Pubbl/distr/stampa	Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
Descrizione fisica	1 online resource (352 pages)
Disciplina	610.285
Soggetto topico	Artificial intelligence - Medical applications Medical technology
Soggetto genere / forma	Electronic books.
ISBN	1-119-76906-X 1-119-76908-6 1-119-76907-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: MACHINE LEARNING FOR HANDLINGCOVID-19 -- 1 COVID-19 and Machine Learning Approaches to Deal With the Pandemic -- 1.1 Introduction -- 1.1.1 COVID-19 and its Various Transmission Stages Depending Upon the Severity of the Problem -- 1.2 COVID-19 Diagnosis in Patients Using Machine Learning -- 1.2.1 Machine Learning to Identify the People who are at More Risk of COVID-19 -- 1.2.2 Machine Learning to Speed Up Drug Development -- 1.2.3 Machine Learning for Re-Use of Existing Drugs in Treating COVID-19 -- 1.3 AI and Machine Learning as a Support System for Robotic System and Drones -- 1.3.1 AI-Based Location Tracking of COVID-19 Patients -- 1.3.2 Increased Number of Screenings Using AI Approach -- 1.3.3 Artificial Intelligence in Management of Resources During COVID-19 -- 1.3.4 Influence of AI on Manufacturing Industry During COVID-19 -- 1.3.5 Artificial Intelligence and Mental Health in COVID-19 -- 1.3.6 Can AI Replace the Human Brain Intelligence in COVID-19 Crisis? -- 1.3.7 Advantages and Disadvantages of AI in Post COVID Era -- 1.4 Conclusion -- References -- 2 Healthcare System 4.0 Perspectives on COVID-19 Pandemic -- 2.1 Introduction -- 2.2 Key Techniques of HCS 4.0 for COVID-19 -- 2.2.1 Artificial Intelligence (AI) -- 2.2.2 The Internet of Things (IoT) -- 2.2.3 Big Data -- 2.2.4 Virtual Reality (VR) -- 2.2.5 Holography -- 2.2.6 Cloud Computing -- 2.2.7 Autonomous Robots -- 2.2.8 3D Scanning -- 2.2.9 3D Printing Technology -- 2.2.10 Biosensors -- 2.3 Real World Applications of HCS 4.0 for COVID-19 -- 2.4 Opportunities and Limitations -- 2.5 Future Perspectives -- 2.6 Conclusion -- References -- 3 Analysis and Prediction on COVID-19 Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Types of Machine Learning. 3.4 Machine Learning Algorithms -- 3.4.1 Linear Regression -- 3.4.2 Logistic Regression -- 3.4.3 K-NN or K Nearest Neighbor -- 3.4.4 Decision Tree -- 3.4.5 Random Forest -- 3.5 Analysis and Prediction of COVID-19 Data -- 3.5.1 Methodology Adopted -- 3.6 Analysis Using Machine Learning Models -- 3.6.1 Splitting of Data into Training and Testing Data Set -- 3.6.2 Training of Machine Learning Models -- 3.6.3 Calculating the Score -- 3.7 Conclusion & -- Future Scope -- References -- 4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning -- 4.1 Introduction -- 4.2 Effect of COVID-19 on Different Sections of Society -- 4.2.1 Effect of COVID-19 on Mental Health of Elder People -- 4.2.2 Effect of COVID-19 on our Environment -- 4.2.3 Effect of COVID-19 on International Allies and Healthcare -- 4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 -- 4.2.5 Effect of COVID-19 on Labor Migrants -- 4.2.6 Impact of COVID-19 on our Economy -- 4.3 Definition and Types of Machine Learning -- 4.3.1 Machine Learning & -- Its Types -- 4.3.2 Applications of Machine Learning -- 4.4 Machine Learning Approaches for COVID-19 -- 4.4.1 Enabling Organizations to Regulate and Scale -- 4.4.2 Understanding About COVID-19 Infections -- 4.4.3 Gearing Up Study and Finding Treatments -- 4.4.4 Predicting Treatment and Healing Outcomes -- 4.4.5 Testing Patients and Diagnosing COVID-19 -- References -- 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Suggested Methodology -- 5.4 Models in Epidemiology -- 5.4.1 Bayesian Inference Models -- 5.5 Particle Filtering Algorithm -- 5.6 MCM Model Implementation -- 5.6.1 Reproduction Number -- 5.7 Diagnosis of COVID-19 -- 5.7.1 Predicting Outbreaks Through Social Media Analysis -- 5.8 Conclusion -- References. Part 2: EMERGING TECHNOLOGIES TO DEAL WITH COVID-19 -- 6 Emerging Technologies for Handling Pandemic Challenges -- 6.1 Introduction -- 6.2 Technological Strategies to Support Society During the Pandemic -- 6.2.1 Online Shopping and Robot Deliveries -- 6.2.2 Digital and Contactless Payments -- 6.2.3 Remote Work -- 6.2.4 Telehealth -- 6.2.5 Online Entertainment -- 6.2.6 Supply Chain 4.0 -- 6.2.7 3D Printing -- 6.2.8 Rapid Detection -- 6.2.9 QRT-PCR -- 6.2.10 Immunodiagnostic Test (Rapid Antibody Test) -- 6.2.11 Work From Home -- 6.2.12 Distance Learning -- 6.2.13 Surveillance -- 6.3 Feasible Prospective Technologies in Controlling the Pandemic -- 6.3.1 Robotics and Drones -- 6.3.2 5G and Information and Communications Technology (ICT) -- 6.3.3 Portable Applications -- 6.4 Coronavirus Pandemic: Emerging Technologies That Tackle Key Challenges -- 6.4.1 Remote Healthcare -- 6.4.2 Prevention Measures -- 6.4.3 Diagnostic Solutions -- 6.4.4 Hospital Care -- 6.4.5 Public Safety During Pandemic -- 6.4.6 Industry Adapting to the Lockdown -- 6.4.7 Cities Adapting to the Lockdown -- 6.4.8 Individuals Adapting to the Lockdown -- 6.5 The Golden Age of Drone Delivery -- 6.5.1 The Early Adopters are Winning -- 6.5.2 The Golden Age Will Require Collaboration and Drive -- 6.5.3 Standardization and Data Sharing Through the Smart City Network -- 6.5.4 The Procedure of AI and Non-AI-Based Applications -- 6.6 Technology Helps Pandemic Management -- 6.6.1 Tracking People With Facial Recognition and Big Data -- 6.6.2 Contactless Movement and Deliveries Through Autonomous Vehicles, Drones, and Robots -- 6.6.3 Technology Supported Temperature Monitoring -- 6.6.4 Remote Working Technologies to Support Social Distancing and Maintain Business Continuity -- 6.7 Conclusion -- References -- 7 Unfolding the Potential of Impactful Emerging Technologies Amid COVID-19. 7.1 Introduction -- 7.2 Review of Technologies Used During the Outbreak of Ebola and SARS -- 7.2.1 Technological Strategies and Tools Used at the Time of SARS -- 7.2.2 Technological Strategies and Tools Used at the Time of Ebola -- 7.3 Emerging Technological Solutions to Mitigate the COVID-19 Crisis -- 7.3.1 Artificial Intelligence -- 7.3.2 IoT & -- Robotics -- 7.3.3 Telemedicine -- 7.3.4 Innovative Healthcare -- 7.3.5 Nanotechnology -- 7.4 Conclusion -- References -- 8 Advances in Technology: Preparedness for Handling Pandemic Challenges -- 8.1 Introduction -- 8.2 Issues and Challenges Due to Pandemic -- 8.2.1 Health Effect -- 8.2.2 Economic Impact -- 8.2.3 Social Impact -- 8.3 Digital Technology and Pandemic -- 8.3.1 Digital Healthcare -- 8.3.2 Network and Connectivity -- 8.3.3 Development of Potential Treatment -- 8.3.4 Online Platform for Learning and Interaction -- 8.3.5 Contactless Payment -- 8.3.6 Entertainment -- 8.4 Application of Technology for Handling Pandemic -- 8.4.1 Technology for Preparedness and Response -- 8.4.2 Machine Learning for Pandemic Forecast -- 8.5 Challenges with Digital Healthcare -- 8.6 Conclusion -- References -- 9 Emerging Technologies for COVID-19 -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Technologies to Combat COVID-19 -- 9.3.1 Blockchain -- 9.3.2 Unmanned Aerial Vehicle (UAV) -- 9.3.3 Mobile APK -- 9.3.4 Wearable Sensing -- 9.3.5 Internet of Healthcare Things -- 9.3.6 Artificial Intelligence -- 9.3.7 5G -- 9.3.8 Virtual Reality -- 9.4 Comparison of Various Technologies to Combat COVID-19 -- 9.5 Conclusion -- References -- 10 Emerging Techniques for Handling Pandemic Challenges -- 10.1 Introduction to Pandemic -- 10.1.1 How Pandemic Spreads? -- 10.1.2 Background History -- 10.1.3 Corona -- 10.2 Technique Used to Handle Pandemic Challenges -- 10.2.1 Smart Techniques in Cities. 10.2.2 Smart Technologies in Western Democracies -- 10.2.3 Technoor Human-Driven Approach -- 10.3 Working Process of Techniques -- 10.4 Data Analysis -- 10.5 Rapid Development Structure -- 10.6 Conclusion & -- Future Scope -- References -- Part 3: ALGORITHMIC TECHNIQUES FOR HANDLING PANDEMIC -- 11 A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling -- 11.1 Introduction -- 11.2 Methodology -- 11.2.1 Data Collection -- 11.2.2 Mathematical Model Development -- 11.2.3 Proposed Hybrid Adaptive PSO-GWO (APGWO) Algorithm -- 11.2.4 Discrete Version of APGWO -- 11.3 Computational Results -- 11.4 Conclusion -- References -- 12 Multi-Purpose Robotic Sensing Device for Healthcare Services -- 12.1 Introduction -- 12.2 Background and Objectives -- 12.3 The Functioning of Multi-Purpose Robot -- 12.4 Discussion and Conclusions -- References -- 13 Prevalence of Internet of Things in Pandemic -- 13.1 Introduction -- 13.2 What is IoT? -- 13.2.1 History of IoT -- 13.2.2 Background of IoT for COVID-19 Pandemic -- 13.2.3 Operations Involved in IoT for COVID-19 -- 13.2.4 How is IoT Helping in Overcoming the Difficult Phase of COVID-19? -- 13.3 Various Models Proposed for Managing a Pandemic Like COVID-19 Using IoT -- 13.3.1 Smart Disease Surveillance Based on Internet of Things -- 13.3.2 IoT PCR for Spread Disease Monitoring and Controlling -- 13.4 Global Technological Developments to Overcome Cases of COVID-19 -- 13.4.1 Noteworthy Applications of IoT for COVID-19 Pandemic -- 13.4.2 Key Benefits of Using IoT in COVID-19 -- 13.4.3 A Last Word About Industrial Maintenance and IoT -- 13.4.4 Issues Faced While Implementing IoT in COVID-19 Pandemic -- 13.5 Results & -- Discussions -- 13.6 Conclusion -- References -- 14 Mathematical Insight of COVID-19 Infection-A Modeling Approach -- 14.1 Introduction -- 14.1.1 A Brief on Coronaviruses. 14.2 Epidemiology and Etiology.
Record Nr.	UNINA-9910555012303321

Autore	Juneja Sapna
Edizione	[1st ed.]
Pubbl/distr/stampa	Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica	1 online resource (425 pages)
Disciplina	174/.90063
Altri autori (Persone)	DhanarajRajesh Kumar JunejaAbhinav SathyamoorthyMalathy ShaikhAsadullah
Soggetto topico	Artificial intelligence - Moral and ethical aspects
ISBN	1-394-27531-5 1-394-27530-7
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 Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector -- 1.1 Introduction -- 1.2 Chatbots in the Financial Industry -- 1.3 Background of the Study -- 1.4 Literature Review -- 1.5 Understanding Bias in Customer Service Chatbots -- 1.5.1 Categorizing Biases in Financial Chatbots -- 1.5.2 Sources and Origins of Bias in Financial Chatbots -- 1.5.3 User Feedback and Bias Detection -- 1.5.4 The Role of Explainability in Unveiling Bias -- 1.6 Impact of Bias in Financial Chatbot Interactions -- 1.6.1 Customer Trust and Satisfaction -- 1.6.2 Perpetuation of Inequalities -- 1.6.3 Reputational Risks for Financial Institutions -- 1.6.4 Regulatory Compliance Challenges -- 1.6.5 Implications for Brand Image -- 1.7 Strategies for Mitigating Bias in Financial Customer Service Chatbots -- 1.7.1 Diverse and Representative Training Data -- 1.7.2 Continuous Monitoring and Iterative Improvement -- 1.7.3 Explainability Features for User Trust -- 1.7.4 Inclusive User Testing -- 1.7.5 Ethical Guidelines and Governance -- 1.7.6 Collaborative Partnerships with Ethical AI Experts -- 1.8 Ethical Considerations and Transparency in Financial Chatbot Firms -- 1.9 Future Directions and Recommendations -- 1.10 Conclusion -- References -- Chapter 2 Domain-Specific AI Algorithms and Models in Decision-Making: An Overview -- 2.1 Introduction -- 2.1.1 Overview of the Role of AI in Decision Making -- 2.1.1.1 The Emergence of Artificial Intelligence: How it is Changing Decision-Making in Several Domains of Economics -- 2.1.1.2 Putting the Power of Artificial Intelligence to Work in a Particular Field -- 2.1.1.3 The AI-Assisted Decision-Making Process -- 2.1.1.4 Benefits and Future of AI-Powered Decision-Making. 2.1.2 Importance of Domain-Specific Approaches -- 2.1.2.1 Advantages of Domain-Specific AI -- 2.1.2.2 Instances of Domain-Specific AI in Action -- 2.1.2.3 General AI versus Domain-Specific AI: Powering Intelligent Decisions -- 2.2 Understanding Domain-Specific Decision Making -- 2.2.1 Bridging the Gap: Explainable AI for Effective Collaboration between Machine Learning and Domain Expertise -- 2.3 Building Blocks of AI for Decision-Making -- 2.3.1 Overview of AI Approaches -- 2.3.2 Machine Learning for Data-Driven Decision Generating -- 2.3.3 Knowledge-Based Systems for Rule-Based Decision-Making -- 2.3.4 Reinforcement Learning in Dynamic Environments -- 2.4 Domain-Specific AI: Revolutionizing Industries -- 2.4.1 Healthcare -- 2.4.1.1 The Importance of Patient-Centered Design in Regulating Large Language Models or Generative AI -- 2.4.1.2 XAI in Biomedicine: A Post-Pandemic Surge for Trustworthy AI in Healthcare Delivery -- 2.4.2 Finance -- 2.4.2.1 Explainable AI: A Path Toward Trustworthy and Ethical Applications of Machine Learning in Finance -- 2.4.2.2 Learning Machines, Evolving Markets: The Need for Adaptable Generative AI in Finance -- 2.4.3 Manufacturing -- 2.4.3.1 The Rise of Generative AI: A Call for Responsible AI Frameworks in MSME Manufacturing -- 2.4.3.2 Guiding the Future of Manufacturing: Responsible AI as a Cornerstone for Sustainable and Ethical Production -- 2.4.4 Transportation -- 2.4.4.1 Revolutionizing Urban Mobility: The Power of Machine Learning and AI in Smart City Transportation -- 2.4.4.2 AI Revolutionizes Transportation: Boosting Efficiency, Safety, and New Business Opportunities -- 2.4.5 Agriculture -- 2.4.5.1 Cultivating a Sustainable Future: How AI and Big Data are Revolutionizing Precision Agriculture. 2.4.5.2 AI in the Fields: From Precision Irrigation to Smart Robots, How Artificial Intelligence Is Revolutionizing Agribusiness -- 2.4.6 Retail -- 2.4.6.1 The Generative Retail Revolution: How AI is Personalizing Customer Experience, Optimizing Inventory, and Driving Sales -- 2.4.6.2 The Future of Retail: Leveraging AI for Efficiency and Personalization while Navigating Data Privacy and Ethical Challenges -- 2.4.7 Domain-Specific AI: A Comparative Analysis -- 2.5 Ethical and Societal Implications -- 2.6 Future Directions and Emerging Trends -- 2.7 Conclusion -- References -- Chapter 3 Role of AI in Decision-Making . A Comprehensive Study -- 3.1 Introduction -- 3.2 Need of AI-Based Decision-Making System -- 3.3 Major Obstacle for AI-Based Decision-Making System -- 3.4 Applications of AI-Based Decision-Making System -- 3.5 Case Study: AIDMS for Age-Related Macular Degeneration (AMD) -- 3.6 Conclusion and Future Directions -- References -- Chapter 4 Ethical Challenges in AI Decision-Making: From the User's Perspective -- 4.1 Introduction -- 4.1.1 Ethical Principles in AI -- 4.1.2 The Role of Data in AI Decision-Making -- 4.2 Public Perception towards AI -- 4.3 Ethical Dilemmas of AI -- 4.4 Emerging Issues that are Prevailing in the Current World -- 4.4.1 Case Studies -- 4.4.2 Collaboration and Stakeholder Involvement -- 4.5 Future Considerations -- 4.5.1 Conclusion -- References -- Chapter 5 Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration -- 5.1 Introduction -- 5.1.1 About Yoga -- 5.1.1.1 Advantages and Disadvantages of Yoga -- 5.1.2 Posture Detection System -- 5.1.2.1 Components of Posture Detection System -- 5.1.2.2 Process of Posture Detection System -- 5.1.2.3 Applications of Posture Detection System -- 5.1.2.4 Advantages and Disadvantages of Posture Detection System. 5.1.3 Ethical Decision-Making in Yoga Posture Detection through AI -- 5.2 Literature Review -- 5.3 Technologies Used -- 5.3.1 MediaPipe -- 5.3.2 OpenCV (Open-Source Computer Vision Library) -- 5.4 Dataset Used -- 5.5 Methodology -- 5.5.1 How Does It Work? -- 5.6 Conclusion -- References -- Chapter 6 Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control -- 6.1 Introduction -- 6.1.1 The Effect of Artificial Intelligence on Controlling Inventory -- 6.1.2 Process of Evolution and Development in Stock Control -- 6.2 Benefits and Impact of AI on Inventory Control -- 6.2.1 Moral Considerations in AI-Primarily Based Selection Making -- 6.3 Best Practices for Implementing AI for Stock Management in E-Commerce -- 6.3.1 Consideration in Statistics and Statistics Safety -- 6.3.2 How AI Enables Stock Administration for Important Corporations -- 6.3.3 Synthetic Intelligence in Inventory Administration: Destiny Styles and Extension -- 6.3.4 Inventory Control with Predictive Renovation -- 6.4 Formulation of Proposed Model -- 6.4.1 Framework Discussion -- 6.4.2 Assumptions and Notations -- 6.4.3 Proposed Mathematical Model -- 6.4.4 Example -- 6.4.5 Sensitivity Analysis -- 6.5 Conclusion -- References -- Chapter 7 Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making -- 7.1 Introduction -- 7.2 Concept of Machine Learning and Data Ethics -- 7.3 Importance of ML and AI in Design Making -- 7.4 Defining an Intelligent Decision-Making Support System -- 7.5 Transformation of the Decision-Making System to Intelligent Decision-Making Support -- 7.6 Architecture Framework -- 7.6.1 Components of the IDSS Architecture -- 7.7 Conceptual Framework -- 7.7.1 Core Concepts -- 7.7.2 Components of the Conceptual Framework -- 7.7.3 Block Diagram of the Conceptual Framework. 7.7.4 Principles of Framework -- 7.7.4.1 Tools Used in IDMSS -- 7.7.4.2 Data Processing Tools -- 7.7.4.3 Machine Learning Frameworks -- 7.7.4.4 Cloud Computing Platforms -- 7.7.5 Analyzing Different Tools -- 7.7.6 Data Processing Tools -- 7.7.7 Machine Learning Frameworks -- 7.7.8 Convolutional Neural Networks (CNNs) -- 7.7.9 Recurrent Neural Networks (RNNs) -- 7.7.10 Cloud Computing Platforms -- 7.8 Cloud-Based Scalability with Auto Scaling -- 7.9 Case Study of Complex Problem Using Framework -- 7.10 Algorithm and Coding Analysis -- 7.11 Results and Impact Analysis -- 7.12 Conclusion -- References -- Chapter 8 Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective -- 8.1 Introduction -- 8.1.1 Human-in-the-Loop -- 8.2 Human Interaction with AI Platform -- 8.3 Human and Machine Ethical Annotation -- 8.4 Exploring AI with Human-in-the-Loop Technique -- 8.4.1 AI-Ethical Module -- 8.4.2 Role of HITL in Ethical Decision-Making -- 8.5 Creating Ethical AI Using HTIL Technique -- 8.5.1 Distributed Ethical Decision System -- 8.5.2 Viability and Advantages of Decision-Making Using Ethical AI -- 8.5.3 Problem Statement -- 8.6 Conclusion -- References -- Chapter 9 AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Deep Learning Approach -- 9.2.2 Gradient Boosting Tree -- 9.2.3 Convolutional Neural Network -- 9.2.3.1 Layer of Convolution -- 9.2.3.2 Pool Layer -- 9.2.3.3 Active Layer -- 9.2.3.4 Full Connection Layer -- 9.2.4 Deeper Learning Organizational Techniques -- 9.3 The Proposed Model -- 9.4 Evaluation of the Impact of the Technology -- 9.4.1 Data Set -- 9.4.1.1 Evaluation Criteria -- 9.5 Conclusion -- References -- Chapter 10 Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions. 10.1 Introduction.
Record Nr.	UNINA-9911020433703321

Autore	Anand Rohit
Edizione	[1st ed.]
Pubbl/distr/stampa	Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica	1 online resource (460 pages)
Altri autori (Persone)	JunejaAbhinav PandeyDigvijay JunejaSapna SindhwaniNidhi
ISBN	1-394-18809-9 1-394-18807-2
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Cover -- Title Page -- Copyright Page -- Contents -- About the Book -- About the Editors -- List of Contributors -- Preface -- 1 The Future of Business Management with the Power of Distributed Systems and Computing -- 1.1 Introduction -- 1.1.1 Distributed Systems in Business Management -- 1.2 Understanding Distributed Systems and Computing -- 1.2.1 Definition of Distributed Systems and Computing -- 1.2.2 Advantages for Business Management -- 1.2.3 Characteristics of Distributed Systems and Computing for Business Management -- 1.3 Applications of Distributed Systems and Computing in Business Management -- 1.3.1 Inventory Management and Supply Chain Optimization -- 1.3.2 Customer Relationship Management -- 1.3.3 Financial Management and Accounting -- 1.3.4 Data Analytics and Decision-Making -- 1.3.5 Collaboration and Communication Within and Across Organizations -- 1.4 Limitations of Distributed Systems in Business Management -- 1.4.1 Security and Privacy Concerns -- 1.4.2 Technical Issues and Maintenance -- 1.4.3 Organizational and Cultural Challenges -- 1.4.4 Legal and Regulatory Compliance -- 1.5 Future Developments and Opportunities -- 1.5.1 Potential Future Developments and their Implications for Business Management -- 1.5.2 Opportunities for Research and Innovation in the Field -- 1.6 Conclusion -- References -- 2 Applications of Optimized Distributed Systems in Healthcare -- 2.1 Introduction -- 2.2 Literature Survey -- 2.2.1 Need for Optimization of Distributed Systems -- 2.2.2 Performance Optimization of Distributed Systems -- 2.2.3 Characteristics of Optimized Distributed Systems in Healthcare -- 2.2.4 Applications of Optimized Distributed Systems in Healthcare -- 2.2.5 Technologies Being Used in Healthcare -- 2.2.5.1 Spark -- 2.2.5.2 Hadoop -- 2.3 Real Cases -- 2.4 Conclusion -- References. 3 The Impact of Distributed Computing on Data Analytics and Business Insights -- 3.1 Introduction -- 3.1.1 Role of Distributed Computing in Data Analytics -- 3.1.2 Importance of Business Insights in Decision-Making -- 3.1.3 Overview of Distributed Computing and Data Analytics -- 3.2 Distributed Computing and Data Analytics -- 3.2.1 Distributed Computing -- 3.2.2 Overview of Data Analytics -- 3.2.3 Distributed Computing in Data Analytics -- 3.3 Business Insights and Decision-Making -- 3.3.1 Definition of Business Insights -- 3.3.2 Importance of Business Insights in Decision-Making -- 3.3.3 Applications of Business Insights and their Impact -- 3.4 Challenges and Limitations -- 3.5 The Impact of Distributed Computing on Data Analytics -- 3.5.1 Distributed Computing in Improvising Data Analytics -- 3.6 Conclusion -- References -- 4 Machine Learning and Its Application in Educational Area -- 4.1 Introduction -- 4.2 Previous Work -- 4.3 Technique -- 4.3.1 Machine Learning -- 4.3.2 Supervised Learning -- 4.3.3 Unsupervised Learning -- 4.4 Analysis of Data -- 4.5 Educational Data Mining -- 4.6 Hadoop Approach -- 4.7 Artificial Neural Network (ANN) -- 4.8 Decision Tree -- 4.9 Results/Discussion -- 4.9.1 Personalized Learning Through Adaptive Learning -- 4.10 Increasing Efficiency Using Learning Analytics -- 4.11 Predictive Analysis for Better Assessment Evaluation -- 4.12 Future Scope -- 4.13 Conclusion -- References -- 5 Approaches and Methodologies for Distributed Systems: Threats, Challenges, and Future Directions -- 5.1 Introduction -- 5.2 Distributed Systems -- 5.3 Literature Review -- 5.4 Threats to Distributed Systems Security -- 5.4.1 Hacking -- 5.4.2 Malware -- 5.4.3 Denial of Service (DoS) Attacks -- 5.4.4 Man-in-the-Middle (MitM) Attacks -- 5.4.5 Advanced Persistent Threats (APTs) -- 5.4.6 Insider Threats -- 5.4.7 Phishing -- 5.4.8 Ransomware. 5.5 Security Standards and Protocols -- 5.5.1 ISO/IEC 27001 -- 5.5.2 NIST SP 800-53 -- 5.5.3 SOC 2 -- 5.5.4 PCI DSS -- 5.5.5 IEC 62443 -- 5.5.6 OWASP -- 5.5.7 Control Objectives for Information and Related Technologies (COBIT) -- 5.6 Network Security -- 5.7 Access Control -- 5.7.1 Role-based Access Control (RBAC) -- 5.7.2 Discretionary Access Control (DAC) -- 5.7.3 Mandatory Access Control (MAC) -- 5.8 Authentication and Authorization -- 5.9 Privacy Concerns -- 5.10 Case Studies -- 5.10.1 Equifax Data Breach -- 5.10.2 Target Data Breach -- 5.10.3 WannaCry Ransomware Attack -- 5.11 Conclusion -- 5.12 Future Scope -- References -- 6 Efficient-driven Approaches Related to Meta-Heuristic Algorithms using Machine Learning Techniques -- 6.1 Introduction -- 6.2 Stochastic Optimization -- 6.2.1 Genetic Algorithm -- 6.2.2 Particle Swarm Optimization -- 6.3 Heuristic Search -- 6.3.1 Heuristic Search Techniques -- 6.4 Meta-Heuristic -- 6.4.1 Structures of Meta-Heuristic -- 6.5 Machine Learning -- 6.5.1 Applications of Meta-Heuristic -- References -- 7 Security and Privacy Issues in Distributed Healthcare Systems - A Survey -- 7.1 Introduction -- 7.1.1 Traditional Systems -- 7.1.2 Distributed Systems -- 7.2 Previous Study -- 7.2.1 Background and Definitions -- 7.3 Security and Privacy Needs -- 7.4 Security and Privacy Goals -- 7.5 Type of Attacks in Distributed Systems -- 7.5.1 Malicious Hardware -- 7.5.2 Malicious Programs -- 7.6 Recommendations and Future Approaches -- 7.7 Conclusion -- References -- 8 Implementation and Analysis of the Proposed Model in a Distributed e-Healthcare System -- 8.1 Introduction -- 8.2 Outmoded Systems -- 8.3 Distributed Systems -- 8.3.1 Peer-to-Peer Architecture -- 8.4 Previous Work -- 8.5 Service-Oriented Architecture of e-Healthcare -- 8.6 Implementation of the Proposed Model -- 8.6.1 Speech Software. 8.7 Evaluation of the Proposed Model Performance -- 8.8 Conclusion and Future Work -- References -- 9 Leveraging Distributed Systems for Improved Educational Planning and Resource Allocation -- 9.1 Introduction -- 9.1.1 Overview of the Current State of Educational Planning and Resource Allocation -- 9.1.2 The Potential Benefits of Leveraging Distributed Systems in Education -- 9.2 Theoretical Framework -- 9.2.1 Overview of Distributed Systems and their Key Concepts -- 9.2.2 Theoretical Basis for the Use of Distributed Systems in Education -- 9.2.3 Comparison of Different Distributed Systems Architectures -- 9.3 Distribution System in Education -- 9.4 Technical Aspects of Distributed Systems in Education -- 9.4.1 Infrastructure Requirements for Implementing Distributed Systems in Education -- 9.4.2 Security and Privacy Concerns in Distributed Systems for Education -- 9.4.3 Data Management and Analysis in Distributed Systems for Education -- 9.5 Challenges and Limitations -- 9.5.1 Merits of Distributed Systems for Educational Planning and Resource Allocation -- 9.5.2 Demerits of Distributed Systems for Educational Planning and Resource Allocation -- 9.6 Discussion -- 9.7 Conclusion -- References -- 10 Advances in Education Policy Through the Integration of Distributed Computing Approaches -- 10.1 Introduction -- 10.1.1 Technology in Education Policy -- 10.1.2 Advances in Education Policy through Distributed Computing -- 10.2 Distributed Computing Approaches -- 10.2.1 Benefits of Education Policy -- 10.2.2 Types of Distributed Computing Approaches -- 10.3 Advances in Education Policy Through Distributed Computing Approaches -- 10.3.1 Significant Impact on Education Policy -- 10.3.2 Improved Access -- 10.3.3 Personalized Learning -- 10.3.4 Data-Driven Decision-Making -- 10.4 Challenges: Privacy Concerns -- 10.4.1 Technical Requirements. 10.4.2 Impact of Emerging Technologies and Use of Distributed Computing -- 10.5 Conclusion -- References -- 11 Revolutionizing Data Management and Security with the Power of Blockchain and Distributed System -- 11.1 Introduction -- 11.1.1 Importance of Data Management and Security -- 11.1.2 Current State of Data Management and Security -- 11.2 Blockchain Technology -- 11.2.1 Benefits of Using Blockchain for Data Management and Security -- 11.2.2 Limitations of Using Blockchain for Data Management and Security -- 11.3 Distributed System -- 11.3.1 Benefits of Using Distributed Systems for Data Management and Security -- 11.3.2 Limitations of Using Distributed Systems for Data Management and Security -- 11.4 Revolutionizing Data Management and Security with Blockchain and Distributed Systems -- 11.4.1 Blockchain and Distributed Systems Can Revolutionize Data Management and Security -- 11.4.2 Real-World Examples of Blockchain and Distributed Systems in Data Management and Security -- 11.5 Challenges of Using Blockchain and Distributed Systems -- 11.5.1 Limitations of Using Blockchain and Distributed Systems -- 11.6 Discussion -- 11.7 Conclusion -- References -- 12 Enhancing Business Development, Ethics, and Governance with the Adoption of Distributed Systems -- 12.1 Introduction -- 12.1.1 Distributed Systems for Business Development -- 12.2 Applications of Distributed Systems in Business Development -- 12.2.1 Characteristics of Distributed Systems -- 12.2.2 Benefits of Distributed Systems in Business Development -- 12.2.3 Applications in Business Development -- 12.3 The Importance of Ethics in Distributed Systems -- 12.3.1 Ethics in Distributed Systems -- 12.3.2 Ethics to Business Development and Governance -- 12.3.3 Distributed Systems in Promoting Ethical Practices -- 12.4 Governance in Distributed Systems. 12.4.1 Importance of Governance in Distributed Systems.
Record Nr.	UNINA-9910842399403321