Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 A Glance View on Cloud Infrastructures Security and Solutions
1.1 Introduction
1.2 Methodology
1.3 Literature Review
1.4 Open Challenges
1.5 Recommendations
1.6 Conclusion
Acknowledgments
References
Chapter 2 Artificial Intelligence Effectiveness for Conversational Agents in Healthcare Security
2.1 Introduction
2.2 Types of AI Relevance to Healthcare
2.2.1 Machine Learning (ML)—Neural Networks and Deep Learning
2.2.2 Rule-Based Expert System
2.2.3 Robotic Process Automation
2.3 The Future of AI in Healthcare
2.4 Ways of Artificial Intelligence that Will Impact Healthcare
2.4.1 Unifying Mind and Machine Using BCIs
2.4.2 Radiology’s Next Generation
2.4.3 Developing the Immunotherapy Treatment
2.4.4 Tracking Health with Personal and Portable Devices
2.5 AI Models
2.5.1 Artificial Neural Network
2.5.2 Zero Trust Technology Application for AI Medical Research
2.6 Compare E-Cohort Findings on Wearables and AI in Healthcare
2.6.1 Results
2.6.1.1 Participant Characteristics
2.7 Ethical Concerns of AI in Healthcare
2.8 Future in Healthcare
2.9 Conclusion
References
Chapter 3 Conversational AI: Security Features, Applications, and Future Scope at Cloud Platform
3.1 Introduction
3.2 How Does Conversational Artificial Intelligence (AI) Work?
3.3 The Conversational AI Components
3.4 Uses of Conversational AI
3.5 Advantages of Conversational AI
3.5.1 Cost-Effectiveness
3.5.2 Enhanced Sale and Customer Engagement
3.5.3 Scalability
3.5.4 Detect and Prevent Cyberattacks Through AI
3.5.5 Businesses Benefit from Automated Response Work
3.6 Challenges with Conversational Artificial Intelligence
3.7 Risks Associated with Conversational AI
3.7.1 5D-Model for Discretion
3.7.2 User Consciousness
3.7.3 RFID Protects Privacy and Security
3.7.4 Data Aggregation
3.7.5 Stakeholder Model
3.7.6 2 × 2 Framework
3.7.7 Framework for Mobile Cloud
3.7.8 Changes to Pseudonyms in Intelligent Transportation Systems
3.7.9 Homomorphic Encryption
3.7.10 Three-Layer Model
3.7.11 Linear Algebra
3.7.12 Continuous Streaming Data
3.7.13 DBMS Defense Against Insider Threats
3.7.14 Transaction Data Anonymization
3.7.15 D-Mash Model
3.7.16 A Safe Cryptosystem Based on Lattice
3.8 Proposed Model for Conversational AI in Cloud Platform
3.9 Conclusion
3.10 Future Work
References
Chapter 4 Unsupervised BERT-Based Granular Sentiment Analysis of Literary Work
4.1 Introduction
4.2 Related Works
4.3 Text Extraction
4.4 Data Preprocessing
4.5 Sentiment Analysis on Literary Works
4.6 TF-IDF Vectorizer
4.7 Fine-Grained Sentiment Analysis on Literary Data
4.8 BERT Classifier for Unsupervised Learning
4.9 Conclusion
References
Chapter 5 Extracting and Analyzing Factors to Identify the Malicious Conversational AI Bots on Twitter
5.1 Introduction
5.2 Literature Review
5.3 Methods
5.4 Results and Discussion
5.5 Conclusion and Future Direction
References
Chapter 6 Evolution and Adoption of Conversational Artificial Intelligence in the Banking Industry
6.1 Introduction
6.2 Significance of Artificial Intelligence
6.3 Conversational AI in the Indian Banking Industry
6.4 Conversational AI in Use in Various Companies
6.5 Conclusion
References
Chapter 7 Chatbots: Meaning, History, Vulnerabilities, and Possible Defense
7.1 Understanding Chatbots
7.2 History of Chatbots
7.3 Vulnerabilities and Security Concerns of Chatbots
7.4 Possible Defense Strategies
7.5 Conclusion
References
Chapter 8 Conversational Chatbot-Based Security Threats for Business and Educational Platforms and Their Counter Measures
8.1 Introduction
8.2 Chatbot Applications in Education, Business Management, and Health Sector
8.2.1 Chatbots for Education
8.2.2 Healthcare Domain Chatbots
8.2.3 E-Commerce Applications
8.2.4 Customer Support
8.3 Security and Privacy in Chatbot
8.4 Related Work
8.5 Methodology
8.6 Results and Discussion
8.7 Conclusion
References
Chapter 9 Identification of User Preference Using Human–Computer Interaction Technologies and Design of Customized Reporting for Business Analytics Using Ranking Consistency Index
9.1 Introduction
9.2 Literature Review
9.3 Design of Metric for Ranking Consistency Index
9.4 Experimentation
9.5 Results and Discussion
9.6 Conclusion
References
Chapter 10 Machine Learning for Automatic Speech Recognition
10.1 Introduction
10.2 Related Work
10.3 Methodology
10.4 Results
10.5 Conclusion
References
Chapter 11 Conversational Artificial Intelligence at Industrial Internet of Things
11.1 Introduction
11.1.1 What is Conversational AI?
11.1.2 What Does Conversational AI Do?
11.1.3 How Does Conversational AI Work?
11.2 Technology Components Used in Conversational AI
11.2.1 Natural Language Processing (NLP)
11.2.2 Advanced Dialog Management (ADM)
11.2.3 Machine Learning (ML)
11.2.4 Automatic Speech Recognition (ASR)
11.3 Benefits of Conversational AI
11.3.1 Improved Scalability
11.3.2 Cost-Efficient Technology
11.3.3 Improved User Engagement
11.3.4 Better Accessibility
11.4 How to Create Conversational AI?
11.5 Conversational Platforms and Internet of Things: Relevance and Benefits
11.5.1 Benefits of Implementing Chatbots with the IoT Interface
11.6 Internet of Things Status for Industry
11.6.1 The Impact of IIoT on Operational Effectiveness
11.6.2 Evolution of Legacy Systems
11.6.3 Increased Efficiency in Energy
11.6.4 Intelligent Data Analytics
11.6.5 Support Human Workers, Cobots are Connected
11.6.6 Smart Technologies (Digital Twins) are Becoming More Popular
11.6.7 The IIoT Landscape is Changing Due to Edge Computing
11.6.8 Adoption Hurdles and Common Pitfalls
11.7 Scope of IIoT in Future
11.7.1 The Effect of IIoT on Research and Development
11.8 Work of IIoT with Additional New Innovations
11.8.1 Manufacturing as a Service with IIoT
11.8.2 Cloud and Edge Computing Together
11.8.3 Prevention-Based Service
11.8.4 Equipment, Tool, and Inventory Use in Global Positioning System (GPS)
11.9 Conclusion
References
Chapter 12 Performance Analysis of Cloud Hypervisor Using Network Package Workloads in Virtualization
12.1 Introduction
12.2 A Related Study on Energy Efficiency
12.3 Motivation
12.4 Experiment Methodology and Setup
12.4.1 Work Setup
12.5 Results and Discussion
12.5.1 Observation of Windows
12.5.2 Observation on Ubuntu
12.6 Conclusion
References
Chapter 13 Evaluation of Chabot Text Classification Using Machine Learning
13.1 Introduction
13.2 Literature Survey
13.2.1 Optimization Techniques for Chatbot Text Feature Selection
13.2.2 Chatbot Text Classification Using Machine Learning
13.2.3 Text Clustering Techniques
13.3 Methodology
13.4 Results
13.5 Conclusion
References
Chapter 14 Enhanced Security in Chatbot
14.1 Introduction
14.2 Architecture of Chatbots
14.3 Working of Chatbots
14.4 Background
14.5 Literature Survey
14.6 Proposed System
14.7 Analysis of the Work
14.8 Future Work
14.9 Conclusion
References
Chapter 15 Heart Disease Prediction Using Ensemble Feature Selection Method and Machine Learning Classification Algorithms
15.1 Introduction
15.2 Review of Literature
15.3 Proposed Methodology
15.3.1 Data Description
15.3.2 Data Preprocessing
15.3.3 Feature Selection
15.3.4 Classification Algorithms
15.4 Experimental Results
15.5 Conclusion
References
Chapter 16 Conversational AI: Dialoguing Most Humanly With Non-Humans
16.1 Introduction
16.2 History
16.3 Chatbot vs. Conversational AI
16.4 Dialogue Systems
16.5 Human Computer Interaction
16.6 Artificial Intelligence
16.7 Components of Conversational AI
16.8 Frameworks, Models, and Architectures
16.9 Conclusion
References
Chapter 17 Counterfeit Pharmaceutical Drug Identification
17.1 Introduction
17.1.1 Related Works
17.2 Materials and Methods
17.2.1 Data Description
17.2.2 Proposed Method
17.2.2.1 Preprocessing
17.2.2.2 Optical Character Recognition
17.2.2.3 Named Entity Recognition
17.2.2.4 Hash Functions
17.3 Results and Discussion
17.4 Conclusion
References
Chapter 18 Advanced Security Solutions for Conversational AI
18.1 Introduction
18.1.1 Conversational AI Vulnerability
18.2 Background
18.3 Components of Conversational AI
18.4 Challenges with Conversational AI
18.5 Conclusion
References
Chapter 19 Security Threats and Security Testing for Chatbots
19.1 Introduction
19.2 Related Work
19.3 Vulnerability Assessment Tools
19.3.1 OpenVAS
19.3.2 Acunetix Vulnerability Scanner
19.3.3 Zaproxy by OWASP
19.3.4 Burpsuite by Portswigger
19.4 Penetration Testing
19.4.1 Types of Penetration Test
19.4.1.1 Black Box Testing
19.4.1.2 White Box Testing
19.4.1.3 Gray Box Testing
19.4.2 Penetration Testing Phases
19.4.2.1 Planning Process
19.4.2.2 Discovery Phase
19.4.2.3 Attack Phase
19.4.2.4 Reporting Phase
19.5 Vulnerabilities in Chatbot
19.6 Conclusion
References
Chapter 20 ChatBot-Based Next-Generation Intrusion Detection System
20.1 Introduction
20.2 Literature Survey
20.2.1 Review of Optimization Algorithms for IDS
20.2.2 Review of Classification Methods for IDS
20.3 Methodology
20.4 Result Analysis
20.5 Conclusion
References
Chapter 21 Conversational Chatbot With Object Recognition Using Deep Learning and Machine Learning
21.1 Introduction
21.2 Literature Survey
21.2.1 Object Recognition Using Deep Learning
21.2.2 Object Recognition in Real-Time Images
21.3 Methodology
21.4 Results and Discussion
21.5 Conclusion
References
Chapter 22 Automatic Speech Recognition Design Modeling
22.1 Introduction
22.2 Literature Survey
22.2.1 Noise Reduction Algorithms for Speech Enhancement
22.2.2 Speech Segmentation, Feature Extraction
22.3 Methodology
22.4 Experimental Result Analysis
22.5 Conclusion
References
Chapter 23 The Future of Modern Transportation for Smart Cities Using Trackless Tram Networks
23.1 Introduction
23.2 Proposed System Architecture
23.3 Working Process of the TRAM-RSU Framework
23.4 Experimental Analysis
23.5 Summary
References
Chapter 24 Evaluating the Performance of Conversational AI Tools: A Comparative Analysis
24.1 Introduction
24.2 Literature Review
24.3 Methodology
24.3.1 Methodology Phases
24.3.1.1 Define the Evaluation Metrics
24.3.1.2 Prepare the Test Data and Scenarios
24.3.1.3 Select the Evaluation Methodology
24.3.1.4 Conduct the Evaluation
24.3.1.5 Analyze and Interpret the Results
24.3.1.6 Draw Conclusions and Make Recommendations
24.3.2 Types of Evaluation Metrics Used for the Evaluation
24.3.2.1 Evaluation Metrics
24.3.2.2 Dialog Generation
24.3.2.3 User Experience
24.4 Result
24.4.1 Platforms Used for the Implementation of Conversational AI Tools
24.4.1.1 Amazon Lex
24.4.1.2 Google Dialog Flow
24.4.1.3 Microsoft’s Bot Framework
24.4.1.4 Wit.ai
24.4.1.5 Dialog Flow
24.4.1.6 LUIS
24.4.1.7 Watson Conversation
24.4.1.8 Rasa
24.4.1.9 Century Tech
24.4.2 Conversational AI Tools Used in Research
24.4.2.1 Data Collection
24.4.2.2 Data Analysis
24.4.2.3 Data Interpretation
24.4.2.4 Human–AI Interaction
24.4.2.5 Ethical Considerations
24.4.3 Smart Tutoring Systems
24.4.3.1 Carnegie Learning
24.4.3.2 DreamBox Learning
24.4.3.3 Squirrel AI
24.4.3.4 Knewton
24.4.3.5 Duolingo
24.4.3.6 Coursera
24.4.4 Smart Invigilation
24.4.4.1 Proctorio
24.4.4.2 ExamSof
24.4.4.3 Respondus Monitor
24.4.4.4 Honorlock
24.4.4.5 ProctorU
24.4.4.6 Kryterion Webassessor
24.4.4.7 Secure Exam Remote Proctor
24.4.4.8 Bvirtual
24.4.4.9 PSI Bridge
24.4.4.10 Talview
24.4.5 Conversational AI Tools Used for Autonomous Grading Systems
24.4.5.1 Gradescope
24.4.5.2 Turnitin
24.4.5.3 Coursera Auto-Grader
24.4.5.4 Code Runner
24.4.5.5 EdX
24.4.5.6 AI-Assisted Grading
24.4.5.7 Light SIDE
24.4.5.8 Xceptiona lED
24.4.5.9 Vantage Learning’s MY Access
24.4.5.10 Gradescope for Programming
24.4.5.11 Grammarly
24.4.5.12 Pearson
24.4.6 Conversational AI-Based Monitoring System Tools
24.4.6.1 IBM Watson
24.4.6.2 Verint
24.4.6.3 Darktrace
24.4.6.4 Cybereason
24.4.6.5 Splunk
24.4.6.6 Microsoft Azure Monitor
24.4.6.7 OpsGenie
24.4.6.8 Botmetric
24.4.6.9 DataDog
24.4.6.10 IBM Watson Assistant
24.4.6.11 Hugging Face
24.4.6.12 Zabbix
24.5 Discussion
24.6 Conclusion
References
Chapter 25 Conversational AI Applications in Ed-Tech Industry: An Analysis of Its Impact and Potential in Education
25.1 Introduction
25.2 Conversational AI in Ed-Tech Overview
25.3 Methodology
25.3.1 Planning
25.3.1.1 Inclusion and Exclusion Criteria
25.3.1.2 Source of Information
25.3.1.3 Search Procedure
25.3.2 Conducting the Review
25.3.2.1 Search String
25.3.2.2 Data Extraction
25.3.2.3 Quality Evaluation
25.3.3 Research Question Formulation
25.3.4 Review Reporting
25.3.4.1 RQ1. What are the Different Streams in the Ed-Tech Industry Where Conversational AI Applications are Used?
25.3.4.2 RQ2. What are the Different Platforms Used in the Ed-Tech Industry to Operate Conversational AI Applications?
25.3.4.3 RQ3. What are the Different Roles Played by Conversational AI Applications in the Ed-Tech Industry?
25.3.4.4 RQ2. What are the Primary Benefits of the Conversational AI Applications in Ed-Tech Industry?
25.3.4.5 What are the Challenges Faced in the Implementation of Conversational AI Applications in the Ed-Tech Industry that Literature Reviewed?
25.3.4.6 RQ3. What are the Potential Future Areas of the Ed-Tech Industry that Could be Benefited via the Applications of Conversational AI Applications?
25.4 Conclusion, Limitation, and Future Work
References
Chapter 26 Conversational AI: Introduction to Chatbot’s Security Risks, Their Probable Solutions, and the Best Practices to Follow
26.1 Introduction
26.2 Related Work
26.3 History and Evolution of Chatbots
26.4 Components & Concepts that Make Conversational AI Possible
26.5 Working of Conversational AI
26.6 Reasons Behind why Companies are Using Chatbot
26.7 Plans for the Future Development of Conversational AI
26.8 Security Risks of Conversational AI’s Chatbot
26.9 Probable Solutions to the Security Vulnerabilities
26.10 Privacy Laws for the Security of Conversational AI and Chatbot
26.11 Chatbot and GDPR
26.12 Best Practices to Follow to Ensure Chatbot Security
26.13 Conclusion
Acknowledgment
References
Chapter 27 Recent Trends in Pattern Recognition, Challenges and Opportunities
27.1 Introduction
27.2 Optical Character Recognition
27.3 Various Sectors of Pattern Recognition
27.4 Applications of Natural Language Processing
27.5 Conclusion
References
Chapter 28 A Review of Renewable Energy Efficiency Technologies Toward Conversational AI
28.1 Introduction
28.2 Renewable Energy
28.3 Energy Technologies
28.4 Conversational AI
28.5 Conclusion
References
Chapter 29 Messaging Apps Vulnerability Assessment Using Conversational AI
29.1 Introduction
29.1.1 Nature of Information Security
29.1.2 Information Security Assessment
29.1.2.1 Testing
29.1.2.2 Checking
29.1.2.3 Interview
29.1.3 Information Security Assessment Methodology
29.1.4 Information Security and Penetration Testing
29.2 Penetration Test
29.2.1 Difference between a Penetration Tester and an Attacker
29.2.2 Objectives of Penetration Test
29.2.3 Penetration Testing Phases
29.2.3.1 Planning Process
29.2.3.2 Discovery Phase
29.2.3.3 Attack Phase
29.2.3.4 Reporting Phase
29.3 Mobile App Security
29.3.1 Types of Attacks
29.4 Discovered Vulnerabilities in Mobile Applications
29.5 Mitigation Strategies Against Cross-Site Scripting and SQL Attacks
29.5.1 Cross-Site Scripting Mitigation Strategies
29.5.2 SQL Injection Attack Mitigation Strategies
29.6 Mobile Application Security Framework
29.7 Conclusion
References
Chapter 30 Conversational AI Threat Identification at Industrial Internet of Things
30.1 Introduction
30.2 IoT Layered Architecture
30.3 Security Issues in IoT
30.4 Literature Survey of Various Attacks on Industrial Internet of Things
30.5 Various Attacks in Industrial Internet of Things
30.5.1 Perception Layer Attacks
30.5.2 Network Layer Attacks
30.5.3 Application Layer Attacks
30.6 Recent Attacks on Industrial IoT
30.7 Conclusion
References
Chapter 31 Conversational AI—A State-of-the-Art Review
31.1 Introduction
31.1.1 History of Conversational AI
31.1.2 Evolution of Conversational AI
31.2 Related Work
31.3 Problem Statement
31.4 Proposed Methodology
31.5 Regulatory Landscape of Conversational AI
31.6 Future Works
31.7 Conclusion
References
Chapter 32 Risks for Conversational AI Security
32.1 Introduction
32.2 Related Work
32.3 History and Evolution of Conversational AI Security
32.4 Components and Concepts that Make Coversational AI Security
32.4.1 Authentication and Authorization
32.4.1.1 Authentication and Authorization Working Together
32.4.1.2 Benefits of Authentication and Authorization
32.4.2 Encryption
32.4.3 Privacy
32.4.4 Trust
32.4.5 Threat Detection and Response
32.4.6 Compliance
32.4.7 Testing and Auditing
32.4.8 Continuous Improvement
32.5 Working of Conversational AI Security
32.5.1 Threat Modeling
32.5.2 Data Encryption
32.5.3 Secure Communication
32.5.4 Bot Detection
32.5.5 Continuous Monitoring
32.5.6 Regular Updates and Patching
32.6 Risk for Conversational AI Security
32.6.1 Data Privacy
32.6.2 Malicious Attacks
32.6.3 Misuse of User Data
32.6.4 Social Engineering
32.7 Solutions for Conversational AI Security
32.7.1 Encryption and Access Control
32.7.2 Regular Security Audits
32.7.3 Transparent Data Collection Practices
32.7.4 Suspicious Behavior Detection
32.8 Conclusion
Acknowledgement
References
Chapter 33 Artificial Intelligence for Financial Inclusion in India
33.1 Introduction
33.1.1 Financial Inclusion in India
33.1.2 Growth of Financial Inclusion in India
33.1.3 Direct Benefit Transfer
33.2 Digitalization of Banking Sector Paving Way for AI in Financial Inclusion
33.2.1 Digital Financial Inclusion Journey
33.2.2 Importance of Aadhaar for Digital Banking
33.2.3 Unified Payments Interface (UPI)
33.2.4 RuPay
33.3 Technology Acceptance Model
33.4 AI and Use of AI in Financial Inclusion
33.4.1 Advent of AI Banking in India
33.4.2 Artificial Intelligence (AI)
33.4.3 Function of AI in Banking Sector
33.4.4 The Main Purpose of Using AI in Banking
33.4.5 Adoption of AI for Financial Inclusion
33.4.6 Use of AI for Financial Inclusion
33.4.7 Financial Inclusion With the Help of e-KYC
33.5 Conclusion
References
Chapter 34 Revolutionizing Government Operations: The Impact of Artificial Intelligence in Public Administration
34.1 Introduction
34.2 Methodology
34.3 The Origin and Development of AI Technology and Current Methodologies in the Field
34.4 Artificial Intelligence in Indian Governance
34.4.1 Law Enforcement
34.4.1.1 Facial and Speech Recognition
34.4.1.2 Predictive Analytics
34.4.1.3 Robo-Cops
34.4.2 Education
34.4.2.1 Decision Making
34.4.2.2 Personalized Learning
34.4.2.3 Student Services
34.4.2.4 Student Progress Monitoring
34.4.3 Defense
34.4.3.1 Intelligence, Surveillance, and Reconnaissance
34.4.3.2 Robot Soldiers
34.4.3.3 Cyber Defense
34.4.3.4 Intelligent Weapon Systems
34.5 Discharge of Government Functions
34.5.1 Citizen/Government Interface/E-Governance
34.5.1.1 Agriculture
34.5.1.2 Categorization and Arrangement of Documents
34.6 Challenges
34.6.1 Improved Capacity And Enhanced Understanding of Emerging Technologies
34.6.2 Infrastructure
34.6.3 Trust
34.6.4 Funding
34.6.5 Privacy and Security
34.6.6 Transparency
34.7 Conclusion
References
Bibliography
Chapter 35 Conversational AI and Cloud Platform: An Investigation of Security and Privacy
35.1 Introduction
35.2 Cloud Architecture
35.2.1 Cloud Components
35.2.1.1 Clients
35.2.1.2 Datacenter
35.2.1.3 Distributed Servers
35.2.2 Cloud Deployment Models
35.2.2.1 Public Cloud
35.2.2.2 Private Cloud
35.2.2.3 Community Cloud
35.2.2.4 Hybrid Cloud
35.2.3 The Cloud Computing Stack as Cloud Services
33.2.3.1 Software as a Service (SaaS)
35.2.3.2 Platform as a Service (PaaS)
33.2.3.3 Infrastructure as a Service (IaaS)
35.3 Literature Survey
35.4 Security in Conversational AI and Cloud Computing
35.4.1 Importance of Security in Cloud Computing
35.4.2 Security Principles
35.4.3 Cloud Computing and Conversational AI Security Issues
35.4.4 Security Attacks in Chatbot and Cloud Platform
35.5 Conclusion
References
Chapter 36 Chatbot vs Intelligent Virtual Assistance (IVA)
36.1 Introduction
36.2 Related Work
36.3 Problem Statement
36.4 Proposed Methodology
36.5 Regulatory Landscape
36.6 Future Works
36.7 Conclusion
References
Chapter 37 Digital Forensics with Emerging Technologies: Vision and Research Potential for Future
37.1 Introduction
37.2 Background Work
37.3 Digital Twin Technology—An Era of Emerging Technology
37.4 Security
37.4.1 Types of Security
37.5 Digital Forensics Characteristics
37.6 Computer Forensics
37.7 Tool Required for Digital Forensics
37.8 Importance of Computer and Digital Forensics in Smart Era
37.9 Methods/Algorithms for Digital Forensics in Smart Era
37.10 Popular Tools Available for Digital Forensics
37.11 Popular Issues Towards Using AI–Blockchain–IoT in Digital Forensics
37.12 Future Research Opportunities Using AI-Blockchain-IoT in Digital Forensics
37.12.1 Other Future Works
37.12.2 Future of Blockchain
37.12.3 Future of Artificial Intelligence
37.12.4 Future of Internet of Things
37.12.5 Future of AI/ML–Blockchain–IoT Based Smart Devices in Digital Forensics
37.13 Limitations AI/ML–Blockchain–IoT-Based Smart Devices in Digital Forensics
37.14 Conclusion
References
Chapter 38 Leveraging Natural Language Processing in Conversational AI Agents to Improve Healthcare Security
38.1 Introduction
38.2 Natural Language Process in Healthcare
38.3 Role of Conversational AI in Healthcare
38.4 NLP-Driven Security Measures
38.5 Integrating NLP With Security Framework
38.6 Conclusion
References
Chapter 39 NLP-Driven Chatbots: Applications and Implications in Conversational AI
39.1 Introduction
39.2 Related Work
39.3 NLP-Driven Chatbot Technologies
39.4 Chatbot Software for Automated Systems
39.5 Conclusion
References
About the Editors
Index
EULA