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Biblioteca

MARC Lista (tabellare)
AI-Powered IoT in the Energy Industry : Digital Technology and Sustainable Energy Systems / / edited by S. Vijayalakshmi, Savita ., Balamurugan Balusamy, Rajesh Kumar Dhanaraj
AI-Powered IoT in the Energy Industry : Digital Technology and Sustainable Energy Systems / / edited by S. Vijayalakshmi, Savita ., Balamurugan Balusamy, Rajesh Kumar Dhanaraj
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (318 pages)
Disciplina 004.678
Collana Power Systems
Soggetto topico Electric power distribution
Renewable energy sources
Cooperating objects (Computer systems)
Electric power production
Energy Grids and Networks
Renewable Energy
Cyber-Physical Systems
Electrical Power Engineering
ISBN 9783031150449
9783031150432
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto AI and ML Towards Sustainable Solar Energy -- AI and Intermittency Management of Renewable Energy -- AI Impact on Energy and Utilities -- Energy Intelligence – The Smart Grid Perspective -- IoT Towards Leveraging Renewable Energy -- IoT Contribution in Construct of Green Energy -- IoT, Smart Grids, and Big Data – Renewable Energy Insights -- IoT Infrastructure to Energize Electromobility -- Building Sustainable Charging Infrastructure – Smart Solutions -- Biomass Renewable Energy: Introduction and Application of AI and IoT -- Modernization of Rural Electric Infrastructure -- AI and IoT in Improving Resilience of Smart Energy Infrastructure -- Empowering Renewable Energy Using Internet of Things -- Role of Artificial Intelligence in Renewable Energy -- IoT and Sustainable Energy System: Risk and Opportunity -- Powering the Geothermal Energy with AI, IoT, and ML.
Record Nr. UNINA-9910686478203321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology
Artificial Intelligence for Autonomous Vehicles : The Future of Driverless Technology
Autore Rajendran Sathiyaraj
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (268 pages)
Disciplina 629.2/046028563
Altri autori (Persone) SabharwalMunish
HuYu-Chen
BalusamyBalamurugan
Collana Advances in Data Engineering and Machine Learning Series
Soggetto topico Automated vehicles - Technological innovations
ISBN 9781119847656
1119847656
9781119847649
1119847648
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 Autonomous Vehicles-A Survey of Trends and Challenges -- 1.1 Introduction -- 1.2 Research Trends of AI for AV -- 1.3 AV-Pipeline Activities -- 1.3.1 Vehicle Detection -- 1.3.2 Rear-End Collision Avoidance -- 1.3.3 Traffic Signal and Sign Recognition -- 1.3.4 Lane Detection and Tracking -- 1.3.5 Pedestrian Detection -- 1.4 Datasets in the Literature of Autonomous Vehicles -- 1.4.1 Stereo and 3D Reconstruction -- 1.4.2 Optical Flow -- 1.4.3 Recognition and Segmentation of Objects -- 1.4.4 Tracking Datasets -- 1.4.5 Datasets for Aerial Images -- 1.4.6 Sensor Synchronization Datasets -- 1.5 Current Industry Standards in AV -- 1.6 Challenges and Opportunities in AV -- 1.6.1 Cost -- 1.6.2 Security Concerns -- 1.6.3 Standards and Regulations -- 1.7 Conclusion -- References -- Chapter 2 Age of Computational AI for Autonomous Vehicles -- 2.1 Introduction -- 2.1.1 Autonomous Vehicles -- 2.1.2 AI in Autonomous Vehicles -- 2.1.2.1 Functioning of AI in Autonomous Vehicles -- 2.2 Autonomy -- 2.2.1 Autonomy Phases -- 2.2.2 Learning Methodologies for Incessant Learning in Real-Life Autonomy Systems -- 2.2.2.1 Supervised Learning -- 2.2.2.2 Unsupervised Learning -- 2.2.2.3 Reinforcement Learning -- 2.2.3 Advancements in Intelligent Vehicles -- 2.2.3.1 Integration of Technologies -- 2.2.3.2 Earlier Application of AI in Automated Driving -- 2.3 Classification of Technological Advances in Vehicle Technology -- 2.4 Vehicle Architecture Adaptation -- 2.5 Future Directions of Autonomous Driving -- 2.6 Conclusion -- References -- Chapter 3 State of the Art of Artificial Intelligence Approaches Toward Driverless Technology -- 3.1 Introduction -- 3.2 Role of AI in Driverless Cars -- 3.2.1 What is Artificial Intelligence? -- 3.2.2 What are Autonomous Vehicles?.
3.2.3 History of Artificial Intelligence in Driverless Cars -- 3.2.4 Advancements Over the Years -- 3.2.5 Driverless Cars and the Technology they are Built Upon -- 3.2.6 Advancement of Algorithms -- 3.2.7 Case Study on Tesla -- 3.3 Conclusion -- References -- Chapter 4 A Survey on Architecture of Autonomous Vehicles -- 4.1 Introduction -- 4.1.1 What is Artificial Intelligence? -- 4.1.2 What are Autonomous Vehicles? -- 4.2 A Study on Technologies Used in AV -- 4.2.1 Artificial Vision -- 4.2.2 Varying Light and Visibility Conditions -- 4.2.3 Scenes with a High Dynamic Range (HDR) -- 4.2.3.1 3 Dimensional Technology -- 4.2.3.2 Emerging Vision Technologies -- 4.2.4 Radar -- 4.2.4.1 Emerging Radar Technologies -- 4.2.5 LiDAR -- 4.2.5.1 Emerging LiDAR Technologies -- 4.3 Analysis on the Architecture of Autonomous Vehicles -- 4.3.1 Hardware Architecture -- 4.3.2 Software Architecture -- 4.4 Analysis on One of the Proposed Architectures -- 4.5 Functional Architecture of Autonomous Vehicles -- 4.6 Challenges in Building the Architecture of Autonomous Vehicles -- 4.6.1 Road Condition -- 4.6.2 Weather Condition -- 4.6.3 Traffic Condition -- 4.6.4 Accident Responsibility -- 4.6.5 Radar Interference -- 4.7 Advantages of Autonomous Vehicles -- 4.8 Use Cases for Autonomous Vehicle Technology -- 4.8.1 Five Use Cases -- 4.9 Future Aspects of Autonomous Vehicles -- 4.9.1 Levels of Vehicle Autonomy -- 4.9.2 Safer Mobility Technology -- 4.9.3 Industry Collaboration and Policy Matters -- 4.10 Summary -- References -- Chapter 5 Autonomous Car Driver Assistance System -- 5.1 Introduction -- 5.1.1 Traffic Video Surveillance -- 5.1.2 Need for the Research Work -- 5.2 Related Work -- 5.3 Methodology -- 5.3.1 Intelligent Driver Assistance System -- 5.3.2 Traffic Police Hand Gesture Region Identification -- 5.3.3 Vehicle Brake and Indicator Light Identification.
5.4 Results and Analysis -- 5.5 Conclusion -- References -- Chapter 6 AI-Powered Drones for Healthcare Applications -- 6.1 Introduction -- 6.1.1 Role of Artificial Intelligence in Drone Technology -- 6.1.2 Unmanned Aerial Vehicle-Drone Technology -- 6.2 Kinds of Drones Used by Medical Professionals -- 6.2.1 Multirotor -- 6.2.2 Only One Rotor -- 6.2.3 Permanent-Wing Drones -- 6.2.4 Drones for Passenger Ambulances -- 6.3 Medical and Public Health Surveillance -- 6.3.1 Telemedicine -- 6.3.2 Drones as Medical Transportation Devices -- 6.3.3 Advanced System for First Aid for the Elderly People -- 6.4 Potential Benefits of Drones in the Healthcare Industry -- 6.4.1 Top Medical Drone Delivery Services -- 6.4.2 Limitations of Drones in Healthcare -- 6.4.3 The Influence of COVID on Drones -- 6.4.4 Limitations of Drone Technology in the Healthcare Industry -- 6.4.4.1 Privacy -- 6.4.4.2 Legal Concerns -- 6.4.4.3 Rapid Transit-One of the Biggest Drawbacks of Drones is Time -- 6.4.4.4 Bugs in the Technology -- 6.4.4.5 Dependence on Weather -- 6.4.4.6 Hackable Drone Technology -- 6.5 Conclusion -- References -- Chapter 7 An Approach for Avoiding Collisions with Obstacles in Order to Enable Autonomous Cars to Travel Through Both Static and Moving Environments -- 7.1 Introduction -- 7.1.1 A Brief Overview of Driverless Cars -- 7.1.2 Objectives -- 7.1.3 Possible Uses for a Car Without a Driver -- 7.2 Related Works -- 7.3 Methodology of the Proposed Work -- 7.4 Experimental Results and Analysis -- 7.5 Results and Analysis -- 7.6 Conclusion -- References -- Chapter 8 Drivers' Emotions' Recognition Using Facial Expression from Live Video Clips in Autonomous Vehicles -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Face Detection -- 8.2.2 Facial Emotion Recognition -- 8.3 Proposed Method -- 8.3.1 Dataset -- 8.3.2 Preprocessing -- 8.3.3 Grayscale Equalization.
8.4 Results and Analysis -- 8.5 Conclusions -- References -- Chapter 9 Models for the Driver Assistance System -- 9.1 Introduction -- 9.2 Related Survey -- 9.3 Proposed Methodology -- 9.3.1 Proposed System -- 9.3.2 Data Acquisition -- 9.3.3 Noise Reduction -- 9.3.4 Feature Extraction -- 9.3.5 Histogram of Oriented Gradients -- 9.3.6 Local Binary Pattern -- 9.3.7 Feature Selection -- 9.3.8 Classification -- 9.4 Experimental Study -- 9.4.1 Quantitative Investigation on the NTHU Drowsy Driver Detection Dataset -- 9.5 Conclusion -- References -- Chapter 10 Control of Autonomous Underwater Vehicles -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Control Problem in AUV Control System -- 10.4 Methodology -- 10.5 Results -- References -- Chapter 11 Security and Privacy Issues of AI in Autonomous Vehicles -- 11.1 Introduction -- 11.2 Development of Autonomous Cars with Existing Review -- 11.3 Automation Levels of Autonomous Vehicles -- 11.4 The Architecture of an Autonomous Vehicle -- 11.5 Threat Model -- 11.6 Autonomous Vehicles with AI in IoT-Enabled Environments -- 11.7 Physical Attacks Using AI Against Autonomous Vehicles -- 11.8 AI Cybersecurity Issues for Autonomous Vehicles -- 11.9 Cyberattack Defense Mechanisms -- 11.9.1 Identity-Based Approach -- 11.9.2 Key-Based Solution -- 11.9.3 Trust-Based Solution -- 11.9.4 Solution Based on Behavior Detection -- 11.10 Solution Based on Machine Learning -- 11.11 Conclusion -- References -- Index -- EULA.
Record Nr. UNINA-9910876861403321
Rajendran Sathiyaraj  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial Intelligence in IoT and Cyborgization / / edited by Rajesh Kumar Dhanaraj, Bharat S. Rawal, Sathya Krishnamoorthi, Balamurugan Balusamy
Artificial Intelligence in IoT and Cyborgization / / edited by Rajesh Kumar Dhanaraj, Bharat S. Rawal, Sathya Krishnamoorthi, Balamurugan Balusamy
Autore Dhanaraj Rajesh Kumar
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (191 pages)
Disciplina 629.892
Altri autori (Persone) RawalBharat S
KrishnamoorthiSathya
BalusamyBalamurugan
Collana Studies in Computational Intelligence
Soggetto topico Internet of things
Artificial intelligence
Computational intelligence
Cooperating objects (Computer systems)
Internet of Things
Artificial Intelligence
Computational Intelligence
Cyber-Physical Systems
ISBN 981-9943-03-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to Cyborgization systems -- AI based Smart IoT systems -- Role of Machine Learning and Deep Learning applications in the Internet of Things (IoT) Security -- IOT Based Experimental Relaying System for Smart Grid -- Environment Twin based Deep learning model using Reconfigurable Holographic Surface for user location prediction -- Surveillance of Robotic Boat Using IoT and Image Processing -- Advanced Human – Computer Interaction Technology in Digital Twins -- CNN Architecture and Classification of Miosis and Mydriasis Clinical Conditions -- Role of Object Detection for Brain Tumor Identification Using Magnetic Resonance Image Scans -- Deep Learning Model for Predicting Diabetes Disease Using SVM -- Deep Learning For Targeted Treatment. .
Record Nr. UNINA-9910746959203321
Dhanaraj Rajesh Kumar  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Blockchain Technology in Corporate Governance : Transforming Business and Industries
Blockchain Technology in Corporate Governance : Transforming Business and Industries
Autore Sood Kiran
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (443 pages)
Altri autori (Persone) DhanarajRajesh Kumar
BalusamyBalamurugan
KadrySeifedine
Soggetto genere / forma Electronic books.
ISBN 1-119-86524-7
1-119-86523-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Role of Blockchain Technology in the Modern Era -- 1.1 Introduction -- 1.2 What is Blockchain Technology? -- 1.3 Blockchain Technology in Healthcare 4.0 -- 1.3.1 Area of Blockchain Technology-Based Healthcare 4.0 -- 1.3.2 Smart Blockchain Healthcare 4.0 -- 1.4 Energy Sector -- 1.5 Applications of Blockchain in the Energy Sector -- 1.5.1 Decentralized Storage and Control in Power Grid -- 1.5.2 Electricity Trading Law -- 1.5.3 Electric Vehicles -- 1.5.4 Decrease the Global Carbon Emission -- 1.6 Blockchain-Based Financial Sector -- 1.6.1 Legal Policies of the Financial Sector -- 1.6.2 Credit Risk -- 1.6.3 KYC and Product Personalization -- 1.6.4 Insurance Monetary Management -- 1.6.5 Collaborative Techniques in Financial Services Chain -- 1.7 Bitcoins and Blockchain Sustainability Issues -- 1.8 Conclusion -- References -- Part 1: Blockchain: Opportunities for Healthcare 4.0 -- Chapter 2 BTCG4: Blockchain Technology in Electronic Healthcare Systems -- 2.1 Introduction -- 2.1.1 Healthcare Industry -- 2.1.2 Requirement of Electronic Healthcare Industry -- 2.1.2.1 System Security -- 2.1.2.2 Interoperability -- 2.1.2.3 Sharing of Information -- 2.1.2.4 Mobility -- 2.1.2.5 Mobile Health -- 2.1.2.6 Wireless -- 2.1.2.7 Internet of Things -- 2.2 Overview of Blockchain -- 2.2.1 Distinct Characteristics of the Use of Blockchain in the Electronic Healthcare Industry -- 2.2.1.1 Decentralization of Storage -- 2.2.1.2 Authentication -- 2.2.1.3 Immutability -- 2.2.1.4 Improvement in Security -- 2.2.1.5 Efficiency -- 2.2.1.6 Distributed Ledger -- 2.3 Blockchain Applications -- 2.3.1.1 Smart Contracts -- 2.3.1.2 Spotting and Preventing Fraudulent Activity -- 2.3.1.3 Authentication of the User's Identity -- 2.4 Challenges Associated with Blockchain Technology.
2.4.1 Unavailability of Uniformity -- 2.4.2 Decentralization of Storage and Leakage of Privacy -- 2.4.3 Handling of Critical Information -- 2.4.4 Scalability and Internet of Things Overhead -- 2.4.5 Vulnerabilities Specific to Blockchain Technology -- 2.4.6 General Vulnerabilities in Software -- 2.5 Opportunities of Blockchain in the Healthcare Industry -- 2.5.1 The Gem Health Network Facilitates the Exchange of Healthcare Data -- 2.5.2 MDREC -- 2.5.3 System of Pervasive Social Network -- 2.5.4 Virtual Resources -- 2.5.5 Data Recording for Body-Worn Sensing Devices Based on Context -- 2.5.6 MeDShare -- 2.5.7 Blockchain-Based Clinical and Precise Platform Trials -- 2.5.8 Access to Health-Related Information -- 2.6 Concluding Remarks -- References -- Chapter 3 Blockchain Technology and Healthcare: Towards Combating COVID-19 -- 3.1 Introduction -- 3.1.1 Blockchain Technology in Healthcare -- 3.1.2 Features of Blockchain Technology -- 3.1.3 Applications of Blockchain in Healthcare -- 3.1.4 Data Management -- 3.1.5 Electronic Health Record -- 3.1.6 Claims and Billing Management -- 3.1.7 Pandemic Data Tracking -- 3.1.8 Tracking PPE -- 3.1.9 Vaccine Monitoring -- 3.1.10 Future Vaccination -- 3.1.11 Digital Contact Tracing -- 3.1.12 Prescription Management -- 3.2 Combating COVID-19 -- 3.2.1 Handling Fake Infodemic Using MiPasa Platform -- 3.2.2 VIRI Platform Preventing Spread -- 3.2.3 WIShelter for Data Privacy -- 3.3 Reviving Capabilities -- 3.3.1 Blockchain in Healthcare - Global Scenario -- 3.3.2 Blockchain in Healthcare - Indian Scenario -- 3.4 Challenges in Adopting Blockchain in Healthcare -- 3.5 Conclusion -- References -- Chapter 4 Blockchain-Based Energy-Efficient Heterogeneous Sensor Networks in Healthcare System -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Literature Gap -- 4.2.2 Fuzzy Improved Model (Improved Model Fuzzy).
4.3 Proposed Energy Protocol with Blockchain -- 4.4 Conclusion -- References -- Chapter 5 Development of a Safe Health Framework Using a Temporary Blockchain Technique -- 5.1 Introduction -- 5.2 Related Work -- 5.2.1 Blockchain-Based Healthcare Organization -- 5.2.2 Medrek -- 5.2.3 Stratum -- 5.2.4 Factor -- 5.2.5 Pocketbook -- 5.2.6 Tyrion -- 5.2.7 Roomed -- 5.3 Secure Framework (Sefira) for Healthcare System -- 5.3.1 Progressive Temporal Blockchain -- 5.3.2 Temporal Shadow -- 5.3.2.1 Context-Based Merkle Tree (CBMT) -- 5.3.2.2 Temporal Hash Signature (THS) -- 5.3.2.3 Context-Based Access Control (CBAC) in Smart Contract -- 5.3.2.4 Layered Architecture of SeFra -- 5.4 Conclusion -- References -- Chapter 6 Data Consistency, Transparency, and Privacy in Healthcare Systems Using Blockchain Technology -- 6.1 Introduction -- 6.2 The Cutting Edge in Genomics -- 6.2.1 Next Generation Sequencing (NGS) -- 6.2.2 EDGE Bioinformatics -- 6.2.3 Pharmacogenetics and Personalized Medicine -- 6.2.4 Prenatal Diagnosis -- 6.2.5 Diagnosis of Infectious Diseases -- 6.2.6 Gene Therapy and Genome Editing -- 6.2.7 Genomics with Blockchain Technology -- 6.3 Medical Records -- 6.3.1 Blockchain Architecture - Components and Types -- 6.3.2 Blockchain Benefits in Healthcare Records Maintenance -- 6.3.3 Brief Overview on the Blockchain-Enabled Patient Healthcare Record Management Process -- 6.3.3.1 Data Generation -- 6.3.3.2 Data Cleaning and Enrichment -- 6.3.3.3 Data Capturing -- 6.3.3.4 Data Consumption -- 6.3.3.5 Data Mining -- 6.4 Supply Chain Management -- 6.4.1 Pharmaceutical Applications -- 6.4.2 Medical Devices and Medical Supplies -- 6.4.3 Internet of Healthy Things -- 6.4.4 Public Health -- References -- Part 2: Blockchain in the Energy Sector -- Chapter 7 Application of Blockchain Technology in Sustainable Energy Systems -- 7.1 Introduction -- 7.2 Blockchain.
7.3 Blockchain Applications in Energy Sector -- 7.3.1 Blockchain Applications in Smart Grid -- 7.3.2 Blockchain Applications in Energy Trading -- 7.3.3 Blockchain Applications in Micro-Grid -- 7.3.4 Blockchain in Electric Vehicles -- 7.3.5 Blockchain Applications in Cyber Physical Security -- 7.4 Blockchain as New Substructure -- 7.5 Limitations of Blockchain -- 7.6 Conclusions -- References -- Chapter 8 Revamping Energy Sector with a Trusted Network: Blockchain Technology -- 8.1 Introduction -- 8.2 Energy Digital Transformation -- 8.2.1 Digitalization, Decarbonization, and Decentralization of the Energy Sector -- 8.2.2 Blockchain: A Disruptive Technology of the Energy Value Chain -- 8.2.3 Blockchain Advancing DERs -- 8.3 Energy Trading Mechanisms -- 8.3.1 Blockchain P2P Energy Trading: A New Financing Mechanism -- 8.3.2 Blockchain-Based Virtual Power Plant (VPP) Model -- 8.3.3 Blockchain Technology for Electric Vehicle (EV) Charging and Discharging -- 8.4 Blockchain Unlocking New Demand Side Management Models -- 8.4.1 Blockchain in the Energy Efficiency Market -- 8.4.2 New Blockchain-Enabled Demand Response (DR) Models -- 8.4.3 Blockchain-Based Energy Performance Contracting -- 8.5 Energy Blockchain's Social and Environmental Impacts -- 8.5.1 Blockchain Market for Carbon Credits and RECs -- 8.5.2 Fighting Energy Poverty -- 8.6 Conclusion -- References -- Part 3: The Impact of Blockchain on the Financial Industry -- Chapter 9 Process Innovation and Unification of KYC Document Management System with Blockchain in Banking -- 9.1 Introduction -- 9.2 Blockchain -- 9.3 Blockchain Technology Applications Sectors -- 9.4 Know Your Customer (KYC) -- 9.4.1 KYC Advantages -- 9.4.2 KYC Document List -- 9.4.3 Re-KYC -- 9.4.4 Types of KYC Verification -- 9.4.5 KYC Through Manual Verification Process -- 9.4.6 Typical KYC Verification Process - Issues and Challenges.
9.5 Electronic Know Your Customer (e-KYC) -- 9.5.1 e-KYC Documents Management System Using Blockchain -- 9.6 Blockchain KYC Verification Process Advantages -- 9.7 Taxonomy of Blockchain Systems -- 9.8 Literature Survey -- 9.9 Potential Use-Cases of Blockchain Technology in Banks -- 9.10 Blockchain KYC-AML Solution -- 9.11 Conclusion -- References -- Chapter 10 Applying Blockchain Technology to Address NPA Issues During the COVID-19 Pandemic -- 10.1 Introduction -- 10.2 ACT 1: Foundation of Non-Performing Assets Management and Blockchain Technology -- 10.3 Induction to Non-Performing Assets -- 10.4 Charter for NPA Management -- 10.5 Reasons for Growth of NPAs -- 10.6 Induction to Blockchain Technology -- 10.7 Possible Applications of Blockchain Technology -- 10.8 ACT II Confrontation Stage -- 10.9 Investigation of Loan Quality-Related Issues in the Indian Banking System -- 10.10 Stage 3 - Treatment Stage for Bad Loans Through Blockchain in Indian Banks -- 10.11 The Challenges of the Blockchain Technology in Financial Sector -- 10.12 Conclusion -- References -- Chapter 11 Blockchain and Smart Contracts for Insurance Industry -- 11.1 Introduction -- 11.1.1 Blockchain in Insurance -- 11.1.2 Blockchain in Insurance Applications -- 11.2 Smart Contracts by Insurance Providers Using Blockchain Technologies -- 11.2.1 Blockchain: A Built-In Data -- 11.2.2 Advanced Insurance Automation -- 11.2.3 Cyber Security in Insurance through Blockchain -- 11.3 Review of Literature -- 11.4 Opportunities Provided by Blockchain Technology -- 11.5 How Blockchain Technologies Work in Insurance Companies -- 11.6 Challenges Posed by Blockchain -- 11.6.1 Technologies Leveraging Technologies -- 11.6.2 Strategic Alliances -- 11.6.3 New Product Development -- 11.7 Conclusion -- References -- Chapter 12 How Blockchain Can Transform the Financial Services Industry -- 12.1 Introduction.
12.2 Literature Review.
Record Nr. UNINA-9910623987903321
Sood Kiran  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Blockchain with Artificial Intelligence for Healthcare : A Synergistic Approach
Blockchain with Artificial Intelligence for Healthcare : A Synergistic Approach
Autore Malviya Rishabha
Edizione [1st ed.]
Pubbl/distr/stampa Bristol : , : Institute of Physics Publishing, , 2023
Descrizione fisica 1 online resource (209 pages)
Disciplina 610.285
Altri autori (Persone) SinghArun Kumar
SundramSonali
BalusamyBalamurugan
KadrySeifedine
Collana IOP Ebooks Series
ISBN 0-7503-5841-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910915779203321
Malviya Rishabha  
Bristol : , : Institute of Physics Publishing, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Computational Intelligence for Autonomous Finance
Computational Intelligence for Autonomous Finance
Autore Gupta Deepak
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (337 pages)
Altri autori (Persone) GuptaMukul
DhanarajRajesh Kumar
BalusamyBalamurugan
GuptaParth M
Collana Fintech in a Sustainable Digital Society Series
ISBN 9781394233236
139423323X
9781394233250
1394233256
9781394233243
1394233248
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910908384403321
Gupta Deepak  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Intelligence in Bioprinting : Challenges and Future Directions
Computational Intelligence in Bioprinting : Challenges and Future Directions
Autore Gangadevi E
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (346 pages)
Altri autori (Persone) ShriM. Lawanya
BalusamyBalamurugan
ISBN 1-394-20487-6
1-394-20486-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 The Emergence of Bioprinting and Computational Intelligence -- 1.1 Introduction -- 1.2 Related Study -- 1.3 Understanding the Basics of Bioprinting and Computational Intelligence -- 1.3.1 Bioprinting: The Basics -- 1.3.2 Computational Intelligence: The Basics -- 1.3.3 Applications of Bioprinting and Computational Intelligence -- 1.4 The Role of Computational Intelligence in Bioprinting -- 1.5 Applications of Bioprinting and Computational Intelligence in Medicine -- 1.6 Bioprinting and Computational Intelligence in Tissue Engineering and Regenerative Medicine -- 1.7 Advancements in Bioprinting and Computational Intelligence Technologies -- 1.8 The Ethical and Regulatory Implications of Bioprinting and Computational Intelligence -- 1.9 The Future of Bioprinting and Computational Intelligence: Opportunities and Challenges -- 1.10 Case Studies: Bioprinting and Computational Intelligence in Action -- 1.10.1 Trends in Computational Intelligence and Bioprinting -- 1.10.2 Challenges in Computational Intelligence and Bioprinting -- 1.11 Conclusion -- References -- Chapter 2 Design, Architecture, Implementation, and Evaluation of Bioprinting Technology for Tissue Engineering -- 2.1 Introduction -- 2.2 3D Bioprinting -- 2.3 Material Characteristics -- 2.3.1 Printability -- 2.4 Mechanical Properties -- 2.5 Biomaterials -- 2.6 Design, Architecture of 3D Bioprinting -- 2.6.1 Inkjet Bioprinting -- 2.6.2 Laser-Assisted Bioprinting (LAB) -- 2.6.3 Extrusion Bioprinting -- 2.7 3D Bioprinting Tissue Models -- 2.8 3D Multimaterial Bioprinting-Development of Complex Architectures -- 2.9 Implementation and Evaluation -- 2.10 Bone -- 2.11 Cartilage -- 2.12 Soft Tissue Engineering -- 2.13 Vascular Tissue -- 2.14 Skin -- 2.15 Biocompatibility and Control of Degradation and Byproducts.
2.16 Conclusion -- References -- Chapter 3 Design and Development of IoT Devices: Methods, Tools and Technologies -- 3.1 Introduction to IoT Devices and 3D Bioprinting -- 3.2 Methodology for Designing IoT Devices for 3D Bioprinting -- 3.3 Additional Considerations in IoT Device Design for 3D Bioprinting -- 3.4 Tools for Developing IoT Devices for 3D Bioprinting -- 3.4.1 Microcontrollers and Development Boards -- 3.4.2 Sensors and Actuators -- 3.4.3 Communication Protocols -- 3.4.4 Software Development Kits -- 3.4.5 Cloud Platforms -- 3.4.6 3D Printing Software -- 3.4.7 CAD Software -- 3.4.8 Simulation Software -- 3.4.9 Data Analytics Tools -- 3.4.10 Cybersecurity Tools -- 3.5 Techniques for Developing IoT Devices for 3D Bioprinting -- 3.5.1 Agile Development -- 3.5.2 Rapid Prototyping -- 3.5.3 Test-Driven Development -- 3.5.4 Continuous Integration -- 3.5.5 Modular Design -- 3.5.6 Power Optimization -- 3.5.7 Data Processing Techniques -- 3.5.8 Quality Assurance -- 3.5.9 Cybersecurity Techniques -- 3.5.10 Standardization -- 3.6 Case Studies of IoT Devices for 3D Bioprinting -- 3.7 Future Directions in IoT Devices for 3D Bioprinting -- 3.8 Conclusion -- References -- Chapter 4 AI-Based AR/VR Models in Biomedical Sustainable Industry 4.0 -- 4.1 Introduction -- 4.2 Mixed Augmented Reality -- 4.2.1 SDK in Augmented Reality -- 4.2.2 Application Scope of Augmented Reality -- 4.2.2.1 Video Capabilities -- 4.2.2.2 AR Toolkit Technology -- 4.2.2.3 Quality of Tracking System -- 4.3 AR Technology -- 4.3.1 High Level Augmented Reality -- 4.3.2 Limitations of Enhanced Image -- 4.3.3 Limitations of CAD Model -- 4.3.4 Augmented Reality in Manufacturing Sector -- 4.4 Requirement of Augmented Reality -- 4.4.1 Capability of AR -- 4.4.2 Computational Hardware Capabilities -- 4.4.3 Symbol-Based Tracking Software -- 4.5 Conclusions -- References.
Chapter 5 Computational Intelligence-Based Image Classification for 3D Printing: Issues and Challenges -- 5.1 Introduction -- 5.2 Brief Concepts -- 5.2.1 3D Printing Tools -- 5.2.2 Artificial Intelligence-Based Digital Marketing -- 5.2.3 Automated Machine Learning Prediction System -- 5.3 Role of Artificial Intelligence in Industry 4.0 -- 5.3.1 3D Printing Process -- 5.3.2 Enhancement in Machine Learning -- 5.3.3 Genetics-Based Machine Learning -- 5.3.4 Slicing Technique in 3D Model -- 5.3.5 Printing Path Trajectory -- 5.3.6 Improvement in Computational Simulation -- 5.3.7 Improving Service-Oriented Architecture -- 5.3.8 Capabilities of Cloud Computing -- 5.3.9 Hamming Distance Technique -- 5.3.10 Improving Knowledge Skills -- 5.3.11 Object Detection Algorithm -- 5.3.12 Improvement in Manufacturing Defects -- 5.4 Conclusion -- References -- Chapter 6 Role of Cybersecurity to Safeguard 3D Bioprinting in Healthcare: Challenges and Opportunities -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Creation of 3D Objects and Printing -- 6.3.1 Benefits of 3D Printing -- 6.3.2 Bioprinting -- 6.3.3 A Flow Diagram Depicting the Bioprinting Process -- 6.3.4 Datasets Used in Bioprinting -- 6.4 Schematic Diagram of 3D Bioprinting -- 6.4.1 3D Bioprinting Strategies -- 6.4.2 Comparison Among the 3D Bioprinting Approaches -- 6.4.3 Materials Used in Bioprinting -- 6.4.4 Bioprinting in Diverse Domains -- 6.5 Cyberthreats Posed to Bioprinting -- 6.5.1 Challenges and Opportunities of Cybersecurity in Bioprinting -- 6.5.2 Proposed Solutions -- 6.5.3 Combating the Cybersecurity Risks of 3D Bioprinting -- 6.5.4 Blockchain Technology and Bioprinting -- 6.5.5 A Comparative Survey of Cyberthreats in Additive Manufacturing Technology -- 6.6 Conclusion -- References -- Chapter 7 Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting.
7.1 Introduction -- 7.2 Current 3D Bioprinting Market Trends -- 7.3 Legal and Ethical Perspectives -- 7.4 Regarding the Introduction and Advancement of 3D Bioprinting -- 7.4.1 Current and Potential Paths for Bioethical Discourse -- 7.4.2 Legal Concerns with the Introduction of 3D Bioprinting Into Clinical Practice -- 7.4.3 Ethical Concerns with the 3D Bioprinting of Artificial Ovaries and Their Use in Clinical Settings -- 7.5 Conclusion -- 7.6 Future Scope -- References -- Chapter 8 Optimizing 3D Bioprinting Using Advanced Deep Learning Techniques A Comparative Study of CNN, RNN, and GAN -- 8.1 Introduction -- 8.2 Convolutional Neural Networks in Optimization of 3D Bioprinting -- 8.3 RNN in Optimization of 3D Bioprinting -- 8.4 Generative Adversarial Networks (GAN) in Optimization of 3D Bioprinting -- 8.5 Datasets Used for Optimization of 3D Bioprinting -- 8.6 3D Slicer Medical Image Segmentation Dataset -- 8.7 Sensor Data -- 8.8 Open Organ Database Dataset -- 8.9 Proposed Model -- 8.10 CNN U-Net -- 8.11 RNN Long Short-Term Memory -- 8.12 Wasserstein Generative Adversarial Network -- 8.13 Process of Combined Model -- 8.14 Conclusion -- References -- Chapter 9 Research Trends in Intelligence-Based Bioprinting for Construction Engineering Applications -- 9.1 Introduction -- 9.2 Analysis of Bioprinting -- 9.3 Model Development in Bioprinting Technology -- 9.4 3D Bioprinting Academic Institutions in the World -- 9.5 Emerging Bioprinting Technology -- 9.5.1 Opportunities -- 9.5.2 Challenges -- 9.6 Development in Bioengineering -- 9.7 Evolution of Patent Trends in Bioprinting -- 9.8 Conclusions -- References -- Chapter 10 Design and Development to Collect and Analyze Data Using Bioprinting Software for Biotechnology Industry -- 10.1 Introduction -- 10.2 Digital Technology in Bioprinting -- 10.2.1 Shape of Bioprinting.
10.2.2 Heterogeneity Units of Material -- 10.2.2.1 Tissue Improvement -- 10.2.2.2 Formation of Biomaterials -- 10.2.2.3 Biomaterial and Biological Factors -- 10.2.3 Dynamic Changes in Fabrication Process -- 10.3 Designing Techniques in Bioprinting -- 10.3.1 Data Processing in Biomedical Imaging -- 10.3.2 Process Bioprinting Techniques -- 10.3.3 Interaction of Bioink Formulation -- 10.4 3D Bioprinting -- 10.4.1 Optimized Bioprinting -- 10.4.2 Modifying Crosslinking -- 10.4.3 Multiple Crosslinking -- 10.4.4 Enhance Bioprinting -- 10.4.5 Hybrid Bioprinting -- 10.5 Enhanced Biotissue Printing -- 10.5.1 Integrating Thickness of Engineered Tissue -- 10.5.2 Integration and Enhancement of Cellular Interaction -- 10.5.3 DNA with a Smart Biomaterial -- 10.5.3.1 Biomaterials -- 10.5.3.2 Reactive Hydrogel to External Stimuli -- 10.5.4 Simulation -- 10.6 Conclusion -- 10.7 Future Work -- References -- Chapter 11 Cyborg Intelligence for Bioprinting in Computational Design and Analysis of Medical Application -- 11.1 Introduction -- 11.2 Next Generation of Bioprinting -- 11.2.1 Medicine Management -- 11.2.2 Varieties of Bioprinting Material -- 11.2.2.1 Thermoresponsive Materials -- 11.2.2.2 Biocompatible Polymers Materials -- 11.2.2.3 Endophyte Biocompatible Polymers Materials -- 11.2.2.4 Photo-Conductive Polymer Materials -- 11.2.2.5 UV-Assisted in 3D Printing -- 11.2.2.6 Sensitivity Polymeric Materials -- 11.2.2.7 Macromolecules Materials -- 11.2.2.8 Dual-Sensitive Materials -- 11.2.3 Biosensing Scaffolds -- 11.3 Biosensors and Actuators -- 11.3.1 Fabricated Scaffold Tissues -- 11.3.2 Vascularizing Tissues -- 11.3.3 4D Bioprinting Neural Tissue -- 11.3.4 Longitudinal Deformation -- 11.3.5 Uses of Biomedical Appliances -- 11.4 Enhancing Technology in Bioprinting -- 11.5 Conclusion and Future Work -- References.
Chapter 12 Computer Vision-Aides 3D Bioprinting in Ophthalmology Recent Trends and Advancements.
Record Nr. UNINA-9910877204603321
Gangadevi E  
Newark : , : John Wiley & Sons, Incorporated, , 2024
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Computer Vision in Smart Agriculture and Crop Management
Computer Vision in Smart Agriculture and Crop Management
Autore Dhanaraj Rajesh Kumar
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (402 pages)
Altri autori (Persone) BalusamyBalamurugan
SamuelPrithi
SathyamoorthyMalathy
BashirAli Kashif
ISBN 9781394186662
1394186665
9781394186686
1394186681
9781394186679
1394186673
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910908384203321
Dhanaraj Rajesh Kumar  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Deep Learning for Targeted Treatments : Transformation in Healthcare
Deep Learning for Targeted Treatments : Transformation in Healthcare
Autore Malviya Rishabha
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (458 pages)
Altri autori (Persone) GhineaGheorghita
DhanarajRajesh Kumar
BalusamyBalamurugan
SundramSonali
Soggetto genere / forma Electronic books.
ISBN 1-119-85798-8
1-119-85797-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors.
5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support.
7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction.
9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors.
11.5.1.1 Heredity.
Record Nr. UNINA-9910595599103321
Malviya Rishabha  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Digital Twins in Industrial Production and Smart Manufacturing : An Understanding of Principles, Enhancers, and Obstacles
Digital Twins in Industrial Production and Smart Manufacturing : An Understanding of Principles, Enhancers, and Obstacles
Autore Dhanaraj Rajesh Kumar
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (444 pages)
Altri autori (Persone) BalusamyBalamurugan
SamuelPrithi
BashirAli Kashif
KadrySeifedine
ISBN 1-394-19533-8
1-394-19532-X
1-394-19531-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910889695103321
Dhanaraj Rajesh Kumar  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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