Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
7th International Conference on Nanotechnologies and Biomedical Engineering : Proceedings of ICNBME-2025, October 7-10, 2025, Chisinau, Moldova -Volume 2: Biomedical Engineering and New Technologies for Diagnosis, Treatment, and Rehabilitation
| 7th International Conference on Nanotechnologies and Biomedical Engineering : Proceedings of ICNBME-2025, October 7-10, 2025, Chisinau, Moldova -Volume 2: Biomedical Engineering and New Technologies for Diagnosis, Treatment, and Rehabilitation |
| Autore | Sontea Victor |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2025 |
| Descrizione fisica | 1 online resource (1006 pages) |
| Disciplina | 620.5 |
| Altri autori (Persone) |
TiginyanuIon
RaileanSerghei |
| Collana | IFMBE Proceedings Series |
| Soggetto topico |
COMPUTERS / Image Processing
SCIENCE / Bioinformatics TECHNOLOGY & ENGINEERING / Biomedical |
| ISBN | 3-032-06497-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911031664803321 |
Sontea Victor
|
||
| Cham : , : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Design and Forecasting Models for Disease Management
| Design and Forecasting Models for Disease Management |
| Autore | Dutta Pijush |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (325 pages) |
| Disciplina | 616.00285631 |
| Altri autori (Persone) |
MandalSudip
CengizKorhan SadhuArindam JanaGour Gopal |
| Soggetto topico | SCIENCE / Bioinformatics |
| ISBN |
9781394234059
1394234058 9781394234073 1394234074 9781394234066 1394234066 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Part 1: Safety and Regulatory Aspects for Disease Pre-Screening -- Chapter 1 A Study of Possible AI Aversion in Healthcare Consumers -- 1.1 Introduction to AI in Healthcare -- 1.1.1 The Role of AI in Transforming Healthcare -- 1.1.2 The Unfolding Paradigm: Potential Benefits and Challenges of AI Implementation in Healthcare -- 1.1.3 Overview of Consumer Receptivity Towards AI in Medicine: A Comparative Analysis -- 1.2 Consumer Reluctance to Utilize AI in Healthcare: Present Scenario -- 1.2.1 Top Factors Influencing Consumer Resistance to Medical AI -- 1.2.2 Uncovering the Psychological Barriers and Concerns Associated with AI Adoption in Healthcare -- 1.2.3 Case Studies and Research Findings on Consumer Aversion to AI-Based Healthcare Services -- 1.2.4 Impact on Consumer Decision-Making -- 1.2.5 Effects of AI Aversion on Consumer Decision-Making Processes: An Analysis -- 1.2.6 Understanding How Consumer Perceptions Influence Their Choice Between Human and AI Healthcare Providers -- 1.2.7 Exploring Role of Trust, Perceived Competence and Empathy in Consumer Preferences -- 1.3 Economic Implications of AI Aversion -- 1.3.1 Investigating Influence of AI Aversion on Consumer Willingness to Pay for Healthcare Services -- 1.3.2 Influence of Patient Education on AI Aversion in Healthcare -- 1.3.3 Influence of Patient Awareness on AI Aversion in Healthcare -- 1.3.4 Influence of Age of Patient on AI Aversion in Healthcare -- 1.4 Overcoming Resistance to Medical AI -- 1.4.1 Strategies for Enhancing Consumer Trust and Acceptance of AI in Healthcare -- 1.4.2 Approaches to Alleviate Consumer Concerns and Misconceptions: Communication and Education -- 1.4.3 Cases of Successful Implementation of AI Technologies in Healthcare and Lessons Learned.
1.5 Ethical Considerations and Governance -- 1.5.1 Regulatory Frameworks for Ethical AI Operations to Fight Aversion in Healthcare Consumers -- 1.5.2 Addressing the Potential Cost-Effectiveness and Affordability Concerns Associated with AI-Based Healthcare Solutions -- 1.5.3 Balancing Privacy, Data Protection and Need for Transparency in AI Healthcare Applications -- 1.6 Future Outlook and Opportunities -- 1.6.1 The Future of AI in Healthcare and Its Impact on Consumer Aversion -- 1.6.2 Exploring Emerging Technologies and Trends That May Alleviate Consumer Concerns -- 1.6.3 Opportunities for Collaboration Between AI Developers, Healthcare Providers, and Consumers -- 1.6.4 Summary of Key Findings on Consumer Aversion to AI in Healthcare -- 1.6.5 Implications for Healthcare Practitioners, Policymakers and Researchers -- 1.7 Conclusion -- References -- Chapter 2 A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector -- 2.1 Introduction -- 2.1.1 Understanding the Role of AI in Healthcare -- 2.1.2 Advantages of AI in Healthcare -- 2.1.3 Moral Dilemmas and AI-Based Healthcare -- 2.2 What is Integrated and Systematic Moral Cognitive Therapy (ISMCT)? -- 2.2.1 Integrating Moral Cognitive Therapy with AI -- 2.2.2 Alignment of Moral Cognitive Therapy Principles with AI Applications -- 2.2.3 Benefits of Integrated and Systematic Moral Cognitive Therapy -- 2.2.4 Applications of AI-Integrated Moral Cognitive Therapy in Healthcare -- 2.3 The Role of AI in Healthcare: A Fine Balance Between Ethics and Innovation -- 2.3.1 Humanizing Healthcare: Towards an AI-ISMCT -- 2.3.2 Synergized AI and ISMCT -- 2.3.3 Case Study and Success Stories -- 2.4 Advancing Research in AI-Integrated Moral Cognitive Therapy -- 2.4.1 Collaborative Efforts Between Healthcare Professionals and AI Developers. 2.4.2 Implications for Policy and Regulatory Frameworks -- 2.5 Conclusion -- References -- Chapter 3 A Strategic Model to Control Non-Communicable Diseases -- 3.1 Introduction -- 3.1.1 India and NCDs -- 3.2 Survey of Literature -- 3.2.1 Factors Contributing to the Growth of NCDs -- 3.2.2 Lifestyle Modification - A Strategic Role in Mitigation of NCD -- 3.2.3 Policy to Control NCDs -- 3.3 Proposed Model -- 3.3.1 Registration and Information Centre (RIC) -- 3.3.2 Integration Centre (IIC) -- 3.3.3 Strategic Review Centre (SRC) -- 3.3.4 Expected Outcome of the Proposed Model -- 3.4 Conclusion -- References -- Chapter 4 Image Compression Technique Using Color Filter Array (CFA) for Disease Diagnosis and Treatment -- 4.1 Introduction -- 4.1.1 Color Filter Array -- 4.1.2 Electronic Health Record (EHR) -- 4.2 Related Works -- 4.3 Proposed Model -- 4.4 Implementation -- 4.5 Results -- 4.6 Conclusion -- References -- Chapter 5 Research in Image Processing for Medical Applications Using the Secure Smart Healthcare Technique -- 5.1 Introduction -- 5.1.1 Imaging Systems -- 5.1.2 The Digital Image Processing System -- 5.1.3 Image Enhancement -- 5.2 Classification of Digital Images -- 5.2.1 Utilizations of Digital Image Processing (DIP) -- 5.2.1.1 Medicine -- 5.2.1.2 Forensics -- 5.2.2 Medical Image Analysis -- 5.2.3 Max-Variance Automatic Cut-Off Method -- 5.2.4 Medical Imaging Segmentation -- 5.2.5 Image-Based on Edge Detection -- 5.2.5.1 Robert's Kernel Method -- 5.2.5.2 Prewitt Kernel -- 5.2.5.3 Sobel Kernel -- 5.2.5.4 k-Means Segmentation -- 5.2.6 Images from .-Rays -- 5.2.6.1 Non-Ionizing Radiation -- 5.2.6.2 Magnetic Resonance Imaging -- 5.2.6.3 Segmentation Using Multiple Images Acquired by Different Imaging Techniques -- 5.3 Methods -- 5.3.1 k-Means Approach -- 5.3.2 Bayesian Objective Function. 5.4 Segmentation and Database Extraction with Neural Networks -- 5.4.1 Artificial Neural Network -- 5.4.2 Bayesian Belief Networks -- 5.5 Applications in Medical Image Analysis -- 5.5.1 Using Artificial Neural Network for Better Optimization and Detection in Medical Imaging -- 5.5.1.1 Opportunities -- 5.6 Standardize Analytics Pipeline for the Health Sector -- 5.7 Feature Extraction/Selection -- 5.7.1 Significance of Machine Learning for Medical Image Processing -- 5.7.2 Significance of Deep Learning for Medical Image Processing -- 5.8 Image-Based Forecasting Using Internet of Things (IoT) in Smart Healthcare System -- 5.9 IoT Monitoring Applications Based on Image Processing -- 5.10 Significance of Computer-aided Big Healthcare Data (BHD) for Medical Image Processing -- 5.11 Applications of Big Data -- 5.11.1 Big Data Analytics in Health Sector -- 5.11.2 Computer-Aided Diagnosis in Mammography -- 5.11.3 Tumor Imaging and Treatment -- 5.11.4 Molecular Imaging -- 5.11.5 Surgical Interventions -- 5.12 Conclusion -- References -- Chapter 6 Comparative Study on Image Enhancement Techniques for Biomedical Images -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Theoretical Concepts -- 6.3.1 Logarithmic Transformation -- 6.3.1.1 Advantages of Log Transformation -- 6.3.1.2 Limitations of Log Transformation -- 6.3.2 Power Law Transformation or Gamma Correction -- 6.3.2.1 Advantages of Gamma Correction -- 6.3.2.2 Limitations of Gamma Correction -- 6.3.3 Piecewise Linear Transformation or Contrast Stretching -- 6.3.3.1 Advantages of Contrast Stretching -- 6.3.3.2 Limitations of Contrast Stretching -- 6.3.4 Histogram Equalization -- 6.3.4.1 Advantages of Histogram Equalization -- 6.3.4.2 Limitations of Histogram Equalization -- 6.3.5 Contrast-Limited Adaptive Histogram Equalization (CLAHE) -- 6.3.5.1 Advantages of CLAHE -- 6.3.5.2 Limitation of CLAHE. 6.3.6 Adjustment Function -- 6.4 Results and Discussion -- 6.4.1 Images and Histograms for Different Images Using Different Enhancement Methods -- 6.4.2 Comparison for Different Image Enhancement Techniques -- 6.5 Conclusion -- References -- Chapter 7 Exploring Parkinson's Disease Progression and Patient Variability: Insights from Clinical and Molecular Data Analysis -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Data Review -- 7.3.1 Clinical Data -- 7.3.2 Peptides Data -- 7.3.3 Protein Data -- 7.4 Parkinson's Dynamic for Patients in Train -- 7.5 Conclusion -- References -- Chapter 8 A Survey-Based Comparative Study on Machine Learning Techniques for Early Detection of Mental Illness -- 8.1 Introduction -- 8.2 Background -- 8.3 Review of Previous Works -- 8.3.1 Standard Questionnaire -- 8.3.2 Social Media Content -- 8.4 Comparative Result -- 8.5 Discussion -- 8.6 Conclusion -- References -- Part 2: Clinical Decision Support System for Early Disease Detection and Management -- Chapter 9 Diagnostics and Classification of Alzheimer's Diseases Using Improved Deep Learning Architectures -- 9.1 Introduction -- 9.2 Related Works -- 9.3 Method -- 9.3.1 Data Description -- 9.4 Result Analysis -- 9.4.1 Performance Metrics -- 9.4.2 Experimental Setup -- 9.5 Conclusion -- Data Availability -- References -- Chapter 10 Perform a Comparative Study Based on Conventional Machine Learning Approaches for Human Stress Level Detection -- 10.1 Introduction -- 10.2 Related Work -- 10.3 Architecture Design -- 10.3.1 Body Temperature -- 10.3.2 Humidity Analysis -- 10.3.3 Step Count Analysis -- 10.3.4 Dataset -- 10.4 Experiment -- 10.4.1 Performance Matrices -- 10.5 Result Analysis -- 10.6 Conclusion -- References -- Chapter 11 Diabetes Prediction Using a Hybrid PCA-Based Feature Selection and Computational Machine Learning Algorithm -- 11.1 Introduction. 11.2 Related Work. |
| Record Nr. | UNINA-9911020138203321 |
|
Dutta Pijush
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
