1 online resource (442 pages) |
| Disciplina | 610.28563 |
| Collana | ISTE Invoiced Series |
| Soggetto topico |
COMPUTERS / Artificial Intelligence / General
TECHNOLOGY & ENGINEERING / Biomedical |
| ISBN |
1-394-42355-1
1-394-42353-5 9781394423538 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911040927703321 |
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Kumar Abhishek
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| Newark : , : John Wiley & Sons, Incorporated, , 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set
| Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Wiley-Blackwell, 2026 |
| Descrizione fisica | 1 online resource (846 pages) |
| Disciplina | 610.285 |
| Soggetto topico |
Artificial intelligence - Medical applications
Medical ethics |
| ISBN |
1-394-38912-4
1-394-38911-6 1-394-38913-2 9781394389124 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Volume One -- Series Page -- Title Page -- Copyright Page -- Contents -- Brief Contents of Volume 2 -- Preface -- Chapter 1 Ethical Frameworks for AI-Driven Healthcare: Genetic and Epidemiological Perspectives on Ethical AI Frameworks -- 1.1 Introduction -- 1.1.1 The Role of AI in Healthcare -- 1.1.2 Importance of Ethical Frameworks -- 1.2 Ethical Considerations in AI-Driven Healthcare -- 1.2.1 Medical Ethics -- 1.2.2 Challenges in Applying Ethical Principles to AI -- 1.3 Genetic Perspectives on Ethical AI Frameworks -- 1.3.1 Personalized Medicine and AI -- 1.3.2 Data Protection of Genetics Information -- 1.3.3 Informed Consent and Genetic Testing -- 1.3.4 Equity in Access to Genetic Treatments -- 1.4 Ethical Frameworks for AI in Genetics -- 1.4.1 Principles of Autonomy, Beneficence, and Justice -- 1.4.2 Ensuring Patient Control Over Genetic Data -- 1.4.3 Utilizing AI-Derived Knowledge for the Patient's Treatment -- 1.4.4 Fair Access to Available Services for Genetic Examinations and Interventions -- 1.5 Epidemiological Perspectives on Ethical AI Frameworks -- 1.5.1 AI and Disease Surveillance -- 1.5.2 Public Health Interventions and AI -- 1.5.3 Information Confidentiality Applying to Consent in Epidemiology Designs -- 1.5.4 Bias in Algorithmic Decision-Making -- 1.6 Ethical Frameworks for AI in Epidemiology -- 1.6.1 Principles of Transparency, Accountability, and Fairness -- 1.6.2 Responsible Use of AI-Powered Insights -- 1.6.3 Fair Division of AI Advantages Across Social Groups -- 1.7 Conclusion -- References -- Chapter 2 Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment -- 2.1 Introduction -- 2.1.1 Summary of AI in Medicine -- 2.1.2 Importance of Ethical Considerations in AI Deployment -- 2.2 Ethical Principles in AI Deployment.
2.2.1 Principles of Beneficence, Nonmaleficence, Autonomy, and Justice -- 2.2.2 Explainability and Transparency in AI Algorithms -- 2.2.3 The Importance of Accountability and Responsibility in AI Decision-Making -- 2.3 Challenges in AI Deployment in Urology and Gastroenterology -- 2.3.1 Data Privacy and Security Concerns -- 2.3.2 Bias and Fairness in AI Algorithms -- 2.3.3 Clinical Integration and Acceptance of AI Technologies -- 2.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology -- 2.4.1 Data Governance and Management -- 2.4.2 Safeguarding Patient Consent and Sensitive Information -- 2.4.3 Addressing Bias within AI Algorithms -- 2.4.4 Clinical Validation and Evaluation of AI Technologies -- 2.5 Case Studies -- 2.5.1 Application of AI Technology in Urology with Regard to Chronic Prostate Cancer -- 2.5.2 The Role of AI in Gastroenterology, with Relation to Diagnosing Other Digestive Tract Ailments -- 2.6 Future Directions and Recommendations -- 2.6.1 Progress of AI Ethics and Regulation -- 2.6.2 Collaboration Between Stakeholders for Ethical AI Deployment -- 2.6.3 Continuous Monitoring and Evaluation of AI Technologies -- 2.7 Conclusion -- References -- Chapter 3 Bias Mitigation and Fairness in AI Healthcare Applications: Addressing Bias and Equity in AI-Driven Healthcare Solutions -- 3.1 Introduction -- 3.1.1 AI in Healthcare -- 3.1.2 Bias Mitigation and Fairness -- 3.2 Bias in AI Healthcare Applications -- 3.2.1 Sources of Bias in AI Algorithms -- 3.2.2 Impact of Bias on Healthcare Equity -- 3.3 Strategies for Bias Mitigation in AI Healthcare -- 3.3.1 Diverse and Representative Training Data -- 3.3.2 Designing Algorithms in a Clear Manner -- 3.3.3 Auditing and Measuring for Fairness -- 3.4 Promoting Equity in AI Healthcare -- 3.4.1 Accessibility of AI Technologies -- 3.4.2 Addressing Unique Needs of Marginalized Communities. 3.4.3 Designing for Inclusivity -- 3.5 Case Studies and Examples -- 3.5.1 Real-World Examples of Bias in Healthcare AI -- 3.5.2 Effective Approaches for Equity Promotion and Bias Mitigation -- 3.6 Future Directions and Challenges -- 3.6.1 Emerging Trends in Bias Mitigation -- 3.6.2 Ethical and Legal Considerations -- 3.7 Conclusion -- References -- Chapter 4 Regulatory Compliance and Data Governance in AI-Driven Healthcare: Legal and Regulatory Considerations for AI-Driven Healthcare Solutions -- 4.1 Introduction -- 4.1.1 An Overview of Healthcare Options Based on AI -- 4.1.1.1 Benefits of AI-Driven Healthcare Solutions -- 4.1.1.2 AI-Driven Solutions in Healthcare- Implementation Challenges -- 4.1.2 Importance of Compliance with Regulations and Governance of Data -- 4.2 Legal and Regulatory Frameworks -- 4.2.1 Health Insurance Portability and Accountability Act -- 4.2.2 General Data Protection Regulation -- 4.2.3 National Laws and Guidelines -- 4.3 Data Governance in AI-Driven Healthcare -- 4.3.1 Establishing Data Governance Frameworks -- 4.3.2 Ensuring Data Quality, Security, and Privacy -- 4.3.3 Ethical Considerations and Best Practices -- 4.4 Regulatory Compliance Challenges -- 4.4.1 Sensitive Patient Data -- 4.4.1.1 Challenges of Regulatory Compliance -- 4.4.1.2 Techniques for Resolving Compliance Issues-Regulations -- 4.4.2 Owning Data: The Legal Maze -- 4.4.3 Assigning Responsibility for AI Deficiencies and Defaults -- 4.5 Ethical Guidelines for AI in Healthcare -- 4.5.1 European Commission's Ethical Guidelines for Trustworthy AI -- 4.5.2 Ethical Considerations in AI Algorithm Design and Deployment -- 4.6 Case Studies -- 4.6.1 Successful Implementation of Data Governance Frameworks -- 4.6.2 Challenges Faced and Lessons Learned -- 4.7 Future Trends and Considerations -- 4.7.1 Emerging Regulatory Trends in AI-Driven Healthcare. 4.7.2 Possible Effects of New Technologies on Fulfilling Regulatory Obligations -- 4.8 Conclusion -- References -- Chapter 5 Ensuring Responsible Data Use in Healthcare AI Applications: Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.1 Introduction -- 5.1.1 Overview of Healthcare AI Applications -- 5.1.2 Significance on Responsible Use of Data -- 5.2 Responsible Data Use in Radiological AI Applications -- 5.2.1 Role of AI in Radiological Imaging -- 5.2.2 Data Privacy and Anonymization -- 5.2.3 Consent Management for AI Data Usage -- 5.2.4 Strategies for Addressing Bias -- 5.2.5 Transparency and Monitoring in AI Algorithms -- 5.3 Responsible Data Use in Surgical AI Applications -- 5.3.1 Utilizing AI in the Preoperative Planning and Surgical Decision-Making Process -- 5.3.2 Data Security and Patient Privacy in Surgical AI -- 5.3.3 Data Security: Encryption and Protection Measures -- 5.3.4 Methods of Ensuring Secure Transmission of Data -- 5.3.5 Use of AI in Interpretable Algorithms for Surgery -- 5.4 Multidisciplinary Approaches to Responsible AI Data Usage -- 5.4.1 Collaboration Between Radiologists, Surgeons, and Data Scientists -- 5.4.2 Ethical Considerations in AI Development -- 5.4.3 Compliance with Regulatory Frameworks -- 5.5 Case Studies and Best Practices -- 5.5.1 Successful Implementations of Responsible Data Use in Healthcare AI -- 5.5.2 Recap of the Case Real-Life Use Studies -- 5.6 Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.6.1 Radiological Approaches -- 5.6.2 Surgical Approaches -- 5.6.3 Collaboration and Compliance -- 5.7 Conclusion -- References -- Chapter 6 Implementing Secure Health Data Exchange with Blockchain: Orthopedic and Ophthalmological Insights into Secure Health Data Exchange -- 6.1 Introduction -- 6.1.1 Overview of Health Data Exchange. 6.1.2 Importance of Security in Health Data Exchange -- 6.1.3 Role of Blockchain Technology in Secure Health Data Exchange -- 6.2 Orthopedic Insights into Secure Health Data Exchange -- 6.2.1 Orthopedic Data Exchange Obstacles -- 6.2.2 Implementing Blockchain in the Sharing of Orthopedic Data -- 6.2.3 Case Studies and Stories of Success -- 6.3 Ophthalmological Insights into Secure Health Data Exchange -- 6.3.1 Challenges in Sharing Ophthalmological Information -- 6.3.2 Using Blockchain Technology for Data Sharing in Ophthalmology -- 6.3.3 Case Studies and Success Stories -- 6.4 Blockchain Technology for Health Data Exchange -- 6.4.1 Understanding Blockchain Technology -- 6.4.2 Advantages and Disadvantages of Blockchain Technology with Respect to Heath Data Exchange -- 6.4.2.1 Advantages of Applying Blockchain Technology in Health Data Exchange -- 6.4.2.2 Limitations of Blockchain in Health Data Exchange -- 6.5 Regulatory and Legal Considerations -- 6.5.1 HIPAA Compliance and Health Data Security -- 6.5.2 GDPR and Protection of Sensitive Health Information -- 6.5.3 Legal Implications of Blockchain in Health Data Exchange -- 6.6 Future Trends and Challenges -- 6.6.1 New Developments in Technological Health Data Exchange -- 6.6.2 Challenges and Opportunities in Implementing Blockchain -- 6.6.3 Future Directions for Secure Health Data Exchange -- 6.7 Conclusion -- References -- Chapter 7 Securing Clinical Trial Data with Decentralized Technologies and Exploring Blockchain Applications in Modern Healthcare Management -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Overview of Blockchain Technology -- 7.4 Methodology -- 7.5 Blockchain Applications in Clinical Trial Data Management -- 7.6 Decentralized Technologies in Healthcare Management -- 7.7 Results and Discussion -- 7.8 Conclusion -- References. Chapter 8 Blockchain-Enabled Healthcare Ecosystems: Scalability, Security, and Interoperability. |
| Record Nr. | UNINA-9911054510903321 |
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Kumar Abhishek
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| Wiley-Blackwell, 2026 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Beginning PBR Texturing : Learn Physically Based Rendering with Allegorithmic’s Substance Painter / / by Abhishek Kumar
| Beginning PBR Texturing : Learn Physically Based Rendering with Allegorithmic’s Substance Painter / / by Abhishek Kumar |
| Autore | Kumar Abhishek |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (270 pages) : illustrations |
| Disciplina | 794.815 |
| Soggetto topico |
Video games - Programming
Computer graphics Game Development Computer Graphics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: What Is Our Goal in This Book? -- Chapter 2: Graphics in the Game Industry -- Chapter 3: The Workflow of Texturing -- Chapter 4: Texturing Games vs Texturing Movies -- Chapter 5: PBR Texturing vs Traditional Texturing -- Chapter 6: Substance Suite and Substance Painter -- Chapter 7: Hardware Specifications for Your Computer -- Chapter 8: Painter's Graphical User Interface -- Chapter 9: Viewport Navigation in Painter -- Chapter 10: Project Setup: Importing a 3D Model into Painter -- Chapter 11: Baking and the Importance of Mesh Maps -- Chapter 12: Working with Materials, Layers, and Masks -- Chapter 13: Working with Procedural Maps -- Chapter 14: Substance Anchors -- Chapter 15: Rendering with Iray -- Chapter 16: Integration with Marmoset, Maya, and Blender -- Chapter 17: Rendering a Portfolio -- Chapter 18: Integration with Unreal Engine (UE4) -- Chapter 19: Tips and Tricks of Substance Painter. |
| Record Nr. | UNINA-9910409996503321 |
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Kumar Abhishek
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Beginning VFX with Autodesk Maya : Create Industry-Standard Visual Effects from Scratch
| Beginning VFX with Autodesk Maya : Create Industry-Standard Visual Effects from Scratch |
| Autore | Kumar Abhishek |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (389 pages) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781484278574
9781484278567 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910513582603321 |
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Kumar Abhishek
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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