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Biblioteca

MARC Lista (tabellare)
Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities
Artificial and Cognitive Computing for Sustainable Healthcare Systems in Smart Cities
Autore Pradhan Devasis
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (278 pages)
Altri autori (Persone) SahuPrasanna Kumar
TunHla Myo
ChatterjeePrasenjit
ISBN 1-394-29744-0
1-394-29742-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Introduction -- Chapter 1 Artificial Intelligence and its Application in Healthcare Systems -- 1.1. History of healthcare system -- 1.2. Literature studies -- 1.3. Evolution of AI -- 1.3.1. Advantages and disadvantages -- 1.3.2. Components of AI -- 1.4. Machine learning -- 1.4.1. Categories of ML -- 1.4.2. Supervised learning -- 1.4.3. Unsupervised learning -- 1.4.4. Reinforcement learning -- 1.5. Application of ML -- 1.6. Application of AI in healthcare -- 1.6.1. Digital health -- 1.6.2. Genetic solutions -- 1.6.3. Bio-medical visualization -- 1.7. Conclusion -- 1.8. References -- Chapter 2 Medical Laboratory Artificial Intelligence: The Applicability in Nigerian Medical Laboratories -- 2.1. Introduction -- 2.2. Historical trend of artificial intelligence (AI) -- 2.3. AI in medical science/medical laboratory science in history -- 2.4. Medical Laboratory Information Management System, centralized data and WWW -- 2.5. Artificial intelligence methodologies and their application in medical laboratory science -- 2.6. Nigerian medical laboratory intelligence before, now and future -- 2.7. Medical laboratory services where AI is used in Nigeria -- 2.8. AI and Internet of medical laboratory things -- 2.9. Opportunities and challenges of AI for Nigerian medical laboratories -- 2.10. Risks/limitations and challenges associated with AI in Nigerian medical laboratories -- 2.11. AI and digitalization of Nigerian medical laboratories -- 2.12. Conclusion -- 2.13. References -- Chapter 3 Machine Learning and Deep Learning for Smart City Services -- 3.1. Introduction -- 3.2. Basics of machine learning and its implications in smart cities -- 3.3. Basics of deep learning and its implications in smart cities -- 3.4. Algorithms of machine learning and deep learning -- 3.4.1. Classification.
3.4.2. Clustering -- 3.4.3. K-nearest neighbors -- 3.4.4. Naive Bayes -- 3.4.5. The support vector machine -- 3.4.6. Linear regression -- 3.4.7. Random forest -- 3.4.8. K-means -- 3.4.9. Artificial neural network -- 3.4.10. Multi-layer perceptron -- 3.5. Applications in smart cities using machine learning and deep learning -- 3.5.1. Safety of the public -- 3.5.2. Intelligent traffic management -- 3.5.3. Water management strategy -- 3.5.4. Smart street lights -- 3.5.5. Intelligent parking devices -- 3.5.6. Smart waste and disposal management system -- 3.6. Future challenges and research directions -- 3.7. Conclusion -- 3.8. References -- Chapter 4 An Intelligent Healthcare System Based on Machine Learning Models for Accurate Detection of Heart Disease -- 4.1. Introduction -- 4.2. Literature survey -- 4.3. Features of the dataset -- 4.4. Proposed system -- 4.5. ML models used for the experimental work -- 4.5.1. Support vector machines (SVM) -- 4.5.2. Random forest (RF) -- 4.5.3. Stochastic gradient descent (SGD) method -- 4.5.4. Multilayer perceptron (MLP) -- 4.5.5. Naive Bayes (NB) -- 4.6. Performance parameters of ML models -- 4.6.1. Confusion matrix -- 4.6.2. Accuracy -- 4.6.3. F1-score -- 4.6.4. AUC -- 4.7. Result and analysis -- 4.8. Conclusion -- 4.9. References -- Chapter 5 3D Volume Rendering of MRI Images for Tumor Detection and Segmentation using nnUnet -- 5.1. Introduction -- 5.2. Methodology -- 5.2.1. Segmentation -- 5.2.2. Overlapping the scans -- 5.2.3. 3D reconstruction -- 5.3. Results and discussion -- 5.4. Conclusion and future scope -- 5.5. References -- Chapter 6 Implementation of Key Generation in Kyber for Post-Quantum Cryptography using VIVADO -- 6.1. Introduction -- 6.2. Methodology -- 6.2.1. RTL design -- 6.2.2. True random number generator and (SHA-512) HASH -- 6.2.3. Pseudo random number generator.
6.2.4. Numeric theoretic transform -- 6.2.5. Matrix multiplication -- 6.3. Results and discussion -- 6.3.1. Design outcomes -- 6.3.2. Board specifications -- 6.3.3. Interfacing with processor core using AXI -- 6.3.4. Area utilization -- 6.3.5. Power utilization -- 6.3.6. Timing utilization -- 6.4. Conclusion and future scope -- 6.5. References -- Chapter 7 Computational Intelligence and Big Data Analytics for Smart Healthcare: A Comprehensive Study -- 7.1. Introduction -- 7.1.1. Computational intelligence in smart healthcare -- 7.1.2. Big Data analytics in smart healthcare -- 7.1.3. Challenges and future directions -- 7.1.4. The landscape of smart healthcare -- 7.2. Computational intelligence techniques in healthcare -- 7.2.1. Machine learning -- 7.2.2. Natural language processing (NLP) -- 7.2.3. Expert systems -- 7.2.4. Robotic process automation (RPA) -- 7.2.5. Physical robots -- 7.3. Applications of intelligence and Big Data analytics in healthcare -- 7.4. Benefits of computational intelligence and Big Data analytics in healthcare -- 7.5. Challenges in implementing computational intelligence and Big Data analytics -- 7.6. Future aspect of computational intelligence and Big Data analytics in smart healthcare -- 7.7. Conclusion -- 7.8. References -- Chapter 8 Bioinformatics, Healthcare Informatics and Analytics: An Imperative for Improved Healthcare System -- 8.1. Introduction -- 8.1.1. Justification for computational biology -- 8.1.2. Computational biology and its impact -- 8.2. Healthcare informatics -- 8.3. Health analytic -- 8.3.1. Healthcare analytics and its contribution to healthcare framework -- 8.4. The intersection amidst bioinformatics, healthcare informatics and analytics -- 8.5. Future prospects of healthcare informatics and analytics -- 8.6. Conclusion -- 8.7. References.
Chapter 9 Natural Language Processing in Healthcare: A Systematic Review -- 9.1. Introduction -- 9.2. Materials and methods -- 9.3. Data sources and searches strategy -- 9.3.1. Requirements for inclusion -- 9.3.2. Exclusion standards -- 9.3.3. Study selection -- 9.3.4. Data extraction and synthesis -- 9.4. Results and discussion -- 9.5. Conclusion -- 9.6. References -- Chapter 10 Artificial Intelligence and Large Language Models in Mental Healthcare: A Systematic Review -- 10.1. Introduction -- 10.1.1. Objectives -- 10.1.2. Methods -- 10.1.3. Inclusion criteria -- 10.1.4. Exclusion criteria -- 10.1.5. Discussion -- 10.2. AI as an advantage for users -- 10.3. Ethical implications of AI -- 10.4. AI chatbot and its functions in diagnosing and intervention -- 10.5. Machine learning as a base of AI for mental healthcare -- 10.6. Forms of AI as a mental healthcare support mechanism -- 10.7. AI as a support for mental health professional -- 10.7.1. Implication -- 10.7.2. Limitations -- 10.8. Suggestions for future studies -- 10.9. Conclusion -- 10.10. Appendix -- 10.11. References -- Chapter 11 Unleashing the Future: Exploring the Transformative Potential of 5G Technology in Healthcare -- 11.1. Introduction to 5G technology -- 11.2. Definition of 5G -- 11.3. History of 5G evolution -- 11.3.1. 1G to 3G (1980s-2000s) -- 11.3.2. 4G and LTE (late 2000s-early 2010s) -- 11.3.3. The path to 5G (2010s) -- 11.3.4. Key milestones towards 5G (2016-2019) -- 11.3.5. 5G deployment and expansion (2020s) -- 11.4. 5G bands -- 11.5. 5G use cases and spectrum band relevance -- 11.6. 5G for industries -- 11.7. Importance -- 11.7.1. Faster data speeds -- 11.7.2. Ultra-low latency -- 11.7.3. Massive device connectivity -- 11.7.4. Industrial automation and IoT -- 11.7.5. Healthcare advancements -- 11.7.6. Autonomous vehicles -- 11.7.7. Smart cities.
11.7.8. Remote work and education -- 11.7.9. Entertainment and media -- 11.7.10. Economic growth and innovation -- 11.7.11. Global competitiveness -- 11.8. Key features of 5G -- 11.9. Intel 5G technologies and solutions -- 11.10. Healthcare -- 11.11. 5G technology's impact on healthcare: a comprehensive overview -- 11.12. Conclusion -- 11.13. References -- List of Authors -- Index -- Other titles from ISTE in Computer Engineering -- EULA.
Record Nr. UNINA-9910857796903321
Pradhan Devasis  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain Applications in Healthcare : Innovations and Practices
Blockchain Applications in Healthcare : Innovations and Practices
Autore Choudhury Tanupriya
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (254 pages)
Altri autori (Persone) KhannaAbhirup
ChatterjeePrasenjit
UmJung-Sup
BhattacharyaAbhishek
ISBN 1-394-22951-8
1-394-22949-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1. Framework for Blockchain in Healthcare -- 1.1. Concept of Blockchain -- 1.2. Blockchain as distributed database -- 1.3. Architecture of Blockchain in healthcare -- 1.4. Development of Blockchain: A state of art -- 1.5. Information distribution in Blockchain -- 1.6. The growing anticipation of Blockchain -- 1.6.1. Challenges faced by Blockchain -- 1.7. The benefits of Blockchain in healthcare -- 1.8. Open issues related to Blockchain -- 1.9. Future trends of Blockchain -- 1.10. References -- Chapter 2. Role of Smart Contracts in Blockchain -- 2.1. Introduction to Blockchain -- 2.1.1. Types of Blockchain -- 2.1.2. Characteristics of Blockchain -- 2.2. Smart contracts -- 2.2.1. Operating mechanism of smart contracts -- 2.2.2. Applications of smart contracts -- 2.2.3. Programming languages and platforms -- 2.3. Quantitative analysis -- 2.3.1. Results -- 2.4. Role of smart contracts in healthcare -- 2.4.1. Health Insurance -- 2.4.2. Healthcare -- 2.4.3. Telemedicine -- 2.5. Example of smart contracts -- 2.5.1. Simple open auction -- 2.5.2. Voting -- 2.5.3. Patient record -- 2.6. Challenges related to smart contracts -- 2.6.1. Contract vulnerabilities -- 2.6.2. Privacy and legal issues -- 2.6.3. Immutability issue -- 2.7. Conclusion -- 2.8. References -- Chapter 3. Blockchain-based Platforms for the Healthcare Industry -- 3.1. Introduction -- 3.2. Literature review -- 3.3. Blockchain technology -- 3.3.1. Uses of Blockchain in the healthcare sector -- 3.4. Blockchain applications that can be useful for treating the medical sector problems -- 3.4.1. Smart contracts -- 3.4.2. Fraud detection -- 3.4.3. Identity verification -- 3.5. Examples of healthcare platforms using Blockchain -- 3.5.1. Data sharing using Gem Health Network -- 3.5.2. MeDshare.
3.5.3. OmniPHR -- 3.6. Blockchain during the Covid-19 pandemic -- 3.7. Conclusion -- 3.8. References -- Chapter 4. Analyzing and Modeling the Challenges Faced by the Healthcare Sector in the Adoption Process of Blockchain Technologies -- 4.1. Introduction -- 4.2. Literature review -- 4.2.1. Blockchain in healthcare -- 4.3. Challenges of Blockchain in healthcare -- 4.3.1. Technical challenges (TC) -- 4.3.2. Social challenges (SC) -- 4.3.3. Organizational challenges (OC) -- 4.4. Research methodology -- 4.5. Data analysis -- 4.6. Discussion -- 4.7. Conclusion -- 4.8. References -- Chapter 5. Blockchain as an Effective Technology in Maintaining Electronic Health Record Systems -- 5.1. Introduction -- 5.2. Background concepts on Blockchain technology -- 5.2.1. Consensus algorithms -- 5.2.2. Types of Blockchain -- 5.2.3. Smart contracts -- 5.2.4. Features of Blockchain -- 5.2.5. Applications of Blockchain technology -- 5.3. Blockchain in healthcare -- 5.4. Electronic health records using Blockchain -- 5.5. Quantitative analysis -- 5.5.1. Results -- 5.6. Proposed framework for the EHRs using Blockchain -- 5.6.1. System workflow -- 5.7. Issues in Blockchain-based EHRs -- 5.8. Case studies -- 5.8.1. MedRec -- 5.8.2. AI-based solution for EHRs -- 5.8.3. Improving medical record keeping with Blockchain -- 5.9. Conclusion -- 5.10. References -- Chapter 6. An Optimistic Approach to Share Private Health Records Using Blockchain Technology -- 6.1. Introduction -- 6.2. Related work -- 6.2.1. Process of storing larger healthcare data -- 6.3. Blockchain-based EHR system -- 6.3.1. Sharing of data -- 6.3.2. Interoperability -- 6.3.3. A distributed network -- 6.3.4. Shared ledger -- 6.3.5. Digital transactions -- 6.4. Blockchain in healthcare -- 6.4.1. On-chain storage -- 6.4.2. Off-chain storage -- 6.4.3. Trust issues in the context of health information exchange (HIE).
6.5. Conclusion and future scope -- 6.6. References -- Chapter 7. Patient Data Privacy Using Blockchain -- 7.1. Introduction -- 7.2. Threat modeling - digitalization in the healthcare industry -- 7.2.1. Data flow diagram (DFD) -- 7.2.2. Threat analysis -- 7.3. Privacy versus security -- 7.3.1. Privacy in Blockchain -- 7.3.2. Process flow -- 7.4. Regulatory compliance requirements -- 7.4.1. HIPAA, HITRUST, HITECH and GDPR -- 7.4.2. Blockchain as a savior -- 7.5. Differential privacy -- 7.5.1. Local differential privacy versus global differential privacy -- 7.5.2. Quantification of privacy and mathematical form of differential privacy -- 7.5.3. Advantages of using differential privacy in Blockchain -- 7.6. Privacy by Design -- 7.7. Conclusion -- 7.8. References -- Chapter 8. Decentralized Smart Healthcare Systems Using Blockchain and AI -- 8.1. Introduction to the healthcare system -- 8.1.1. Introduction to AI -- 8.1.2. Introduction to Blockchain -- 8.2. Use of AI in healthcare systems -- 8.3. Use of Blockchain in healthcare systems -- 8.4. History of medical care -- 8.4.1. Health claims -- 8.4.2. Interoperability -- 8.4.3. Exposure to healthcare -- 8.4.4. Supply chains -- 8.5. Literature review -- 8.6. Bringing intelligence to medical devices and machines -- 8.7. Using artificial intelligence to transform clinical decision-making in hospitals -- 8.7.1. Advantages of Blockchain in healthcare systems -- 8.8. Results of existing models -- 8.9. Conclusion -- 8.10. References -- Chapter 9. Component-based Healthcare Software Application Using Blockchain -- 9.1. Introduction -- 9.2. Literature review -- 9.3. Software development models -- 9.3.1. Traditional software development methodologies -- 9.3.2. Modern software development methodologies -- 9.4. Proposed model -- 9.4.1. Component-based software development life-cycle.
9.4.2. Component development life-cycle -- 9.5. Comparison among different software development life-cycle models -- 9.6. Conclusion and future works -- 9.7. References -- Chapter 10. The Role of Smart Contracts and Blockchain Technology in Healthcare and Other Use Cases -- 10.1. Introduction -- 10.1.1. Comparison between traditional contracts and smart contracts -- 10.2. Ethereum: Generation Two of Blockchain technology -- 10.2.1. History of Ethereum -- 10.3. Smart contracts -- 10.3.1. How smart contracts work -- 10.3.2. Benefits of smart contracts -- 10.3.3. Roles of smart contracts -- 10.4. Use of smart contracts in healthcare, patient monitoring, and other use cases -- 10.4.1. Transparency in supply chain -- 10.4.2. Electronic health records on the Blockchain -- 10.4.3. Use of smart contracts for insurance and billing in supply chain management -- 10.4.4. Verification of medical personnel's identity cards -- 10.4.5. IoT security for remote patient monitoring -- 10.5. Building smart contracts on the Ethereum Blockchain -- 10.5.1. Ethereum virtual machine (EVM) -- 10.5.2. Gas -- 10.5.3. Solidity -- 10.6. Real-time use cases of smart contracts -- 10.6.1. Smart contracts and insurance -- 10.6.2. Smart contracts in an electric vehicle -- 10.6.3. Smart contracts in the energy sector -- 10.6.4. Intellectual property rights -- 10.6.5. Stock trading -- 10.7. Six companies using smart contracts in real-world applications -- 10.7.1. Slock.It -- 10.7.2. Fizzy AXA -- 10.7.3. Etherparty -- 10.7.4. Propy -- 10.7.5. Populous -- 10.7.6. PolySwarm -- 10.8. Challenges -- 10.9. Historical attacks and issues with smart contracts -- 10.10. Conclusion -- 10.11. References -- Chapter 11. Healthcare Research Using Blockchain Technology: A Future Perspective -- 11.1. Introduction -- 11.2. Benefits of using Blockchain in the healthcare industry.
11.3. Application of Blockchain in the healthcare industry -- 11.4. Merging of Blockchain with artificial intelligence in healthcare -- 11.5. Drawbacks of using Blockchain in the healthcare industry -- 11.6. Conclusion and future scope -- 11.7. References -- List of Authors -- Index -- EULA.
Record Nr. UNINA-9910830701403321
Choudhury Tanupriya  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Blockchain Applications in Healthcare : Innovations and Practices
Blockchain Applications in Healthcare : Innovations and Practices
Autore Choudhury Tanupriya
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica 1 online resource (254 pages)
Altri autori (Persone) KhannaAbhirup
ChatterjeePrasenjit
UmJung-Sup
BhattacharyaAbhishek
ISBN 1-394-22951-8
1-394-22949-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1. Framework for Blockchain in Healthcare -- 1.1. Concept of Blockchain -- 1.2. Blockchain as distributed database -- 1.3. Architecture of Blockchain in healthcare -- 1.4. Development of Blockchain: A state of art -- 1.5. Information distribution in Blockchain -- 1.6. The growing anticipation of Blockchain -- 1.6.1. Challenges faced by Blockchain -- 1.7. The benefits of Blockchain in healthcare -- 1.8. Open issues related to Blockchain -- 1.9. Future trends of Blockchain -- 1.10. References -- Chapter 2. Role of Smart Contracts in Blockchain -- 2.1. Introduction to Blockchain -- 2.1.1. Types of Blockchain -- 2.1.2. Characteristics of Blockchain -- 2.2. Smart contracts -- 2.2.1. Operating mechanism of smart contracts -- 2.2.2. Applications of smart contracts -- 2.2.3. Programming languages and platforms -- 2.3. Quantitative analysis -- 2.3.1. Results -- 2.4. Role of smart contracts in healthcare -- 2.4.1. Health Insurance -- 2.4.2. Healthcare -- 2.4.3. Telemedicine -- 2.5. Example of smart contracts -- 2.5.1. Simple open auction -- 2.5.2. Voting -- 2.5.3. Patient record -- 2.6. Challenges related to smart contracts -- 2.6.1. Contract vulnerabilities -- 2.6.2. Privacy and legal issues -- 2.6.3. Immutability issue -- 2.7. Conclusion -- 2.8. References -- Chapter 3. Blockchain-based Platforms for the Healthcare Industry -- 3.1. Introduction -- 3.2. Literature review -- 3.3. Blockchain technology -- 3.3.1. Uses of Blockchain in the healthcare sector -- 3.4. Blockchain applications that can be useful for treating the medical sector problems -- 3.4.1. Smart contracts -- 3.4.2. Fraud detection -- 3.4.3. Identity verification -- 3.5. Examples of healthcare platforms using Blockchain -- 3.5.1. Data sharing using Gem Health Network -- 3.5.2. MeDshare.
3.5.3. OmniPHR -- 3.6. Blockchain during the Covid-19 pandemic -- 3.7. Conclusion -- 3.8. References -- Chapter 4. Analyzing and Modeling the Challenges Faced by the Healthcare Sector in the Adoption Process of Blockchain Technologies -- 4.1. Introduction -- 4.2. Literature review -- 4.2.1. Blockchain in healthcare -- 4.3. Challenges of Blockchain in healthcare -- 4.3.1. Technical challenges (TC) -- 4.3.2. Social challenges (SC) -- 4.3.3. Organizational challenges (OC) -- 4.4. Research methodology -- 4.5. Data analysis -- 4.6. Discussion -- 4.7. Conclusion -- 4.8. References -- Chapter 5. Blockchain as an Effective Technology in Maintaining Electronic Health Record Systems -- 5.1. Introduction -- 5.2. Background concepts on Blockchain technology -- 5.2.1. Consensus algorithms -- 5.2.2. Types of Blockchain -- 5.2.3. Smart contracts -- 5.2.4. Features of Blockchain -- 5.2.5. Applications of Blockchain technology -- 5.3. Blockchain in healthcare -- 5.4. Electronic health records using Blockchain -- 5.5. Quantitative analysis -- 5.5.1. Results -- 5.6. Proposed framework for the EHRs using Blockchain -- 5.6.1. System workflow -- 5.7. Issues in Blockchain-based EHRs -- 5.8. Case studies -- 5.8.1. MedRec -- 5.8.2. AI-based solution for EHRs -- 5.8.3. Improving medical record keeping with Blockchain -- 5.9. Conclusion -- 5.10. References -- Chapter 6. An Optimistic Approach to Share Private Health Records Using Blockchain Technology -- 6.1. Introduction -- 6.2. Related work -- 6.2.1. Process of storing larger healthcare data -- 6.3. Blockchain-based EHR system -- 6.3.1. Sharing of data -- 6.3.2. Interoperability -- 6.3.3. A distributed network -- 6.3.4. Shared ledger -- 6.3.5. Digital transactions -- 6.4. Blockchain in healthcare -- 6.4.1. On-chain storage -- 6.4.2. Off-chain storage -- 6.4.3. Trust issues in the context of health information exchange (HIE).
6.5. Conclusion and future scope -- 6.6. References -- Chapter 7. Patient Data Privacy Using Blockchain -- 7.1. Introduction -- 7.2. Threat modeling - digitalization in the healthcare industry -- 7.2.1. Data flow diagram (DFD) -- 7.2.2. Threat analysis -- 7.3. Privacy versus security -- 7.3.1. Privacy in Blockchain -- 7.3.2. Process flow -- 7.4. Regulatory compliance requirements -- 7.4.1. HIPAA, HITRUST, HITECH and GDPR -- 7.4.2. Blockchain as a savior -- 7.5. Differential privacy -- 7.5.1. Local differential privacy versus global differential privacy -- 7.5.2. Quantification of privacy and mathematical form of differential privacy -- 7.5.3. Advantages of using differential privacy in Blockchain -- 7.6. Privacy by Design -- 7.7. Conclusion -- 7.8. References -- Chapter 8. Decentralized Smart Healthcare Systems Using Blockchain and AI -- 8.1. Introduction to the healthcare system -- 8.1.1. Introduction to AI -- 8.1.2. Introduction to Blockchain -- 8.2. Use of AI in healthcare systems -- 8.3. Use of Blockchain in healthcare systems -- 8.4. History of medical care -- 8.4.1. Health claims -- 8.4.2. Interoperability -- 8.4.3. Exposure to healthcare -- 8.4.4. Supply chains -- 8.5. Literature review -- 8.6. Bringing intelligence to medical devices and machines -- 8.7. Using artificial intelligence to transform clinical decision-making in hospitals -- 8.7.1. Advantages of Blockchain in healthcare systems -- 8.8. Results of existing models -- 8.9. Conclusion -- 8.10. References -- Chapter 9. Component-based Healthcare Software Application Using Blockchain -- 9.1. Introduction -- 9.2. Literature review -- 9.3. Software development models -- 9.3.1. Traditional software development methodologies -- 9.3.2. Modern software development methodologies -- 9.4. Proposed model -- 9.4.1. Component-based software development life-cycle.
9.4.2. Component development life-cycle -- 9.5. Comparison among different software development life-cycle models -- 9.6. Conclusion and future works -- 9.7. References -- Chapter 10. The Role of Smart Contracts and Blockchain Technology in Healthcare and Other Use Cases -- 10.1. Introduction -- 10.1.1. Comparison between traditional contracts and smart contracts -- 10.2. Ethereum: Generation Two of Blockchain technology -- 10.2.1. History of Ethereum -- 10.3. Smart contracts -- 10.3.1. How smart contracts work -- 10.3.2. Benefits of smart contracts -- 10.3.3. Roles of smart contracts -- 10.4. Use of smart contracts in healthcare, patient monitoring, and other use cases -- 10.4.1. Transparency in supply chain -- 10.4.2. Electronic health records on the Blockchain -- 10.4.3. Use of smart contracts for insurance and billing in supply chain management -- 10.4.4. Verification of medical personnel's identity cards -- 10.4.5. IoT security for remote patient monitoring -- 10.5. Building smart contracts on the Ethereum Blockchain -- 10.5.1. Ethereum virtual machine (EVM) -- 10.5.2. Gas -- 10.5.3. Solidity -- 10.6. Real-time use cases of smart contracts -- 10.6.1. Smart contracts and insurance -- 10.6.2. Smart contracts in an electric vehicle -- 10.6.3. Smart contracts in the energy sector -- 10.6.4. Intellectual property rights -- 10.6.5. Stock trading -- 10.7. Six companies using smart contracts in real-world applications -- 10.7.1. Slock.It -- 10.7.2. Fizzy AXA -- 10.7.3. Etherparty -- 10.7.4. Propy -- 10.7.5. Populous -- 10.7.6. PolySwarm -- 10.8. Challenges -- 10.9. Historical attacks and issues with smart contracts -- 10.10. Conclusion -- 10.11. References -- Chapter 11. Healthcare Research Using Blockchain Technology: A Future Perspective -- 11.1. Introduction -- 11.2. Benefits of using Blockchain in the healthcare industry.
11.3. Application of Blockchain in the healthcare industry -- 11.4. Merging of Blockchain with artificial intelligence in healthcare -- 11.5. Drawbacks of using Blockchain in the healthcare industry -- 11.6. Conclusion and future scope -- 11.7. References -- List of Authors -- Index -- EULA.
Record Nr. UNINA-9910841001003321
Choudhury Tanupriya  
Newark : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy Computing in Data Science : Applications and Challenges
Fuzzy Computing in Data Science : Applications and Challenges
Autore Mohanty Sachi Nandan
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (363 pages)
Altri autori (Persone) ChatterjeePrasenjit
HungBui Thanh
Collana Smart and Sustainable Intelligent Systems Ser.
Soggetto genere / forma Electronic books.
ISBN 1-394-15688-X
1-394-15687-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Chapter 1 Band Reduction of HSI Segmentation Using FCM -- 1.1 Introduction -- 1.2 Existing Method -- 1.2.1 K-Means Clustering Method -- 1.2.2 Fuzzy C-Means -- 1.2.3 Davies Bouldin Index -- 1.2.4 Data Set Description of HSI -- 1.3 Proposed Method -- 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid -- 1.3.2 Band Reduction Using K-Means Algorithm -- 1.3.3 Band Reduction Using Fuzzy C-Means -- 1.4 Experimental Results -- 1.4.1 DB Index Graph -- 1.4.2 K-Means-Based PSC (EEOC) -- 1.4.3 Fuzzy C-Means-Based PSC (EEOC) -- 1.5 Analysis of Results -- 1.6 Conclusions -- References -- Chapter 2 A Fuzzy Approach to Face Mask Detection -- 2.1 Introduction -- 2.2 Existing Work -- 2.3 The Proposed Framework -- 2.4 Set-Up and Libraries Used -- 2.5 Implementation -- 2.6 Results and Analysis -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Application of Fuzzy Logic to the Healthcare Industry -- 3.1 Introduction -- 3.2 Background -- 3.3 Fuzzy Logic -- 3.4 Fuzzy Logic in Healthcare -- 3.5 Conclusions -- References -- Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database -- 4.1 Introduction -- 4.2 Data Extraction and Interpretation -- 4.3 Results and Discussion -- 4.3.1 Per Year Publication and Citation Count -- 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic -- 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas -- 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles -- 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis -- 4.3.6 Coauthorship of Authors -- 4.3.7 Cocitation Analysis of Cited Authors -- 4.3.8 Cooccurrence of Author Keywords.
4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries -- 4.4.1 Bibliographic Coupling of Documents -- 4.4.2 Bibliographic Coupling of Sources -- 4.4.3 Bibliographic Coupling of Authors -- 4.4.4 Bibliographic Coupling of Countries -- 4.5 Conclusion -- References -- Chapter 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling -- 5.1 Introduction -- 5.2 History of Fuzzy Logic and Its Applications -- 5.3 Approximate Reasoning -- 5.4 Fuzzy Sets vs Classical Sets -- 5.5 Fuzzy Inference System -- 5.5.1 Characteristics of FIS -- 5.5.2 Working of FIS -- 5.5.3 Methods of FIS -- 5.6 Fuzzy Decision Trees -- 5.6.1 Characteristics of Decision Trees -- 5.6.2 Construction of Fuzzy Decision Trees -- 5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment -- 5.8 Conclusion -- References -- Chapter 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model -- 6.1 Introduction -- 6.1.1 Aim and Scope -- 6.1.2 R-Tool -- 6.1.3 Application of Fuzzy Logic -- 6.1.4 Dataset -- 6.2 Model Study -- 6.2.1 Introduction to Machine Learning Method -- 6.2.2 Time Series Analysis -- 6.2.3 Components of a Time Series -- 6.2.4 Concepts of Stationary -- 6.2.5 Model Parsimony -- 6.3 Methodology -- 6.3.1 Exploratory Data Analysis -- 6.3.1.1 Seed Types-Analysis -- 6.3.1.2 Comparison of Location and Seeds -- 6.3.1.3 Comparison of Season (Month) and Seeds -- 6.3.2 Forecasting -- 6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) -- 6.3.2.2 Data Visualization -- 6.3.2.3 Implementation Model -- 6.4 Result Analysis -- 6.5 Conclusion -- References -- Chapter 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach -- 7.1 Introduction -- 7.1.1 Objectives -- 7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculations.
7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculation Method -- 7.3 Application to Industrial Problems -- 7.3.1 Cutting Fluid Selection Problem -- 7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem -- 7.3.3 FMS Selection Problem -- 7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection -- 7.4 Conclusions -- References -- Chapter 8 Fuzzy Decision Making: Concept and Models -- 8.1 Introduction -- 8.2 Classical Set -- 8.3 Fuzzy Set -- 8.4 Properties of Fuzzy Set -- 8.5 Types of Decision Making -- 8.5.1 Individual Decision Making -- 8.5.2 Multiperson Decision Making -- 8.5.3 Multistage Decision Making -- 8.5.4 Multicriteria Decision Making -- 8.6 Methods of Multiattribute Decision Making (MADM) -- 8.6.1 Weighted Sum Method (WSM) -- 8.6.2 Weighted Product Method (WPM) -- 8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) -- 8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) -- 8.7 Applications of Fuzzy Logic -- 8.8 Conclusion -- References -- Chapter 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) -- 9.1 Introduction -- 9.2 Objectives and Methodology -- 9.2.1 Objectives -- 9.2.2 Methodology -- 9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants -- 9.3.1 Psychological Variables Identified -- 9.3.2 Fuzzy Logic for Solace to Migrants -- 9.4 Findings -- 9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid -- 9.6 Conclusion -- References -- Chapter 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow -- 10.1 Significance of Machine Learning in Healthcare -- 10.2 Cloud-Based Artificial Intelligent Secure Models -- 10.3 Applications and Usage of Machine Learning in Healthcare.
10.3.1 Detecting Diseases and Diagnosis -- 10.3.2 Drug Detection and Manufacturing -- 10.3.3 Medical Imaging Analysis and Diagnosis -- 10.3.4 Personalized/Adapted Medicine -- 10.3.5 Behavioral Modification -- 10.3.6 Maintenance of Smart Health Data -- 10.3.7 Clinical Trial and Study -- 10.3.8 Crowdsourced Information Discovery -- 10.3.9 Enhanced Radiotherapy -- 10.3.10 Outbreak/Epidemic Prediction -- 10.4 Edge AI: For Smart Transformation of Healthcare -- 10.4.1 Role of Edge in Reshaping Healthcare -- 10.4.2 How AI Powers the Edge -- 10.5 Edge AI-Modernizing Human Machine Interface -- 10.5.1 Rural Medicine -- 10.5.2 Autonomous Monitoring of Hospital Rooms-A Case Study -- 10.6 Significance of Fuzzy in Healthcare -- 10.6.1 Fuzzy Logic-Outline -- 10.6.2 Fuzzy Logic-Based Smart Healthcare -- 10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems -- 10.6.4 Applications of Fuzzy Logic in Healthcare -- 10.7 Conclusion and Discussions -- References -- Chapter 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS -- 11.1 Introduction -- 11.2 Video Conferencing Software and Its Major Features -- 11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes -- 11.3 Fuzzy TOPSIS -- 11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS -- 11.4 Sample Numerical Illustration -- 11.5 Conclusions -- References -- Chapter 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming -- 12.1 Introduction -- 12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming -- 12.2 Research Model -- 12.2.1 Average Growth Rate Calculation -- 12.3 Result and Discussion -- 12.4 Conclusion -- References -- Chapter 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment -- 13.1 Introduction -- 13.2 Proposed Algorithm.
13.3 An Illustrative Example on Ergonomic Design Evaluation -- 13.4 Conclusions -- References -- Chapter 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic -- 14.1 Introduction -- 14.2 Control Approach in Wave Energy Systems -- 14.3 Related Work -- 14.4 Mathematical Modeling for Energy Conversion from Ocean Waves -- 14.5 Proposed Methodology -- 14.5.1 Wave Parameters -- 14.5.2 Fuzzy-Optimizer -- 14.6 Conclusion -- References -- Chapter 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Methodology -- 15.3.1 Steps of the mFS TOPSIS -- 15.4 Case Study -- 15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method -- 15.4.2 Effect of Shannon's Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method -- 15.5 Results and Discussions -- 15.5.1 Result Validation -- 15.6 Conclusions and Future Scope -- References -- Chapter 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology -- 16.1 Introduction -- 16.2 MCDM Techniques -- 16.2.1 FAHP -- 16.2.2 Entropy Method as Weights (Influence) Evaluation Technique -- 16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches -- 16.3.1 TOPSIS -- 16.3.2 FMOORA Method -- 16.3.3 FVIKOR -- 16.3.4 Fuzzy Grey Theory (FGT) -- 16.3.5 COPRAS -G -- 16.3.6 Super Hybrid Algorithm -- 16.4 Illustrative Example -- 16.5 Results and Discussions -- 16.5.1 FTOPSIS -- 16.5.2 FMOORA -- 16.5.3 FVIKOR -- 16.5.4 Fuzzy Grey Theory (FGT) -- 16.5.5 COPRAS-G -- 16.5.6 Super Hybrid Approach (SHA) -- 16.6 Conclusions -- References -- Chapter 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Objective of Research -- 17.4 Cluster Analysis -- 17.4.1 Hierarchical Clustering -- 17.4.2 Partitional Clustering -- 17.5 Clustering.
17.6 Methodology.
Record Nr. UNINA-9910632499103321
Mohanty Sachi Nandan  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Fuzzy computing in data science : applications and challenges / / edited by Sachi Nandan Mohanty, Prasenjit Chatterjee and Bui Thanh Hung
Fuzzy computing in data science : applications and challenges / / edited by Sachi Nandan Mohanty, Prasenjit Chatterjee and Bui Thanh Hung
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Descrizione fisica 1 online resource (363 pages)
Disciplina 511.313
Collana Smart and sustainable intelligent systems
Soggetto topico Fuzzy logic
Fuzzy systems
Data mining
ISBN 1-394-15688-X
1-394-15687-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgement -- Chapter 1 Band Reduction of HSI Segmentation Using FCM -- 1.1 Introduction -- 1.2 Existing Method -- 1.2.1 K-Means Clustering Method -- 1.2.2 Fuzzy C-Means -- 1.2.3 Davies Bouldin Index -- 1.2.4 Data Set Description of HSI -- 1.3 Proposed Method -- 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid -- 1.3.2 Band Reduction Using K-Means Algorithm -- 1.3.3 Band Reduction Using Fuzzy C-Means -- 1.4 Experimental Results -- 1.4.1 DB Index Graph -- 1.4.2 K-Means-Based PSC (EEOC) -- 1.4.3 Fuzzy C-Means-Based PSC (EEOC) -- 1.5 Analysis of Results -- 1.6 Conclusions -- References -- Chapter 2 A Fuzzy Approach to Face Mask Detection -- 2.1 Introduction -- 2.2 Existing Work -- 2.3 The Proposed Framework -- 2.4 Set-Up and Libraries Used -- 2.5 Implementation -- 2.6 Results and Analysis -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Application of Fuzzy Logic to the Healthcare Industry -- 3.1 Introduction -- 3.2 Background -- 3.3 Fuzzy Logic -- 3.4 Fuzzy Logic in Healthcare -- 3.5 Conclusions -- References -- Chapter 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database -- 4.1 Introduction -- 4.2 Data Extraction and Interpretation -- 4.3 Results and Discussion -- 4.3.1 Per Year Publication and Citation Count -- 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic -- 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas -- 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles -- 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis -- 4.3.6 Coauthorship of Authors -- 4.3.7 Cocitation Analysis of Cited Authors -- 4.3.8 Cooccurrence of Author Keywords.
4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries -- 4.4.1 Bibliographic Coupling of Documents -- 4.4.2 Bibliographic Coupling of Sources -- 4.4.3 Bibliographic Coupling of Authors -- 4.4.4 Bibliographic Coupling of Countries -- 4.5 Conclusion -- References -- Chapter 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling -- 5.1 Introduction -- 5.2 History of Fuzzy Logic and Its Applications -- 5.3 Approximate Reasoning -- 5.4 Fuzzy Sets vs Classical Sets -- 5.5 Fuzzy Inference System -- 5.5.1 Characteristics of FIS -- 5.5.2 Working of FIS -- 5.5.3 Methods of FIS -- 5.6 Fuzzy Decision Trees -- 5.6.1 Characteristics of Decision Trees -- 5.6.2 Construction of Fuzzy Decision Trees -- 5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment -- 5.8 Conclusion -- References -- Chapter 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model -- 6.1 Introduction -- 6.1.1 Aim and Scope -- 6.1.2 R-Tool -- 6.1.3 Application of Fuzzy Logic -- 6.1.4 Dataset -- 6.2 Model Study -- 6.2.1 Introduction to Machine Learning Method -- 6.2.2 Time Series Analysis -- 6.2.3 Components of a Time Series -- 6.2.4 Concepts of Stationary -- 6.2.5 Model Parsimony -- 6.3 Methodology -- 6.3.1 Exploratory Data Analysis -- 6.3.1.1 Seed Types-Analysis -- 6.3.1.2 Comparison of Location and Seeds -- 6.3.1.3 Comparison of Season (Month) and Seeds -- 6.3.2 Forecasting -- 6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) -- 6.3.2.2 Data Visualization -- 6.3.2.3 Implementation Model -- 6.4 Result Analysis -- 6.5 Conclusion -- References -- Chapter 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach -- 7.1 Introduction -- 7.1.1 Objectives -- 7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculations.
7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon's Entropy Weight Calculation Method -- 7.3 Application to Industrial Problems -- 7.3.1 Cutting Fluid Selection Problem -- 7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem -- 7.3.3 FMS Selection Problem -- 7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection -- 7.4 Conclusions -- References -- Chapter 8 Fuzzy Decision Making: Concept and Models -- 8.1 Introduction -- 8.2 Classical Set -- 8.3 Fuzzy Set -- 8.4 Properties of Fuzzy Set -- 8.5 Types of Decision Making -- 8.5.1 Individual Decision Making -- 8.5.2 Multiperson Decision Making -- 8.5.3 Multistage Decision Making -- 8.5.4 Multicriteria Decision Making -- 8.6 Methods of Multiattribute Decision Making (MADM) -- 8.6.1 Weighted Sum Method (WSM) -- 8.6.2 Weighted Product Method (WPM) -- 8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) -- 8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) -- 8.7 Applications of Fuzzy Logic -- 8.8 Conclusion -- References -- Chapter 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) -- 9.1 Introduction -- 9.2 Objectives and Methodology -- 9.2.1 Objectives -- 9.2.2 Methodology -- 9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants -- 9.3.1 Psychological Variables Identified -- 9.3.2 Fuzzy Logic for Solace to Migrants -- 9.4 Findings -- 9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid -- 9.6 Conclusion -- References -- Chapter 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow -- 10.1 Significance of Machine Learning in Healthcare -- 10.2 Cloud-Based Artificial Intelligent Secure Models -- 10.3 Applications and Usage of Machine Learning in Healthcare.
10.3.1 Detecting Diseases and Diagnosis -- 10.3.2 Drug Detection and Manufacturing -- 10.3.3 Medical Imaging Analysis and Diagnosis -- 10.3.4 Personalized/Adapted Medicine -- 10.3.5 Behavioral Modification -- 10.3.6 Maintenance of Smart Health Data -- 10.3.7 Clinical Trial and Study -- 10.3.8 Crowdsourced Information Discovery -- 10.3.9 Enhanced Radiotherapy -- 10.3.10 Outbreak/Epidemic Prediction -- 10.4 Edge AI: For Smart Transformation of Healthcare -- 10.4.1 Role of Edge in Reshaping Healthcare -- 10.4.2 How AI Powers the Edge -- 10.5 Edge AI-Modernizing Human Machine Interface -- 10.5.1 Rural Medicine -- 10.5.2 Autonomous Monitoring of Hospital Rooms-A Case Study -- 10.6 Significance of Fuzzy in Healthcare -- 10.6.1 Fuzzy Logic-Outline -- 10.6.2 Fuzzy Logic-Based Smart Healthcare -- 10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems -- 10.6.4 Applications of Fuzzy Logic in Healthcare -- 10.7 Conclusion and Discussions -- References -- Chapter 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS -- 11.1 Introduction -- 11.2 Video Conferencing Software and Its Major Features -- 11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes -- 11.3 Fuzzy TOPSIS -- 11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS -- 11.4 Sample Numerical Illustration -- 11.5 Conclusions -- References -- Chapter 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming -- 12.1 Introduction -- 12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming -- 12.2 Research Model -- 12.2.1 Average Growth Rate Calculation -- 12.3 Result and Discussion -- 12.4 Conclusion -- References -- Chapter 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment -- 13.1 Introduction -- 13.2 Proposed Algorithm.
13.3 An Illustrative Example on Ergonomic Design Evaluation -- 13.4 Conclusions -- References -- Chapter 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic -- 14.1 Introduction -- 14.2 Control Approach in Wave Energy Systems -- 14.3 Related Work -- 14.4 Mathematical Modeling for Energy Conversion from Ocean Waves -- 14.5 Proposed Methodology -- 14.5.1 Wave Parameters -- 14.5.2 Fuzzy-Optimizer -- 14.6 Conclusion -- References -- Chapter 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Methodology -- 15.3.1 Steps of the mFS TOPSIS -- 15.4 Case Study -- 15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method -- 15.4.2 Effect of Shannon's Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method -- 15.5 Results and Discussions -- 15.5.1 Result Validation -- 15.6 Conclusions and Future Scope -- References -- Chapter 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology -- 16.1 Introduction -- 16.2 MCDM Techniques -- 16.2.1 FAHP -- 16.2.2 Entropy Method as Weights (Influence) Evaluation Technique -- 16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches -- 16.3.1 TOPSIS -- 16.3.2 FMOORA Method -- 16.3.3 FVIKOR -- 16.3.4 Fuzzy Grey Theory (FGT) -- 16.3.5 COPRAS -G -- 16.3.6 Super Hybrid Algorithm -- 16.4 Illustrative Example -- 16.5 Results and Discussions -- 16.5.1 FTOPSIS -- 16.5.2 FMOORA -- 16.5.3 FVIKOR -- 16.5.4 Fuzzy Grey Theory (FGT) -- 16.5.5 COPRAS-G -- 16.5.6 Super Hybrid Approach (SHA) -- 16.6 Conclusions -- References -- Chapter 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Objective of Research -- 17.4 Cluster Analysis -- 17.4.1 Hierarchical Clustering -- 17.4.2 Partitional Clustering -- 17.5 Clustering.
17.6 Methodology.
Record Nr. UNINA-9910830507603321
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent decision support systems for smart city applications / / edited by Loveleen Gaur, Vernika Agarwal, and Prasenjit Chatterjee
Intelligent decision support systems for smart city applications / / edited by Loveleen Gaur, Vernika Agarwal, and Prasenjit Chatterjee
Pubbl/distr/stampa Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , 2023
Descrizione fisica xxi, 237 pages : illustrations ; ; 24 cm
Collana Sustainable computing and optimization
Soggetto topico Smart cities
ISBN 1-119-89695-9
1-119-89694-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910831080903321
Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management
Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management
Autore Antuchevi?ien? Jurgita
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (320 p.)
Soggetto non controllato multiple attribute decision making
maximizing deviation model
interval multiplicative preference relations
rough sets
queuing systems
fuzzy EDAS
nonnegative normal neutrosophic number
single-valued linguistic neutrosophic interval linguistic number
order allocation
multi-attribute decision-making (MADM)
multi-criteria decision-making
Pythagorean uncertain linguistic variable
neutrosophic sets
supplier
green supplier
trust interval
ANFIS
reliable group decision-making
multiple criteria decision-making
adaptive neuro-fuzzy inference system (ANFIS)
multi-attribute group decision-making
Pythagorean fuzzy set
Muirhead mean
subcontractor evaluation
fuzzy sets
group decision-making
score function
supplier selection
unbalanced linguistic set
projection model
multiple criteria group decision-making
warehouse
multi-hesitant fuzzy sets
Dombi operations
interaction operational laws
decision making
MCDM
multiple criteria decision making (MCDM)
rough ANP
MADM
multiple attributes decision-making
interactive approach
weighted aggregation operator
logistics
rough analytical hierarchical process (AHP)
linguistic cubic variable
multiobjective optimization
aggregation operators
bi-directional projection model
rough boundary interval
prioritized average operator
binary discernibility matrices
Einstein operations
hesitant probabilistic fuzzy Einstein aggregation operators
multiple-criteria decision-making (MCDM)
aggregation operator
linguistic cubic variable Dombi weighted arithmetic average (LCVDWAA) operator
linguistic cubic variable Dombi weighted geometric average (LCVDWGA) operator
multi-attribute decision making
trapezoidal fuzzy number
rough number
evidence theory
uncertain group decision-making support systems
desirability function
deterministic finite automata
rough weighted aggregated sum product assessment (WASPAS)
hesitant probabilistic fuzzy element (HPFE)
multiple attribute decision making (MADM)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Multiple-Criteria Decision-Making
Record Nr. UNINA-9910346836503321
Antuchevi?ien? Jurgita  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Smart Edge Computing : An Operation Research Perspective
Smart Edge Computing : An Operation Research Perspective
Autore Chakraborty Rajdeep
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (275 pages)
Altri autori (Persone) GhoshAnupam
MandalJyotsna Kumar
ChoudhuryTanupriya
ChatterjeePrasenjit
ISBN 1-394-27759-8
1-394-27757-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1. Introduction to Operations Research Methodologies -- 1.1. Introduction -- 1.2. Decision-making framework/models for operations research -- 1.3. Operations research in IoT, IIoT, edge and smart edge computing, sensor data -- 1.4. Paradigms and procedures -- 1.5. Conclusion -- 1.6. References -- Chapter 2. Edge Computing: The Foundation, Emergence and Growing Applications -- 2.1. Introduction -- 2.2. Objective of the work -- 2.3. Methods adopted -- 2.4. Edge computing and edge cloud: basics -- 2.5. Edge computing and edge devices -- 2.6. Edge computing: working fashions, buying and deploying and 5G -- 2.7. Functions and features of edge computing -- 2.7.1. Privacy and security -- 2.7.2. Scalability -- 2.7.3. Reliability -- 2.7.4. Speed -- 2.7.5. Efficiency -- 2.7.6. Latency and bandwidth -- 2.7.7. Reduction in congestion -- 2.8. Edge computing: applications and examples -- 2.8.1. Self-managed and automated cars/vehicles -- 2.8.2. Fleet management -- 2.8.3. Predictive maintenance -- 2.8.4. Voice assisting systems -- 2.8.5. Smart cities and town planning -- 2.8.6. Manufacturing and core sector -- 2.8.7. Healthcare and medical segment -- 2.8.8. Edge computing and augmented reality -- 2.9. Drawbacks, obstacles and issues in edge computing -- 2.10. Edge computing, cloud computing and Internet of Things: some concerns -- 2.11. Future and emergence of edge computing -- 2.12. Conclusion -- 2.13. Acknowledgment -- 2.14. References -- Chapter 3. Utilization of Edge Computing in Digital Education: Conceptual Overview -- 3.1. Introduction -- 3.2. Objectives -- 3.3. Methodology used -- 3.4. Digital education -- 3.4.1. Emerging technologies in digital education -- 3.5. Education and information science -- 3.6. Edge computing.
3.6.1. Edge computing promotes education and information science -- 3.6.2. Conceptual overview of edge computing in education -- 3.6.3. Conceptual diagram of edge computing in education -- 3.6.4. Concept of communication between different layers of edge computing in education -- 3.6.5. Diagram of communication between different layers of edge computing in education -- 3.6.6. Stakeholder of edge computing in digital education -- 3.6.7. Advantages of edge computing in digital education -- 3.6.8. Challenges of edge computing in digital education -- 3.7. Conclusion -- 3.8. Acknowledgment -- 3.9. References -- Chapter 4. Edge Computing with Operations Research Using IoT Devices in Healthcare: Concepts, Tools, Techniques and Use Cases -- 4.1. Overview -- 4.2. The smartness of edge across artificial intelligence with the IoT -- 4.2.1. Operations research in edge computing -- 4.2.2. Artificial intelligence and its innovative strategy -- 4.2.3. Machine learning and its potential application -- 4.2.4. Deep learning and its significance -- 4.2.5. Generative adversarial network and healthcare records -- 4.2.6. Natural language processing and its driving factors -- 4.2.7. Cloud-based intelligent edge computing infrastructure -- 4.2.8. Handling security and privacy issues -- 4.3. Promising approaches in edge healthcare system -- 4.3.1. Software adaptable network -- 4.3.2. Self-learning healthcare IoT -- 4.3.3. Towards Big Data in healthcare IoT -- 4.4. Impact of smartphones on edge computing -- 4.4.1. Use in clinical practice -- 4.4.2. Application for healthcare professionals -- 4.4.3. Edge computing in cutting edge devices -- 4.4.4. Robust smartphone using deep learning -- 4.4.5. Smartphone towards healthcare IoT -- 4.5. Tools, techniques and use cases -- 4.5.1. Smart self-monitoring healthcare system -- 4.5.2. Healthcare development tools.
4.5.3. Simple use cases -- 4.6. Significant forthcomings of edge healthcare IoT -- 4.7. Software and hardware companies developing healthcare tools -- 4.8. Summary -- 4.9. References -- Chapter 5. Performance Measures in Edge Computing Using the Queuing Model -- 5.1. Introduction -- 5.2. Methodology -- 5.2.1. Queuing theory on edge computing -- 5.2.2. Result -- 5.3. Conclusion -- 5.4. Future scope -- 5.5. References -- Chapter 6. A Smart Payment Transaction Procedure by Smart Edge Computing -- 6.1. Introduction -- 6.2. Related works -- 6.3. Ethereum -- 6.3.1. Ethereum's four stages of development -- 6.4. Ethereum's components -- 6.4.1. P2P network -- 6.4.2. Consensus rules -- 6.4.3. Transactions -- 6.4.4. State machine -- 6.4.5. Data structures -- 6.4.6. Consensus algorithm -- 6.4.7. Economic security -- 6.4.8. Clients -- 6.5. General-purpose blockchains to decentralized applications (DApps) -- 6.6. Ether currency units -- 6.7. Ethereum wallet -- 6.7.1. MetaMask -- 6.7.2. Jaxx -- 6.7.3. MyEtherWallet (MEW) -- 6.7.4. Emerald Wallet -- 6.8. A simple contract: a test Ether faucet -- 6.9. Ethereum clients -- 6.9.1. Hardware requirements for a full node -- 6.9.2. Advantages and disadvantages of full node -- 6.9.3. The advantages and disadvantages of public testnet -- 6.10. Conclusion -- 6.11. References -- Chapter 7. Statistical Learning Approach for the Detection of Abnormalities in Cancer Cells for Finding Indication of Metastasis -- 7.1. Introduction -- 7.2. Edge computation: a new era -- 7.3. Impact of edge computation in cancer treatment -- 7.4. Assessment parameters operational methodologies -- 7.5. Shape descriptor analysis: statistical approach -- 7.6. Results and discussion -- 7.7. Conclusion -- 7.8. References -- Chapter 8. Overcoming the Stigma of Alzheimer's Disease by Means of Natural Language Processing as well as Blockchain Technologies.
8.1. Introduction -- 8.2. Alzheimer's disease -- 8.3. Alzheimer's disease types -- 8.4. NLP in chat-bots/AI companions -- 8.5. Proposed methodologies for reduction of stigma -- 8.5.1. Proposed methodology using NLP -- 8.5.2. Model objective function of Alzheimer's disease -- 8.6. Blockchain technology for securing all medical data -- 8.6.1. Blockchain strategies for data privacy in healthcare -- 8.6.2. Application of blockchain technologies -- 8.6.3. Blockchain application intended for EHR data management -- 8.6.4. Issues with blockchain security and privacy -- 8.6.5. Challenges faced by blockchain applications -- 8.7. Conclusion -- 8.8. Future scope -- 8.9. Acknowledgments -- 8.10. References -- Chapter 9. Computer Vision-based Edge Computing System to Detect Health Informatics for Oral Pre-Cancer -- 9.1. Introduction -- 9.2. Related works -- 9.3. Materials and methods -- 9.3.1. Microscopic imaging -- 9.3.2. Proposed methodology -- 9.3.3. RGB color segmentation -- 9.4. Results -- 9.5. Conclusion -- 9.6. References -- Chapter 10. A Study of Ultra-lightweight Ciphers and Security Protocol for Edge Computing -- 10.1. Introduction -- 10.1.1. Evolution of the IoT -- 10.1.2. Content of the review work -- 10.2. Ultra-lightweight ciphers -- 10.2.1. SLIM -- 10.2.2. Piccolo -- 10.2.3. Hummingbird -- 10.2.4. Comparison between SLIM, Piccolo and Hummingbird ciphers -- 10.3. Ultra-lightweight security protocols -- 10.3.1. Lightweight extensible authentication protocol (LEAP) -- 10.3.2. MIFARE -- 10.3.3. Remote frame buffer (RFB) -- 10.3.4. Comparison between LEAP, MIFARE and RFB protocols -- 10.4. Conclusion -- 10.5. References -- Chapter 11. A Study on Security Protocols, Threats and Probable Solutions for Internet of Things Using Blockchain -- 11.1. Introduction -- 11.2. IoT architecture and security challenges -- 11.3. Security threat classifications.
11.3.1. Low-level security threats -- 11.3.2. Intermediate-level security threats -- 11.3.3. High-level security threats -- 11.4. Security solutions for IoT -- 11.4.1. Low-level security solutions -- 11.4.2. Intermediate-level security solutions -- 11.4.3. High-level security solutions -- 11.5. Blockchain-based IoT paradigm: security and privacy issues -- 11.5.1. Lack of IoT-centric agreement mechanisms -- 11.5.2. IoT device incorporation -- 11.5.3. Software update -- 11.5.4. Data scalability and organization -- 11.5.5. Interoperability with the varied IoT devices organized lying on blockchain network -- 11.5.6. Perception layer -- 11.5.7. Network layer -- 11.5.8. Processing layer -- 11.5.9. Application layer -- 11.6. IoT Messaging Protocols -- 11.6.1. Hyper Text Transfer Protocol (HTTP) -- 11.6.2. Message Queue Telemetry Protocols (MQTT) -- 11.6.3. Secure MQTT (SMQTT) -- 11.6.4. Advanced Message Queuing Protocol (AMQP) -- 11.6.5. Constrained Application Protocol (CoAP) -- 11.6.6. Extensible Messaging and Presence Protocol (XMPP) -- 11.6.7. Relative study of different messaging protocols of IoT environments -- 11.7. Advantages of edge computing -- 11.8. Conclusion -- 11.9. References -- List of Authors -- Index -- EULA.
Record Nr. UNINA-9910835065703321
Chakraborty Rajdeep
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Smart Edge Computing : An Operation Research Perspective
Smart Edge Computing : An Operation Research Perspective
Autore Chakraborty Rajdeep
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (275 pages)
Disciplina 004.6
Altri autori (Persone) GhoshAnupam
MandalJyotsna Kumar
ChoudhuryTanupriya
ChatterjeePrasenjit
Collana Computer engineering series. International perspectives in decision analysis and operations research set
Soggetto topico Edge computing
Artificial intelligence
ISBN 1-394-27759-8
1-394-27757-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1. Introduction to Operations Research Methodologies -- 1.1. Introduction -- 1.2. Decision-making framework/models for operations research -- 1.3. Operations research in IoT, IIoT, edge and smart edge computing, sensor data -- 1.4. Paradigms and procedures -- 1.5. Conclusion -- 1.6. References -- Chapter 2. Edge Computing: The Foundation, Emergence and Growing Applications -- 2.1. Introduction -- 2.2. Objective of the work -- 2.3. Methods adopted -- 2.4. Edge computing and edge cloud: basics -- 2.5. Edge computing and edge devices -- 2.6. Edge computing: working fashions, buying and deploying and 5G -- 2.7. Functions and features of edge computing -- 2.7.1. Privacy and security -- 2.7.2. Scalability -- 2.7.3. Reliability -- 2.7.4. Speed -- 2.7.5. Efficiency -- 2.7.6. Latency and bandwidth -- 2.7.7. Reduction in congestion -- 2.8. Edge computing: applications and examples -- 2.8.1. Self-managed and automated cars/vehicles -- 2.8.2. Fleet management -- 2.8.3. Predictive maintenance -- 2.8.4. Voice assisting systems -- 2.8.5. Smart cities and town planning -- 2.8.6. Manufacturing and core sector -- 2.8.7. Healthcare and medical segment -- 2.8.8. Edge computing and augmented reality -- 2.9. Drawbacks, obstacles and issues in edge computing -- 2.10. Edge computing, cloud computing and Internet of Things: some concerns -- 2.11. Future and emergence of edge computing -- 2.12. Conclusion -- 2.13. Acknowledgment -- 2.14. References -- Chapter 3. Utilization of Edge Computing in Digital Education: Conceptual Overview -- 3.1. Introduction -- 3.2. Objectives -- 3.3. Methodology used -- 3.4. Digital education -- 3.4.1. Emerging technologies in digital education -- 3.5. Education and information science -- 3.6. Edge computing.
3.6.1. Edge computing promotes education and information science -- 3.6.2. Conceptual overview of edge computing in education -- 3.6.3. Conceptual diagram of edge computing in education -- 3.6.4. Concept of communication between different layers of edge computing in education -- 3.6.5. Diagram of communication between different layers of edge computing in education -- 3.6.6. Stakeholder of edge computing in digital education -- 3.6.7. Advantages of edge computing in digital education -- 3.6.8. Challenges of edge computing in digital education -- 3.7. Conclusion -- 3.8. Acknowledgment -- 3.9. References -- Chapter 4. Edge Computing with Operations Research Using IoT Devices in Healthcare: Concepts, Tools, Techniques and Use Cases -- 4.1. Overview -- 4.2. The smartness of edge across artificial intelligence with the IoT -- 4.2.1. Operations research in edge computing -- 4.2.2. Artificial intelligence and its innovative strategy -- 4.2.3. Machine learning and its potential application -- 4.2.4. Deep learning and its significance -- 4.2.5. Generative adversarial network and healthcare records -- 4.2.6. Natural language processing and its driving factors -- 4.2.7. Cloud-based intelligent edge computing infrastructure -- 4.2.8. Handling security and privacy issues -- 4.3. Promising approaches in edge healthcare system -- 4.3.1. Software adaptable network -- 4.3.2. Self-learning healthcare IoT -- 4.3.3. Towards Big Data in healthcare IoT -- 4.4. Impact of smartphones on edge computing -- 4.4.1. Use in clinical practice -- 4.4.2. Application for healthcare professionals -- 4.4.3. Edge computing in cutting edge devices -- 4.4.4. Robust smartphone using deep learning -- 4.4.5. Smartphone towards healthcare IoT -- 4.5. Tools, techniques and use cases -- 4.5.1. Smart self-monitoring healthcare system -- 4.5.2. Healthcare development tools.
4.5.3. Simple use cases -- 4.6. Significant forthcomings of edge healthcare IoT -- 4.7. Software and hardware companies developing healthcare tools -- 4.8. Summary -- 4.9. References -- Chapter 5. Performance Measures in Edge Computing Using the Queuing Model -- 5.1. Introduction -- 5.2. Methodology -- 5.2.1. Queuing theory on edge computing -- 5.2.2. Result -- 5.3. Conclusion -- 5.4. Future scope -- 5.5. References -- Chapter 6. A Smart Payment Transaction Procedure by Smart Edge Computing -- 6.1. Introduction -- 6.2. Related works -- 6.3. Ethereum -- 6.3.1. Ethereum's four stages of development -- 6.4. Ethereum's components -- 6.4.1. P2P network -- 6.4.2. Consensus rules -- 6.4.3. Transactions -- 6.4.4. State machine -- 6.4.5. Data structures -- 6.4.6. Consensus algorithm -- 6.4.7. Economic security -- 6.4.8. Clients -- 6.5. General-purpose blockchains to decentralized applications (DApps) -- 6.6. Ether currency units -- 6.7. Ethereum wallet -- 6.7.1. MetaMask -- 6.7.2. Jaxx -- 6.7.3. MyEtherWallet (MEW) -- 6.7.4. Emerald Wallet -- 6.8. A simple contract: a test Ether faucet -- 6.9. Ethereum clients -- 6.9.1. Hardware requirements for a full node -- 6.9.2. Advantages and disadvantages of full node -- 6.9.3. The advantages and disadvantages of public testnet -- 6.10. Conclusion -- 6.11. References -- Chapter 7. Statistical Learning Approach for the Detection of Abnormalities in Cancer Cells for Finding Indication of Metastasis -- 7.1. Introduction -- 7.2. Edge computation: a new era -- 7.3. Impact of edge computation in cancer treatment -- 7.4. Assessment parameters operational methodologies -- 7.5. Shape descriptor analysis: statistical approach -- 7.6. Results and discussion -- 7.7. Conclusion -- 7.8. References -- Chapter 8. Overcoming the Stigma of Alzheimer's Disease by Means of Natural Language Processing as well as Blockchain Technologies.
8.1. Introduction -- 8.2. Alzheimer's disease -- 8.3. Alzheimer's disease types -- 8.4. NLP in chat-bots/AI companions -- 8.5. Proposed methodologies for reduction of stigma -- 8.5.1. Proposed methodology using NLP -- 8.5.2. Model objective function of Alzheimer's disease -- 8.6. Blockchain technology for securing all medical data -- 8.6.1. Blockchain strategies for data privacy in healthcare -- 8.6.2. Application of blockchain technologies -- 8.6.3. Blockchain application intended for EHR data management -- 8.6.4. Issues with blockchain security and privacy -- 8.6.5. Challenges faced by blockchain applications -- 8.7. Conclusion -- 8.8. Future scope -- 8.9. Acknowledgments -- 8.10. References -- Chapter 9. Computer Vision-based Edge Computing System to Detect Health Informatics for Oral Pre-Cancer -- 9.1. Introduction -- 9.2. Related works -- 9.3. Materials and methods -- 9.3.1. Microscopic imaging -- 9.3.2. Proposed methodology -- 9.3.3. RGB color segmentation -- 9.4. Results -- 9.5. Conclusion -- 9.6. References -- Chapter 10. A Study of Ultra-lightweight Ciphers and Security Protocol for Edge Computing -- 10.1. Introduction -- 10.1.1. Evolution of the IoT -- 10.1.2. Content of the review work -- 10.2. Ultra-lightweight ciphers -- 10.2.1. SLIM -- 10.2.2. Piccolo -- 10.2.3. Hummingbird -- 10.2.4. Comparison between SLIM, Piccolo and Hummingbird ciphers -- 10.3. Ultra-lightweight security protocols -- 10.3.1. Lightweight extensible authentication protocol (LEAP) -- 10.3.2. MIFARE -- 10.3.3. Remote frame buffer (RFB) -- 10.3.4. Comparison between LEAP, MIFARE and RFB protocols -- 10.4. Conclusion -- 10.5. References -- Chapter 11. A Study on Security Protocols, Threats and Probable Solutions for Internet of Things Using Blockchain -- 11.1. Introduction -- 11.2. IoT architecture and security challenges -- 11.3. Security threat classifications.
11.3.1. Low-level security threats -- 11.3.2. Intermediate-level security threats -- 11.3.3. High-level security threats -- 11.4. Security solutions for IoT -- 11.4.1. Low-level security solutions -- 11.4.2. Intermediate-level security solutions -- 11.4.3. High-level security solutions -- 11.5. Blockchain-based IoT paradigm: security and privacy issues -- 11.5.1. Lack of IoT-centric agreement mechanisms -- 11.5.2. IoT device incorporation -- 11.5.3. Software update -- 11.5.4. Data scalability and organization -- 11.5.5. Interoperability with the varied IoT devices organized lying on blockchain network -- 11.5.6. Perception layer -- 11.5.7. Network layer -- 11.5.8. Processing layer -- 11.5.9. Application layer -- 11.6. IoT Messaging Protocols -- 11.6.1. Hyper Text Transfer Protocol (HTTP) -- 11.6.2. Message Queue Telemetry Protocols (MQTT) -- 11.6.3. Secure MQTT (SMQTT) -- 11.6.4. Advanced Message Queuing Protocol (AMQP) -- 11.6.5. Constrained Application Protocol (CoAP) -- 11.6.6. Extensible Messaging and Presence Protocol (XMPP) -- 11.6.7. Relative study of different messaging protocols of IoT environments -- 11.7. Advantages of edge computing -- 11.8. Conclusion -- 11.9. References -- List of Authors -- Index -- EULA.
Record Nr. UNINA-9910841448803321
Chakraborty Rajdeep  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui