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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
Opac: Controlla la disponibilità qui
Automated Machine Learning and Industrial Applications
Automated Machine Learning and Industrial Applications
Autore Gangadevi E
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (351 pages)
Disciplina 006.3/1
Altri autori (Persone) ShriM. Lawanya
BalusamyBalamurugan
DhanarajRajesh Kumar
Soggetto topico Machine learning
ISBN 1-394-27242-1
1-394-27241-3
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 Design and Architecture of AutoML for Data Science in Next-Generation Industries -- 1.1 Introduction -- 1.2 Modular Design -- 1.3 Data Handling -- 1.4 Model Training and Selection -- Conclusion -- References -- Chapter 2 Automated Machine Learning Model in Secure Data Transmission in Sustainable Healthcare Sensor Network Using Quantum Blockchain Architecture -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Proposed Model -- 2.4 Results and Discussion -- 2.5 Conclusion -- References -- Chapter 3 Automated Machine Learning in the Biological and Medical Healthcare Industries: Analysis Interpretation and Evaluation -- 3.1 Introduction -- 3.1.1 Rise of AutoML -- 3.1.2 Significance of AutoML in Biological and Medical Healthcare -- 3.2 Methodology for Effective Data Management -- 3.3 Foundations of Automated Machine Learning -- 3.3.1 Understanding Automated Machine Learning -- 3.3.2 Components and Workflow -- 3.3.3 Pros of AutoML Implementation -- 3.3.4 Cons of AutoML Implementation -- 3.4 Applications in Healthcare -- 3.4.1 Disease Diagnosis -- 3.4.2 Drug Discovery and Development -- 3.4.3 Personalized Medicine -- 3.4.4 Predictive Analytics in Healthcare -- 3.5 Case Studies and Success Stories -- 3.5.1 Noteworthy Implementations -- 3.5.2 Impact on Patient Outcomes -- 3.5.3 Challenges Encountered and Overcome -- 3.6 Ethical Implications -- 3.6.1 Data Privacy and Security -- 3.6.2 Fairness and Bias Considerations -- 3.7 Practical Implementation: From Concept to Application -- 3.7.1 Problem Formulation and Data Preparation -- 3.7.2 Tool Selection -- 3.7.3 Training and Evaluation -- 3.7.4 Explainability and Interpretability -- 3.7.5 Deployment and Monitoring -- 3.8 Future Directions and Trends -- 3.8.1 Integration with Emerging Technologies.
3.8.2 AutoML in Clinical Trials and Research -- 3.9 Conclusion -- References -- Chapter 4 Advancements in AI and AutoML for Plant Leaf Disease Identification in Sustainable Agriculture -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Preliminary Analysis for Agricultural Diseases -- 4.3.1 Datasets and Descriptions -- 4.3.2 Normalization and Scaling -- 4.3.3 Feature Extraction and Classification -- 4.3.4 Spectral Image Analysis -- 4.4 Proposed Methods -- 4.4.1 Leaf Disease Identification Using ResNet -- 4.4.2 Pixel-Based Information Extraction and Ant Colony Optimization -- 4.4.3 Image Enhancement and Segmentation -- 4.5 Conclusion -- References -- Chapter 5 Predictive Maintenance in Industrial Settings: Video Analytics at the Edge with AutoML -- 5.1 Introduction -- 5.2 Literature Review -- 5.3 Proposed Design of an Efficient Model for Enhancing Predictive Maintenance in Industrial Settings -- 5.4 Result Evaluation and Comparative Analysis -- 5.5 Conclusion and Future Scope -- Future Scope -- References -- Chapter 6 AutoCRM-An Automated Customer Relationship Management Learning System with Random Search Hyper-Parameter Optimization -- 6.1 Introduction -- 6.1.1 Opinion Mining or Sentiment Analysis -- 6.1.2 Machine Learning Approaches -- 6.1.3 Machine Learning Pipeline (ML) -- 6.1.4 Automated Machine Learning (AutoML) -- 6.1.4.1 AutoML Core Goals -- 6.1.4.2 AutoML Tools -- 6.1.5 Objectives of this Research -- 6.1.6 Outline -- 6.2 Literature Review -- 6.3 Methodology -- 6.3.1 Data Preparation -- 6.3.1.1 Data Collection -- 6.3.1.2 Data Cleaning and Labeling -- 6.3.1.3 Data Visualization -- 6.3.1.4 Feature Engineering -- 6.3.2 AutoKeras -- 6.3.2.1 Neural Architecture Search and Hyper-Parameter Tuning -- 6.3.3 Model Selection -- 6.4 Results and Discussions -- 6.4.1 Comparative Analysis: AutoML vs ML -- 6.5 Conclusion -- References.
Chapter 7 The Competence of Customer Support Team for Sentiment Analysis in Chatbots Using AutoML -- 7.1 Introduction -- 7.1.1 Background -- 7.1.2 Problem Definition -- 7.1.3 Scope -- 7.1.4 Technical Highlights -- 7.1.5 Objectives -- 7.1.6 Common Chatbot Use Cases -- 7.1.7 The Basics of Sentiment Analysis -- 7.1.8 Levels of Sentiment Analysis -- 7.2 Literature Survey -- 7.3 Methodology for Chatbot Sentiment Analysis -- 7.3.1 AutoML-Based Exploratory Data Analysis and Subjectivity Detection -- 7.3.2 Trilateral Modifier Utilization -- 7.3.3 Sentiment Polarity Detection -- 7.3.4 Workflow of Customer Service Inquiry-Chatbot Response -- 7.3.5 Scoring -- 7.4 Experimentation and Results -- 7.4.1 Performance Metrics -- 7.4.2 Data Collection -- 7.4.3 Evaluation -- 7.5 Conclusion -- References -- Chapter 8 Financial Risk Prediction with Banking Monitoring for Cyber Security Analysis Using Automated Machine Learning -- 8.1 Introduction -- 8.2 Related Works -- 8.3 System Model -- 8.3.1 Cyber Security Detection Using Gaussian Encoder Belief Network -- 8.4 Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 AutoML Ecosystem and Open-Source Platforms: Challenges and Limitations -- 9.1 Introduction -- 9.2 Related Study -- 9.3 Ecosystem of AutoML -- 9.3.1 Data Preprocessing -- 9.3.2 Model Selection -- 9.3.3 Hyperparameter Tuning -- 9.3.4 Model Evaluation and Deployment -- 9.4 AutoML Frameworks -- 9.4.1 Google AutoML -- 9.4.2 IBM Watson AutoAI -- 9.4.3 Microsoft Azure AutoML -- 9.4.4 H2O.ai -- 9.4.5 Data Robot -- 9.4.6 Databricks AutoML -- 9.4.7 Tune -- 9.4.8 AutoKeras -- 9.4.9 H2O Driverless AI -- 9.4.10 RapidMiner -- 9.4.11 Google Cloud AutoML Tables -- 9.4.12 H2O Sparkling Water -- 9.4.13 Turi Create -- 9.4.14 Big ML -- 9.4.15 Hail -- 9.5 Open-Source AutoML Libraries -- 9.5.1 Auto-Sklearn -- 9.5.2 TPOT (Tree-Based Pipeline Optimization Tool).
9.5.3 AutoKeras -- 9.5.4 MLBox -- 9.5.5 AutoGluon -- 9.5.6 H2O AutoML -- 9.5.7 Auto-WEKA -- 9.5.8 AutoGluon Tabular -- 9.5.9 FLAML -- 9.5.10 Ludwig -- 9.6 Types of AutoML Approaches -- 9.6.1 Fully Automated -- 9.6.2 Human-in-the-Loop -- 9.6.3 Model Assisted -- 9.7 Benefits of AutoML -- 9.8 Challenges and Limitations -- 9.9 Conclusion -- References -- Chapter 10 Plant Disease Identification Using Extended-EfficientNet Deep Learning Model in Smart Farming -- 10.1 Introduction -- 10.1.1 Obstacles in the Agricultural Sector -- 10.1.1.1 Soil Erosion -- 10.1.1.2 Absence of High-Quality Seeds -- 10.1.1.3 Lack of Contemporary Farming Machinery -- 10.1.2 Challenges of AI in Agriculture -- 10.1.3 Existing Plant Disease Identification Methods -- 10.2 Literature Review -- 10.3 Materials and Methods -- 10.3.1 Dataset -- 10.3.2 Existing CNN Models -- 10.3.2.1 AlexNet -- 10.3.2.2 VGG16 -- 10.3.2.3 ResNet50 -- 10.3.2.4 Inception V3 -- 10.4 Methodology-E-ENet -- 10.4.1 Localization of the Leaf -- 10.4.2 Segmentation of Leaf Image -- 10.4.3 The Diseased Leaf Identification -- 10.5 Experimental Analysis -- 10.5.1 The Acquisition of Data -- 10.5.2 The Parameter Setup -- 10.5.2.1 The Configuration of Parameters for Leaf Localization -- 10.5.2.2 The Configuration of Parameters for Leaf Segmentation -- 10.5.2.3 The Configuration of Parameters for Leaf Retrieval -- 10.6 Results -- 10.6.1 The Leaf Localization Outcome -- 10.6.2 The Outcomes of Leaf Segmentation -- 10.6.3 The Result of Disease Identification -- 10.7 Comparative Test -- 10.8 Summary -- References -- Chapter 11 AutoML-Driven Deep Learning for Fake Currency Recognition -- 11.1 Introduction -- 11.2 Literature Review -- 11.2.1 Scope -- 11.2.2 Objectives -- 11.3 Proposed System -- 11.4 Methodology -- 11.5 Convolutional Neural Network -- 11.6 Analysis Modeling -- 11.6.1 Behavioral Modeling -- 11.7 Software Testing.
11.7.1 Types of Testing -- 11.7.2 Test Cases -- 11.8 Results and Discussions -- 11.9 Conclusion -- References -- Chapter 12 Blockchain and Automated Machine Learning-Based Advancements for Banking and Financial Sectors -- 12.1 Introduction -- 12.2 Understanding Blockchain and AutoML -- 12.3 Need of Blockchain -- 12.4 Synergies Between Blockchain and AutoML -- 12.5 Applications in Banking and Finance -- 12.6 Applications of AutoML in Industries -- 12.7 Case Studies and Real-World Applications -- 12.8 Blockchain in Finance -- 12.9 Real-World Examples and Case Studies -- 12.10 Benefits and Challenges -- 12.11 Discussion -- 12.12 Limitations -- 12.13 Recommendations for Implementation -- 12.14 Ethical Considerations and Responsible AI -- 12.15 Future Directions and Emerging Trends -- 12.16 Future Scope -- 12.17 Conclusion -- References -- Chapter 13 Advances in Automated Machine Learning for Precision Healthcare and Biomedical Discoveries -- 13.1 Introduction -- 13.1.1 Some of the Recent Publications and their Findings -- 13.2 Current Day Usage of AI -- 13.2.1 Deep Learning and Neural Networks -- 13.2.2 Natural Language Processing (NLP) -- 13.2.2.1 Clinical Documentation -- 13.2.2.2 Disease Prediction -- 13.2.2.3 Chatbots and Virtual Assistants -- 13.2.2.4 Report Analysis -- 13.2.3 Automation -- 13.2.3.1 Appointment Scheduling -- 13.2.3.2 Medication Dispensary -- 13.2.3.3 Robotic Surgeries -- 13.2.3.4 Inventory Management -- 13.3 Data Management and Security in Healthcare AI -- 13.3.1 Data Acquisition and Storage -- 13.3.2 Data Processing and Analysis -- 13.3.3 Data Protection and Privacy -- 13.3.4 Balancing Technological Advancements with Data Governance -- 13.3.5 The Evolving Role of AI in Data Security -- 13.3.6 Continuous Education in Data Management and AI -- 13.3.7 Preparing for the Future of Healthcare AI and Data Management.
13.4 Challenges in Integrating AI into Healthcare Systems.
Record Nr. UNINA-9911019472703321
Gangadevi E  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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)
Disciplina 332.0285/63
Altri autori (Persone) GuptaMukul
DhanarajRajesh Kumar
BalusamyBalamurugan
GuptaParth M
Collana Fintech in a Sustainable Digital Society Series
Soggetto topico Finance - Technological innovations
ISBN 9781394233236
139423323X
9781394233250
1394233256
9781394233243
1394233248
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9911020266203321
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 Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa
Computational Intelligence in Internet of Agricultural Things / / edited by M. G. Sumithra, Malathy Sathyamoorthy, M. Manikandan, Rajesh Kumar Dhanaraj, Mariya Ouaissa
Autore Sumithra M. G
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (464 pages)
Disciplina 006.3
Altri autori (Persone) SathyamoorthyMalathy
ManikandanM
DhanarajRajesh Kumar
OuaissaMariya
Collana Studies in Computational Intelligence
Soggetto topico Computational intelligence
Artificial intelligence
Agriculture
Computational Intelligence
Artificial Intelligence
ISBN 9783031674501
3031674502
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- 1: Computational Intelligence and Internet of Things in the Agriculture Sector: An Introduction. -- 2: Role of Big Data Analytics in Intelligent Agriculture. -- 3: Machine learning-based remote monitoring and predictive analytics system for apple harvest storage: A statistical model based approach. -- 4: Revolutionizing Agriculture: Integrating IoT Cloud, And Machine Learning for Smart Farm Monitoring and Precision Agriculture. -- 5: Impact of Advanced Sensing Technologies in Agriculture with Soil, Crop, Climate and Farmland-based approaches using Internet of Things. -- 6: An analytical approach and concept mapping of agricultural issues using deep learning techniques. -- 7: Explainable AI for next generation Agriculture - Current Scenario and Future Prospects. -- 8: Barriers to implementing computational intelligence-based agriculture system. -- 9: Agri-Chain: A Blockchain-Empowered Smart Solution for Agricultural Industry. -- 10: Exploiting Internet of Things and AI-Enabled for Real-Time Decision Support in Precision Farming Practices. -- 11: Advancing Plant Disease Detection with Hybrid Models: Vision Transformer and CNN-Based Approaches. -- 12: Optimizing Agricultural Risk Management with Hybrid Block-chain and Fog Computing Architectures for Secure and Efficient Data Handling. -- 13: Innovating with Quantum Computing Approaches in Block-chain for Enhanced Security and Data Privacy in Agricultural IoT Systems. -- 14: Implementing Fog Computing in Precision Agriculture for Real-time Soil Health Monitoring and Data Management. -- 15: Empowering Farmers: An AI-Based Solution for Agricultural Challenges. -- 16: Artificial Intelligence in Agriculture: Potential Applications and Future Aspects. -- 17: Case study on Smart irrigation using Internet of Things and XAI Techniques.
Record Nr. UNINA-9910882897703321
Sumithra M. G  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Cyber Physical Energy Systems
Cyber Physical Energy Systems
Autore Sagar Shrddha
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (564 pages)
Disciplina 621.31
Altri autori (Persone) PoongodiT
DhanarajRajesh Kumar
PadmanabanSanjeevikumar
Soggetto topico Microgrids (Smart power grids) - Security measures
ISBN 9781394173006
1394173008
9781394172986
1394172982
9781394172993
1394172990
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 Cyber-Physical Systems: A Control and Energy Approach -- 1.1 Introduction -- 1.1.1 Background and Motivation -- 1.1.2 Testbeds, Revisions, and a Safety Study for Cyber-Physical Energy Systems -- 1.1.3 CPES Test Chamber -- 1.1.4 Significance and Contributions of Testbed -- 1.1.5 Testbed Setup -- 1.1.6 Illustration of Hybrid CPES Testbed Structure -- 1.2 Studies on CPES Safety -- 1.2.1 Attacks in the CPES System -- 1.2.2 Evaluation of Attack Impacts on CPES -- 1.2.3 CPES's Assault Detection Algorithms -- 1.2.4 CPES's Assault Mitigation and Defense Systems -- 1.2.5 Dangerous Imagery -- 1.2.6 Attack Database -- 1.3 Threat Evaluation -- 1.4 Theory of Cyber-Physical Systems Risk -- 1.4.1 Challenger Type -- 1.4.2 Attack Type -- 1.5 Threat Evaluation Methodology -- 1.5.1 Cyber-System Layer -- 1.5.2 Physical-System Layer -- 1.6 Experimental Setup for Cross-Layer Firmware Threats -- 1.6.1 Risk Model -- 1.6.2 Threat Evaluation -- 1.7 Conclusion -- References -- Chapter 2 Optimization Techniques for Energy Management in Microgrid -- 2.1 Introduction -- 2.1.1 Microgrid Systems -- 2.1.2 Energy Management System -- 2.1.3 Energy Management of Distribution System -- 2.1.4 Techniques to Take Into Account While Implementing the EMS -- 2.1.5 Strategies for Reducing Risk -- 2.1.6 Monitoring Power Systems -- 2.1.7 Demand Response, Price Strategy, and Demand Side Management -- 2.2 Explanation Methods for EMS -- 2.3 EQN EMS on an Arithmetic Optimization Basis -- 2.4 Heuristic-Oriented Methods to EMS Problem-Solving -- 2.5 EMS Solution Techniques Using Meta-Heuristics -- 2.6 Alternative EMS Implementation Strategies -- 2.6.1 SCADA System -- 2.7 Conclusion and Viewpoints -- References -- Chapter 3 Cyber-Physical Energy Systems for Smart Grid: Reliable Distribution -- 3.1 Introduction.
3.1.1 Need for Sustainable and Efficient Power Generation Through Smart Grid Technology and Cyber-Physical Technologies -- 3.1.2 CPES: The Integration of Physical and Digital Worlds -- 3.2 Cyber-Physical Energy Systems (CPES) -- 3.3 Forming Energy Systems -- 3.4 Energy Efficiency -- 3.4.1 CPES Usage on Smart Grids -- 3.5 Smart Grids -- 3.6 Cyber-Physical Systems -- 3.7 SG: A CPS Viewpoint -- 3.7.1 Challenges and Solutions for Coordinating Smart Grids and Cyber-Physical Systems -- 3.7.2 Techniques of Correspondence -- 3.7.3 Data Protection -- 3.7.4 Data Skill and Engineering -- 3.7.5 Distributed Computation -- 3.7.6 Distributed Intellect -- 3.7.7 Distributed Optimization -- 3.7.8 Distributed Controller -- 3.8 Upcoming Prospects and Contests -- 3.8.1 Big Data -- 3.8.2 Cloud Computing -- 3.8.3 IoT -- 3.8.4 Network Science -- 3.8.5 Regulation and Guidelines -- 3.9 Conclusion -- References -- Chapter 4 Evolution of AI in CPS: Enhancing Technical Capabilities and Human Interactions -- 4.1 Introduction to Cyber-Physical System -- 4.2 The Cyber-Physical Systems Architecture -- 4.2.1 5C Architecture or CPS -- 4.2.1.1 Connection -- 4.2.1.2 Conversion -- 4.2.1.3 Cyber -- 4.2.1.4 Knowledge -- 4.2.1.5 Configuration -- 4.3 Cyber-Physical Systems as Real-Time Applications -- 4.3.1 Robotics Distributed -- 4.3.2 Manufacturing -- 4.3.3 Distribution of Water -- 4.3.4 Smart Greenhouses -- 4.3.5 Healthcare -- 4.3.6 Transportation -- 4.4 Impact of AI on Cyber-Physical Systems -- 4.5 Policies -- 4.6 Expected Benefits and Core Promises -- 4.7 Unintended Consequences and Implications for Policy -- 4.7.1 Negative Social Impacts -- 4.7.2 Cybersecurity Risks -- 4.7.3 Impact on the Environment -- 4.7.4 Ethical Issues -- 4.7.5 Policy Implications -- 4.8 Employment and Delegation of Tasks -- 4.9 Safety, Responsibility, and Liability -- 4.10 Privacy Concerns.
4.10.1 Data Collection and Use -- 4.10.2 Data Security -- 4.10.3 Data Sharing -- 4.10.4 Bias and Discrimination -- 4.10.5 User Empowerment -- 4.11 Social Relations -- 4.11.1 Cyber-Physical Systems and Transport -- 4.11.2 Trade of Dual-Use Technology -- 4.11.3 Civil Liberties (Data Protection, Privacy, etc.) -- 4.11.4 Safety (Such as Risk Analysis, Product Safety, etc.) -- 4.11.5 Healthcare (Medical Devices, Clinical Trials, and E-Health Devices) -- 4.11.6 Energy and Environment -- 4.11.7 Horizontal Legal Issues (Cross-Committee Considerations) -- 4.12 Economic Study on CPS -- 4.12.1 Better Resource Allocation -- 4.12.2 Enhanced Marketability -- 4.12.3 Robustness and Resilience -- 4.12.4 Regulatory Compliance -- 4.12.5 Making Decisions in Real-Time -- 4.13 Case Studies -- 4.13.1 The Daily Lives of Older Persons and Disabled Individuals with CPS -- 4.13.2 CPS in Healthcare -- 4.13.3 CPS for Security and Safety -- 4.14 Conclusion -- References -- Chapter 5 IoT Technology Enables Sophisticated Energy Management in Smart Factory -- 5.1 Introduction -- 5.2 IOT Overview -- 5.2.1 The Evolution of the Internet -- 5.2.2 IoT Sensing -- 5.2.3 IOT Data Protocol and Architecture -- 5.3 IOT Enabling Technology -- 5.3.1 Application Domain -- 5.3.2 Middleware Domain -- 5.3.3 Network Domain -- 5.3.4 Object Domain -- 5.4 IOT in Energy Sector -- 5.4.1 Internet of Things and Energy Generation -- 5.5 Challenges of Applying IOT -- 5.6 Reference Architecture for IoT-Based Smart Factory -- 5.7 Characteristics of Smart Factory -- 5.8 Challenges for IoT-Based Smart Industry -- 5.9 How IoT Will Support Energy Management in Smart Factory -- 5.10 IoT Energy Management Architecture for Industrial Applications -- 5.10.1 IoT-Based Energy Management Technology -- 5.10.2 Energy Harvesting -- 5.11 Case Study: Smart Factory -- 5.11.1 Supply Side -- 5.11.2 Photovoltaic Power Generation.
5.11.3 Smart Micro-Grid -- 5.11.4 Demand Side -- 5.11.5 Virtualization -- 5.12 Conclusion -- References -- Chapter 6 IOT-Based Advanced Energy Management in Smart Factories -- 6.1 Introduction -- 6.2 Smart Factory Benefits of IOT-Based Advanced Energy Management -- 6.3 Role of IOT Technology in Energy Management -- 6.4 Developing an IOT Information Model for Energy Efficiency -- 6.5 Integrating Intelligent Energy Systems (IES) and Demand Response (DR) -- 6.6 How to Accurately Measure and Manage Your Energy Usage -- 6.7 Introduction to Energy Efficiency Measures -- 6.8 Identifying Opportunities to Reduce Energy Use -- 6.9 Monitoring and Measuring Energy Usage -- 6.10 Establishing Accounting and Incentives -- 6.11 Sustaining the Long-Term Benefits of Optimized Energy Usage -- 6.12 Role of Cyber Security When Implementing IoT-Based Advanced Energy Solutions -- 6.13 Materials Required in Smart Factories -- 6.14 Methods in IoT-Based Smart Factory Implementation -- 6.15 Steps for Developing an IoT-Based Energy Management System -- 6.15.1 Assess Current Energy Usage -- 6.15.2 Develop an Energy Conservation Plan -- 6.15.3 Implement IoT Technology -- 6.15.4 Monitor Results -- 6.16 Challenges For Adopting IoT-Based Energy Management Systems -- 6.16.1 Big Data and Analytics -- 6.16.2 Connectivity Constraints -- 6.16.3 Data Security and Privacy Issues -- 6.16.4 Device Troubleshooting -- 6.17 Recommendations for Overcoming the Challenges With Implementing IoT-Based Advanced Energy Solution -- 6.17.1 IoT-Enabled Automation -- 6.17.2 Smart Sensors -- 6.17.3 Predictive Analytics -- 6.18 Case Studies -- 6.18.1 Automated Demand Response (ADR) -- 6.18.2 Automated Maintenance -- 6.18.3 Predictive Analytics -- 6.19 Case Studies for Successful Implementation -- 6.20 Applications -- 6.21 Different Techniques for Monitoring and Control of IoT Devices.
6.22 Literature Survey -- 6.23 Conclusion -- References -- Chapter 7 Challenges in Ensuring Security for Smart Energy Management Chapter Systems Based on CPS -- 7.1 Introduction -- 7.1.1 Brief Overview of Smart Energy Management Systems and Cyber-Physical Systems -- 7.1.2 Importance of Security in CPS-Based Smart Energy Management -- 7.2 Cyber-Physical Systems and Smart Energy Management -- 7.2.1 CPS Architecture and Components -- 7.2.2 Types of CPS-Based Smart Energy Management Systems -- 7.2.3 Common Communication Protocols Used in CPS-Based Smart Energy Management -- 7.2.4 Cyber Security Threats in CPS-Based Systems -- 7.3 Security Challenges in CPS-Based Smart Energy Management -- 7.3.1 Cyber Security Threats to CPS-Based Smart Energy Management Systems -- 7.3.2 Vulnerabilities of Communication Protocols Used in Smart Energy Management -- 7.3.3 Attack Vectors for Compromising CPS-Based Smart Energy Management Systems -- 7.4 Cyber Security Standards and Guidelines for Smart Energy Management -- 7.4.1 Cyber Security Incidents in Smart Energy Management -- 7.5 Conclusion -- References -- Chapter 8 Security Challenges in CPS-Based Smart Energy Management -- 8.1 Introduction -- 8.2 CPS Architecture -- 8.3 The Driving Forces for CPS -- 8.3.1 Big Data -- 8.3.2 Cloud -- 8.3.3 Machine-to-Machine Communication and Wireless Sensor Networks -- 8.3.4 Mechatronics -- 8.3.5 Cybernetics -- 8.3.6 Systems of Systems -- 8.4 Advances in Cyber-Physical Systems -- 8.4.1 Application Domains of CPS -- 8.4.1.1 Industrial Transformation -- 8.4.1.2 Smart Grid -- 8.4.1.3 Healthcare -- 8.4.1.4 Smart Parking System -- 8.4.1.5 Household CPS -- 8.4.1.6 Aerospace -- 8.4.1.7 Agriculture -- 8.4.1.8 Construction -- 8.5 Energy Management through CPS -- 8.5.1 Energy Management of CPS for Smart Grid -- 8.5.2 Energy Management of CPS for Smart Building Structure.
8.5.3 Energy Management of CPS for Autonomous Electric Vehicles in Smart Transportation.
Record Nr. UNINA-9911019870203321
Sagar Shrddha  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Distributed Deep Learning and Explainable AI (XAI) in Industry 4. 0
Distributed Deep Learning and Explainable AI (XAI) in Industry 4. 0
Autore Krishnasamy Lalitha
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2025
Descrizione fisica 1 online resource (473 pages)
Disciplina 658.40380285631
Altri autori (Persone) DhanarajRajesh Kumar
PamucarDragan
OuaissaMariya
Collana Information Systems Engineering and Management Series
ISBN 3-031-94637-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9911028763603321
Krishnasamy Lalitha  
Cham : , : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Drones for Transportation Logistics and Disaster Management
Drones for Transportation Logistics and Disaster Management
Autore Prasanth A
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (430 pages)
Altri autori (Persone) DhanarajRajesh Kumar
SabharwalMunish
SharmaVandana
KadrySeifedine
ISBN 1-394-38645-1
1-394-38644-3
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 Journey to Transportation and Logistics Management Using Drone: Digitization and Technological Evolution -- 1.1 Introduction -- 1.1.1 Drone Development and Advancements -- 1.1.2 Sustainability Concern -- 1.2 Literature Review -- 1.3 Fundamental Elements of Drone Technology -- 1.4 Evolution of Drone Technology -- 1.5 Use Case in Various Sectors -- 1.6 Application in Transportation and Logistics -- 1.7 Conclusion -- References -- Chapter 2 Challenges of Big Data Implementation in Drone-Based Logistics -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Big Data in Transit -- 2.4 Factors Affecting UAV Implementation in Logistics -- 2.4.1 Legal Factors -- 2.4.2 Financial Factors -- 2.4.3 Knowledge and Behavioral Factors -- 2.4.4 Privacy and Safety Factors -- 2.5 Conclusion -- References -- Chapter 3 Frameworks for Handover Management for the Networks of Future Drones -- 3.1 An Overview -- 3.2 Literature Review -- 3.3 Handover Management for Future Drone Networks (HOM-FDN) -- 3.4 Results and Discussion -- 3.4.1 Accuracy Analysis -- 3.4.2 Efficiency Analysis -- 3.4.3 Performance Analysis -- 3.4.4 Safety Analysis -- 3.4.5 Prediction Analysis -- 3.5 Conclusion -- References -- Chapter 4 Convergence of Internet of Vehicle Things and Drones: An Interoperability Perspective -- 4.1 Introduction -- 4.1.1 Overview of IoT in Vehicles and Drones -- 4.1.2 Significance of Convergence -- 4.1.3 Significance of Interoperability -- 4.2 Communication Standards for Integration -- 4.2.1 Vehicular Communication Standards -- 4.2.2 Drone Communication Standards -- 4.2.3 Cross-Domain Communication Standards -- 4.2.4 Interoperability Considerations -- 4.3 Data Exchange Protocols -- 4.3.1 IoVT Data Exchange Protocols -- 4.3.2 Drone Data Exchange Protocols.
4.3.3 Cross-Domain Data Exchange Protocols -- 4.3.4 Interoperability Considerations -- 4.4 Integration with Edge Computing -- 4.4.1 IoVT Integration with Edge Computing -- 4.4.2 Drone Integration with Edge Computing -- 4.5 Security and Privacy Measures -- 4.5.1 Security Measures -- 4.5.2 Privacy Measures -- 4.6 IoVT Regulations -- 4.6.1 Automotive Industry Standards -- 4.6.2 Compliance with Road Safety Regulations -- 4.6.3 Drone Regulations -- 4.7 Cross-Industry Collaboration -- 4.7.1 Synergies between IoVT and Drones -- 4.7.2 Industry Partnerships -- 4.8 Interoperability Challenges -- 4.8.1 Technical Challenges -- 4.8.2 Regulatory Challenges -- 4.9 Application and Future of IoVT and Drones -- 4.10 Conclusion -- References -- Chapter 5 5G Communication in Drones for Surveillance in Future Transportation Activities -- 5.1 Introduction -- 5.2 Overview of 5G Communication -- 5.2.1 Key Features of 5G Networks -- 5.3 Drones in Transportation -- 5.3.1 Surveillance and Inspection -- 5.3.2 Delivery Services -- 5.3.3 Current Challenges for the Usage of Drones in Transportation -- 5.4 Drones and 5G Integration -- 5.5 Network Architecture for Drone and 5G in Transportation -- 5.5.1 Infrastructure of 5G Network -- 5.5.2 Edge Computing and Low Latency -- 5.5.3 Drone Traffic Management System -- 5.5.4 Authentication and Security -- 5.5.5 Analytics and Network Monitoring -- 5.5.6 Ground Control Stations (GCS) -- 5.5.7 Regulatory Compliance -- 5.5.8 Integration with Other Transportation Systems -- 5.5.9 Redundancy and Scalability -- 5.6 Security and Privacy Considerations -- 5.6.1 Security Considerations -- 5.6.2 Privacy Considerations -- 5.6.3 Enhanced Security Features in 5G -- 5.7 Regulatory and Ethical Considerations -- 5.7.1 Regulatory Considerations -- 5.7.2 Ethical Considerations -- 5.8 Future Trends and Innovations -- 5.9 Conclusion -- References.
Chapter 6 Impact and Assessment of Artificial Intelligence-Enabled UAV for Real-Time Data Streaming Application -- 6.1 Introduction -- 6.2 Overview of UAVs Powered by AI -- 6.2.1 Understanding AI-Enabled UAVs: Definition and Features -- 6.2.2 Capabilities of AI-Enabled UAVs -- 6.2.3 Advantages of Using UAVs for Data Streaming -- 6.2.3.1 Quick and Effective Information Gathering -- 6.2.3.2 Improved Safety Features and Higher Cost- Effectiveness -- 6.2.3.3 Accuracy and Precision -- 6.2.3.4 Real-Time Decision Support -- 6.2.3.5 Access to Remote Regions -- 6.2.3.6 Enhanced Monitoring and Surveillance -- 6.2.3.7 Scalability and Flexibility -- 6.2.4 Most Important Technologies and Components Utilized -- 6.3 Applications of AI-Enabled UAVs in Real-Time Data Streaming -- 6.3.1 Environmental Monitoring and Conservation -- 6.3.1.1 Wildlife Conservation -- 6.3.1.2 Forestry and Land Governance -- 6.3.1.3 Marine and Coastal Surveillance -- 6.3.1.4 Environmental Research -- 6.3.2 Disaster Management and Response -- 6.3.2.1 Search and Rescue Operations -- 6.3.2.2 Damage Assessment -- 6.3.2.3 Hazard Monitoring -- 6.3.2.4 Evacuation Planning -- 6.3.3 Precision Agriculture and Crop Monitoring -- 6.3.3.1 Crop Health Assessment -- 6.3.3.2 Irrigation Management -- 6.3.3.3 Yield Forecasting -- 6.3.3.4 Soil Analysis -- 6.3.4 Infrastructure Inspection and Maintenance -- 6.3.4.1 Bridge and Building Inspections -- 6.3.4.2 Power Line and Utility Inspections -- 6.3.4.3 Monitoring Oil and Gas Facilities -- 6.3.4.4 Railway and Pipeline Inspections -- 6.3.5 Surveillance and Public Safety -- 6.4 Case Studies: Real-World Implementations -- 6.4.1 AI-Enabled UAVs for Monitoring Wildlife Populations -- 6.4.2 UAV-Based Aerial Imagery for Disaster Assessment -- 6.4.3 Precision Agriculture Using AI Algorithms and UAVs -- 6.4.4 Automated Infrastructure Inspection with UAVs.
6.4.5 UAVs for Surveillance and Emergency Response -- 6.4.6 Machine Learning and Deep Learning Techniques -- 6.4.6.1 Advancements in Machine Learning -- 6.4.6.2 Reinforcement Learning (RL) -- 6.4.6.3 Transfer Learning -- 6.4.6.4 Interpretable Models -- 6.4.6.5 Advancements in Deep Learning -- 6.4.6.6 Convolutional Neural Networks -- 6.4.6.7 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) -- 6.4.6.8 Generative Adversarial Networks (GANs) -- 6.4.7 Object Detection and Recognition -- 6.4.7.1 Single-Stage Object Detectors -- 6.4.7.2 Two-Stage Object Detectors -- 6.4.7.3 Optimal Object Detection -- 6.4.7.4 Fine-Grained Object Recognition -- 6.4.8 Image and Video Processing -- 6.4.8.1 Real-Time Image Segmentation -- 6.4.8.2 Image and Video Signature -- 6.4.8.3 Video Analysis -- 6.4.8.4 Model Generation for Creativity and Art -- 6.4.9 Sensor Fusion and Data Integration -- 6.4.9.1 Multimodal Sensor Fusion -- 6.4.9.2 Healthcare Data Integration -- 6.4.9.3 Environmental Monitoring -- 6.4.9.4 Industrial Automation -- 6.4.9.5 Smart Cities -- 6.5 Challenges and Considerations in AI-Enabled UAVs for Real- Time Data Streaming Applications -- 6.5.1 Data Privacy and Security -- 6.5.2 Regulatory Frameworks and Airspace Management -- 6.5.3 Technical Constraints and Limitations -- 6.5.4 Ethical Considerations and Societal Impact -- 6.6 Future Directions and Opportunities -- 6.6.1 Integration of AI and UAV Technologies -- 6.6.2 Collaborative Research and Development -- 6.6.3 Policy Implications and Standardization -- 6.6.4 Advancements in Hardware and Software Solutions -- 6.7 Conclusion and Future Enhancements -- References -- Chapter 7 Blockchain-Based Security and Privacy Solutions for Drones Systems -- 7.1 Introduction -- 7.1.1 Principle Functionality of the Blockchain -- 7.1.2 Foundation of Drones -- 7.1.3 Blockchain and Drones Security.
7.1.4 Related Work -- 7.2 Drones - The New Network Architecture -- 7.2.1 Components and Parameters -- 7.2.2 Drone Parameters -- 7.2.3 Drones Network Topology Architecture -- 7.3 Drones Privacy Solutions -- 7.4 Integration of Blockchain Functioning with Drones -- 7.5 Security Challenges and the Road Ahead -- 7.6 Conclusion -- References -- Chapter 8 Design and Development of Modular and Multifunctional UAV for Amphibious Landing and Surround Sense Module -- 8.1 Introduction -- 8.2 UAV Design Considerations -- 8.2.1 Design Parameters and Features -- 8.2.2 Aerodynamic Characteristics -- 8.2.3 Power Requirements -- 8.3 Development of Modular UAV -- 8.4 Surround Sense Module -- 8.5 Integration and Testing -- 8.6 Conclusion -- Acknowledgement -- References -- Chapter 9 Implementing Mission Critical Public Safety Using Communication in Drones Network -- 9.1 Introduction -- 9.2 Related Work -- 9.3 UAV - Characteristics and Strategies -- 9.3.1 What is UAV? -- 9.3.2 How Does UAV Work? -- 9.3.3 UAV Categorization -- 9.3.4 Drones for Public Security -- 9.3.5 UAV Placement Strategies -- 9.4 Communication Framework Support by UAVs -- 9.4.1 Potential Roles -- 9.5 Conclusion -- References -- Chapter 10 Assessing the Impact of Drones on Students' Engagement and Learning Outcomes -- 10.1 Introduction -- 10.1.1 Background and Context of the Study -- 10.1.2 Significance of Exploring Drone-Assisted Learning -- 10.1.3 Research Objectives -- 10.2 Literature Review -- 10.2.1 Overview of Drones in Education -- 10.2.2 Previous Research on Student Engagement and Learning Outcomes with Technology Integration -- 10.2.3 Theoretical Frameworks Supporting the Use of Drones in Education -- 10.3 Methodology -- 10.3.1 Research Design and Approach -- 10.3.2 Participant Selection and Sampling Strategy -- 10.3.3 Data Collection Methods -- 10.3.4 Data Analysis Plan.
10.4 Implementation of Drone-Assisted Learning Activities.
Record Nr. UNINA-9911038525403321
Prasanth A  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Energy Conversion Systems-Based Artificial Intelligence : Applications and Tools / / edited by Mahmoud A. Mossa, Najib El Ouanjli, Mariya Ouaissa, Mariyam Ouaissa, Rajesh Kumar Dhanaraj
Energy Conversion Systems-Based Artificial Intelligence : Applications and Tools / / edited by Mahmoud A. Mossa, Najib El Ouanjli, Mariya Ouaissa, Mariyam Ouaissa, Rajesh Kumar Dhanaraj
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (XV, 223 p. 108 illus., 94 illus. in color.)
Disciplina 006.3
Collana Energy Systems in Electrical Engineering
Soggetto topico Artificial intelligence
Energy policy
Electric power production
Artificial Intelligence
Energy Policy, Economics and Management
Electrical Power Engineering
ISBN 981-9626-65-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Artificial Intelligence Techniques for Energy Conversion Systems -- An Overview of Artificial Intelligence Solutions for the Maintenance and Evaluation of Photovoltaic Systems -- Topologies and Control Technologies of Wind Energy Conversion System -- A Robust DDTC Scheme based on Artificial Neural Networks and Genetic Algorithm for Wind Power Generation -- Intelligent Control of DFIG Based Wind Energy Conversion Systems using Artificial Intelligence Techniques.
Record Nr. UNINA-9910999696103321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui