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.
Internet of Medicine for Smart Healthcare
| Internet of Medicine for Smart Healthcare |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (567 pages) |
| Disciplina | 610.285/63 |
| Altri autori (Persone) |
VyasNarayan
Singh RathorePramod AnandAbhineet DixitPooja |
| Soggetto topico | Artificial intelligence - Medical applications |
| ISBN |
9781394272242
1394272243 9781394272266 139427226X 9781394272259 1394272251 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911019110303321 |
Kumar Abhishek
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multimodal Data Fusion for Bioinformatics Artificial Intelligence
| Multimodal Data Fusion for Bioinformatics Artificial Intelligence |
| Autore | Lilhore Umesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (406 pages) |
| Disciplina | 570.285 |
| Altri autori (Persone) |
KumarAbhishek
VyasNarayan SimaiyaSarita DuttVishal |
| Soggetto topico |
Bioinformatics
Artificial intelligence - Biological applications |
| ISBN |
9781394269969
139426996X 9781394269945 1394269943 9781394269952 1394269951 |
| 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 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI -- 1.1 Introduction -- 1.2 Literature Review -- 1.3 Results and Discussion -- Conclusion -- References -- Chapter 2 Automated Machine Learning in Bioinformatics -- 2.1 Introduction -- 2.2 Need of Automated Machine Learning -- 2.3 Automated ML in Various Areas of Bioinformatics -- 2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics -- 2.5 Applications of Automated ML in Various Areas of Bioinformatics -- 2.6 Case Study 1 -- 2.7 Conclusion and Future Directions -- References -- Chapter 3 Data-Driven Discoveries: Unveiling Insights with Automated Methods -- 3.1 Introduction -- 3.2 Important Functions in Bioinformatics Include Data Mining and Analysis -- 3.3 Deep Learning in Bioinformatics -- 3.4 Challenges and Issues -- 3.4.1 Data Requirements for Big Data Sets -- 3.4.2 Model Selection and Learning Strategy -- 3.5 Conclusion -- References -- Chapter 4 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease -- 4.1 Introduction -- 4.2 Symptoms and Dataset for PD -- 4.3 Parkinson's Disease Classification Using Machine Learning Methods -- 4.4 Parkinson's Disease Classification Using DL Methods -- 4.5 Conclusion -- References -- Chapter 5 Foundations of Multimodal Data Fusion -- Introduction -- What is Multimodal Data Fusion in Bioinformatics AI? -- Types of Data Modalities in Bioinformatics -- Challenges and Considerations in Multimodal Data Fusion -- Foundational Principles of Data Fusion -- Machine Learning and Deep Learning Techniques for Multimodal Data Fusion -- Feature Representation and Fusion -- Applications in Bioinformatics AI -- Evaluation Metrics and Validation Strategies -- Evaluation Metrics.
Approval Techniques -- Ethical and Legal Considerations -- Future Directions and Challenges -- Conclusion -- References -- Chapter 6 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI -- 6.1 Introduction -- 6.2 Internet of Things (IoT) in Healthcare -- 6.3 Blockchain Technology in Healthcare -- 6.4 Quantum Machine Learning in Healthcare -- 6.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare -- 6.6 Ethical and Regulatory Considerations in Healthcare Technology -- 6.7 Challenges and Future Directions in Healthcare Technology Integration -- 6.8 Results and Discussion -- 6.9 Conclusion -- References -- Chapter 7 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review -- 7.1 Introduction -- 7.2 Multimodal Biomedical Analysis -- 7.3 Challenges in Data Fusion -- 7.4 Deep Learning Methods for Data Fusion -- 7.5 Case Studies and Applications -- 7.5.1 Neuro-Imaging and Genetic Data Fusion -- 7.5.2 Multi-Omics Data Fusion for Cancer Classification -- 7.5.3 Clinical and Wearable Sensor Data Fusion -- 7.6 Future Directions -- 7.7 Conclusion -- References -- Chapter 8 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics -- 8.1 Introduction -- 8.2 Background and Motivation -- 8.3 Machine Learning Basics -- 8.4 Approaches for Data Integration -- 8.5 Machine Learning Techniques for Imaging and Molecular Data -- 8.6 Applications -- 8.7 Challenges and Future Directions -- 8.8 Case Studies -- 8.9 Conclusion -- References -- Chapter 9 Time Series Analysis in Functional Genomics -- 9.1 Introduction -- 9.2 Foundations of Time Series Analysis in Functional Genomics -- 9.2.1 Definition and Concept -- 9.2.1.1 Time Series Data in Genomics -- 9.2.1.2 Key Terminology. 9.2.2 Challenges in Analyzing Functional Genomic Time Series Data -- 9.2.2.1 Noise and Variability -- 9.2.2.2 Data Preprocessing Considerations -- 9.3 Methodologies for Time Series Analysis -- 9.3.1 Overview of Existing Approaches -- 9.3.1.1 Classical Methods -- 9.3.1.2 Advanced Computational Techniques -- 9.3.2 Case Studies -- 9.3.2.1 Successful Applications -- 9.4 Applications of Time Series Analysis in Functional Genomics -- 9.4.1 Gene Expression Profiling -- 9.4.1.1 Identification of Temporal Patterns -- 9.4.1.2 Regulatory Network Inference -- 9.4.2 Functional Annotation -- 9.4.2.1 Enrichment Analysis -- 9.4.2.2 Pathway Analysis -- 9.4.3 Comparative Analysis -- 9.4.3.1 Contrasting Time Series Data Across Genomic Entities -- 9.5 Integration with Multimodal Data -- 9.5.1 Overview of Multimodal Data Fusion -- 9.5.2 Challenges and Opportunities in Integrating Time Series Data -- 9.5.2.1 Challenges in Integrating Time Series Data -- 9.5.2.2 Opportunities in Integrating Time Series Data -- 9.5.3 Case Studies on Successful Integration -- 9.5.3.1 Unveiling Temporal Interactions Across Multiple Modalities -- 9.5.3.2 Temporal Biomarkers in Disease Progression -- 9.6 Conclusion -- References -- Chapter 10 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Machine and Deep Learning Methods with Multimodal -- 10.2.2 Evaluation of Multimodal -- 10.3 Multimodal and Data Fusion -- 10.4 Applications, Opportunities, and Challenges -- 10.4.1 Audio-Visual Multimodality -- 10.4.2 Human-Machine Interaction (HML) -- 10.4.3 Understanding Brain Functionality -- 10.4.4 Medical Diagnosis -- 10.4.5 Smart Patient Monitoring -- 10.4.6 Remote Sensing and Earth Observations -- 10.4.7 Meteorological Monitoring -- 10.5 Conclusion and Future Directions -- 10.5.1 Conclusion. 10.5.2 Future Directions -- References -- Chapter 11 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques -- 11.1 Introduction -- 11.2 AutoMLDL Methods -- 11.3 Application of AutoMLDL in Bioinformatics -- 11.3.1 Bioinformatics and the Categorization of Cardiovascular Diseases -- 11.3.2 Diagnostics of Coronavirus Disease and Bioinformatics -- 11.3.3 Genomic and Bioinformatic Correlation with Clinical Data and Progress of Disease -- 11.3.4 Bioinformatics in the Study of Drug Resistance -- 11.4 Advanced Algorithm in AutoMLDL for Bioinformatics -- 11.4.1 Optimization with Hybrid Harris Hawks along with Cuckoo Search Applying Chemo Bioinformatics -- 11.4.2 The Integration of Chemoinformatics and Bioinformatics with AI -- 11.5 Security and Privacy Issues in AutoMLDL -- 11.5.1 Security and Privacy -- 11.5.2 Open Issues -- 11.6 Conclusion and Future Works -- References -- Chapter 12 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics -- 12.1 Introduction -- 12.2 Foundational Concepts -- 12.3 Current State of Multimodal Data Fusion in Bioinformatics -- 12.4 Emerging Trends in Data Fusion -- 12.5 Algorithms -- 12.5.1 Deep Learning Architectures for Data Fusion -- 12.5.2 Ensemble Methods for Heterogeneous Data Integration -- 12.5.3 Dimensionality Reduction and Feature Extraction -- 12.5.4 Multi-View Learning Algorithms -- 12.5.5 Federated Learning for Privacy-Preserving Data Fusion -- 12.6 Future Directions -- 12.7 Case Studies and Applications -- 12.8 Challenges and Opportunities -- 12.9 Conclusion -- References -- Chapter 13 Future Trends in Bioinformatics AI Integration -- Introduction -- What Is Multimodal Data Fusion? -- Types of Multimodal Data in Bioinformatics -- Challenges in Multimodal Data Fusion -- Multimodal Data Integration Approaches -- Feature Representation and Selection. Integration of Omics Data -- Clinical Applications -- Imaging Data Fusion -- Biological Network Integration -- Applications in Precision Medicine -- Computational Tools and Resources -- Future Directions and Challenges -- Conclusion -- References -- Chapter 14 Emerging Technologies in IoM: AI, Blockchain and Beyond -- 14.1 Introduction -- 14.1.1 Importance of the Internet of Medicine -- 14.2 Artificial Intelligence (AI) in Healthcare -- 14.2.1 Diagnostic Imaging and Radiology -- 14.2.2 Predictive Analytics and Personalized Medicine -- 14.2.3 Natural Language Processing (NLP) for Clinical Documentation -- 14.2.4 Virtual Health Assistants and Chatbots -- 14.2.5 Drug Discovery and Development -- 14.2.6 Operational Efficiency and Resource Management -- 14.2.7 Remote Patient Monitoring -- 14.2.8 Fraud Detection and Security -- 14.2.9 Ethical Considerations and Bias Mitigation -- 14.2.10 Regulatory Compliance -- 14.3 Blockchain in the Medical Landscape -- 14.3.1 Data Security and Integrity -- 14.3.2 Interoperability -- 14.3.3 Patient Empowerment -- 14.3.4 Supply Chain Management -- 14.3.5 Clinical Trials and Research -- 14.3.6 Smart Contracts -- 14.3.7 Identity Management -- 14.3.8 Credentialing and Certification -- 14.3.9 Data Sharing and Consent -- 14.3.10 Cybersecurity -- 14.4 Benefits of Using Technologies in IoM -- 14.4.1 Remote Monitoring and Telemedicine -- 14.4.2 Improved Diagnostics and Treatment -- 14.4.3 Genomic Medicine and Data Analytics -- 14.4.4 Automation and Robotics -- 14.4.5 Wearables and IoT Devices -- 14.4.6 Virtual Reality (VR) and Augmented Reality (AR) -- 14.4.7 Telehealth and Mobile Health (mHealth) -- 14.4.8 Blockchain for Healthcare Management -- 14.4.9 Data Analytics and AI in Research -- 14.4.10 Blockchain and Encryption -- 14.5 Integration of Cutting-Edge Technologies. 14.6 Beyond AI and Blockchain: Exploring Additional Technologies. |
| Record Nr. | UNINA-9911019835703321 |
|
Lilhore Umesh Kumar
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Secure Energy Optimization : Leveraging Internet of Things and Artificial Intelligence for Enhanced Efficiency
| Secure Energy Optimization : Leveraging Internet of Things and Artificial Intelligence for Enhanced Efficiency |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (497 pages) |
| Disciplina | 621.042 |
| Altri autori (Persone) |
KhanSurbhi Bhatia
VyasNarayan DuttVishal BasheerShakila |
| Soggetto topico | Energy conservation - Technological innovations |
| ISBN |
1-394-27184-0
1-394-27183-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911021979203321 |
|
Kumar Abhishek
|
||
| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||