Artificial Intelligence for Intelligent Systems: Fundamentals, Challenges, and Applications (Intelligent Data-Driven Systems and Artificial Intelligence)

Cover
Half Title
Series
Title
Copyright
Contents
Preface
About the editors
List of contributors
Part I Recent trends and challenges of artificial intelligence
	1 Unleashing the power of artificial intelligence: exploring multidisciplinary frontiers for innovation and impact
		1.1 Introduction
			1.1.1 Brief overview of artificial intelligence and its capabilities
			1.1.2 Importance of multidisciplinary approach in AI applications
		1.2 Health care
			1.2.1 AI in medical diagnosis and treatment
			1.2.2 AI for drug discovery and personalized medicine
		1.3 Finance and banking
			1.3.1 AI for fraud detection and prevention
			1.3.2 AI-based investment strategies and risk assessment
		1.4 Transportation and logistics
			1.4.1 Autonomous vehicles and intelligent transportation systems
			1.4.2 Optimization algorithms for supply chain management
		1.5 Education
			1.5.1 Intelligent tutoring systems and personalized learning
			1.5.2 AI-based student support and career guidance
		1.6 Environmental sustainability
			1.6.1 AI for climate change modeling and prediction
			1.6.2 Smart energy management and resource optimization
		1.7 Ethical and social implications
			1.7.1 Considerations of bias and fairness in AI applications
			1.7.2 Privacy and security concerns in multidisciplinary AI systems
			1.7.3 Transparency and accountability in AI decision-making
		1.8 Conclusion
			1.8.1 Recap of multidisciplinary applications of AI
			1.8.2 Future directions and potential impact of AI in various fields
		References
	2 Advancements in deep learning: unveiling future trends and applications across AI for intelligent computing
		2.1 Introduction
			2.1.1 Machine learning
			2.1.2 Categories of machine learning
		2.2 Introduction to neural networks
			2.2.1 Vanishing and exploding gradient problem
			2.2.2 Different types of activation functions
		2.3 Training a neural network
			2.3.1 Backpropagation
		2.4 Architectural evolution of deep CNNs
			2.4.1 Beginning of CNN
			2.4.2 Revival of CNN
		2.5 Existing architectures for image classification
			2.5.1 LeNet
			2.5.2 AlexNet
			2.5.3 VGG
			2.5.4 GoogLeNet
			2.5.5 Applications of CNNs
		2.6 Conclusion
		References
	3 Comprehensive comparative analysis of artificial intelligence, machine learning, and deep learning
		3.1 Introduction
		3.2 Fundamentals of artificial intelligence
			3.2.1 Interrelationships
			3.2.2 AI Instances
		3.3 Machine learning: the foundation
		3.4 Deep learning: the neural network revolution
		3.5 Tools and frameworks
			3.5.1 AI implementation tools
			3.5.2 Machine learning implementation tools
			3.5.3 Deep learning implementation tools
		3.6 Evaluation parameters used for AI, ML, DL
		3.7 Case studies and applications
		3.8 Future directions and challenges
		3.9 Conclusion
		References
	4 Applications of artificial intelligence in smart distributed processing and big data mining
		4.1 Introduction
			4.1.1 Applications of artificial intelligence in big data and distributed processing
			4.1.2 Improving the effectiveness of data processing
			4.1.3 Making possible cutting-edge analytics
			4.1.4 Enabling on-demand information processing
			4.1.5 Processing in a distributed manner
			4.1.6 Data extraction and visualization automation
		4.2 Challenges in big data mining
		4.3 Frameworks of distributed processing
		4.4 Distributed system and data mining
			4.4.1 Scalable distributed processing techniques
			4.4.2 Challenges in distributed system
		4.5 Opportunities in big data mining
			4.5.1 Big data mining applications
			4.5.2 Trends in big data technologies
			4.5.3 Tools used in big data mining
			4.5.4 Technologies used in big data mining
		4.6 Conclusion
		References
	5 Quantum AI: uniting the future of smart technologies
		5.1 Introduction
			5.1.1 Exploring quantum AI
			5.1.2 Significance in modern technology
		5.2 Quantum computing essentials
			5.2.1 Understanding qubits and quantum gates
			5.2.2 The quantum advantage
		5.3 Quantum machine learning
			5.3.1 Quantum algorithms and their applications
			5.3.2 Quantum-enhanced data analysis
			5.3.3 Quantum AI in industry
		5.4 Quantum hardware and software
			5.4.1 Quantum processors and development tools
			5.4.2 Quantum programming languages
		5.5 Ethics and security in quantum AI
			5.5.1 Ethical considerations
			5.5.2 Cybersecurity implications
		5.6 The future of quantum AI and conclusion
			5.6.1 Envisioning quantum AI’s role
			5.6.2 Ongoing developments and challenges
			5.6.3 Embracing the quantum AI fusion
			5.6.4 Conclusion
		References
Part II Secure artificial intelligence in computing systems
	6 The significance of artificial intelligence in cybersecurity
		6.1 Introduction
		6.2 AI Applications in cybersecurity
			6.2.1 Identifying and preventing threats through AI
			6.2.2 The detection and prevention of advance threats
			6.2.3 Automated and intelligent response to incidents
			6.2.4 Malware detection and analysis improvements
			6.2.5 Behavioral analytics and user monitoring
			6.2.6 Proactive vulnerability management
			6.2.7 Collaborative threat intelligence
		6.3 Artificial intelligence
			6.3.1 Machine learning in cybersecurity
			6.3.2 Supervised learning
			6.3.3 Unsupervised learning
			6.3.4 Deep learning in cybersecurity
		6.5 Cybersecurity and different attack types
			6.5.1 Physical security attack
			6.5.2 Man-in-the-middle
			6.5.3 Bluetooth man-in-the-middle
			6.5.4 False data injection attack
			6.5.5 Botnets
		6.6 Future of cybersecurity
		6.7 Conclusion
		References
	7 Securing the internet of things with blockchain
		7.1 Introduction
		7.2 IoT security challenges and vulnerabilities
			7.2.1 Risks associated with compromised IoT devices
			7.2.2 Existing security measures
		7.3 Blockchain technology for IoT security
			7.3.1 Blockchain’s security features
			7.3.2 Key applications of blockchain in IoT security
			7.3.3 Comparative analysis of blockchain implementations for IoT security
			7.3.4 Scalability, performance, and resource efficiency considerations
			7.3.5 Use of smart contracts for secure and automated transactions in IoT
		7.4 Limitations and challenges
			7.4.1 Scalability and performance considerations
			7.4.2 Energy efficiency and resource constraints
			7.4.3 Regulatory and legal implications
		7.5 Future research opportunities and directions
			7.5.1 Privacy-preserving techniques
			7.5.2 Scalability solutions
			7.5.3 Interoperability and standards
			7.5.4 Smart contract security
			7.5.5 Edge computing and blockchain integration
			7.5.6 Governance models
			7.5.7 AI and machine learning
		7.6 Conclusion
		References
	8 Data traffic management in AI-IoT network to reduce congestion
		8.1 Introduction
		8.2 Literature
		8.3 Objectives of data traffic management in AI-IoT network
		8.4 Significance of data traffic management in AI-IoT networks
			8.4.1 Optimal network performance
			8.4.2 Reliable data transmission
			8.4.3 Enhanced quality of service
			8.4.4 Efficient resource utilization
			8.4.5 Scalability and scalable growth
			8.4.6 Improved security and privacy
			8.4.7 Economic and societal impact
			8.4.8 AI-IoT network architecture
		8.5 Overview of AI-IoT networks
			8.5.1 Overview of AI-IoT network components
			8.5.2 Importance of congestion reduction in AI-IoT networks
			8.5.3 Causes of congestion in AI-IoT networks
			8.5.4 Impact of congestion on AI-IoT network performance
			8.5.5 Challenges in managing congestion in AI-IoT networks
		8.6 Data traffic management techniques
			8.6.1 Traffic shaping
			8.6.2 Quality-of-service differentiation
			8.6.3 Load balancing
			8.6.4 Traffic engineering
			8.6.5 Content delivery networks
			8.6.6 Caching
			8.6.7 Packet prioritization
			8.6.8 Deep packet inspection
			8.6.9 Adaptive and machine learning–based techniques
			8.6.10 Policy-based traffic management
		8.7 Congestion detection and monitoring
			8.7.1 Network traffic analysis
			8.7.2 Packet delay measurement
			8.7.3 Queue length monitoring
			8.7.4 Bandwidth utilization monitoring
			8.7.5 Performance metrics analysis
			8.7.6 Flow-level monitoring
			8.7.7 Anomaly detection
			8.7.8 Real-time monitoring and alarms
			8.7.9 Probing and active measurement
		8.8 Congestion control and mitigation strategies
			8.8.1 Congestion control algorithms for AI-IoT networks
		8.9 Dynamic resource allocation
			8.9.1 Elasticity
			8.9.2 Monitoring and load balancing
			8.9.3 Auto-scaling
			8.9.4 Virtualization
			8.9.5 Resource scheduling
			8.9.6 Reservation and preemption
			8.9.7 Predictive analytics
			8.9.8 Intelligent routing and network optimization
		8.10 Case studies and best practices
			8.10.1 Edge computing and local processing
			8.10.2 Data filtering and aggregation
			8.10.3 Traffic prioritization and QoS
			8.10.4 Dynamic resource allocation and load balancing
			8.10.5 Predictive analytics and machine learning
			8.10.6 Protocol optimization
		8.11 Successful implementations of data traffic management in AI-IoT networks
			8.11.1 Smart grid systems
			8.11.2 Connected vehicles
			8.11.3 Smart health care systems
			8.11.4 Industrial AI-IoT applications
			8.11.5 Smart cities
		8.12 Lessons learned from congestion reduction initiatives
			8.12.1 Comprehensive approach
			8.12.2 Data-driven decision-making
			8.12.3 Multimodal transportation
			8.12.4 Demand management strategies
			8.12.5 Intelligent traffic management systems
			8.12.6 Public engagement and communication
			8.12.7 Continuous evaluation and adaptation
		8.13 Best practices for efficient data traffic management
			8.13.1 Predictive traffic routing
			8.13.2 Dynamic bandwidth allocation
			8.13.3 Network slicing
			8.13.4 Peer-to-peer communication
			8.13.5 Dynamic protocol selection
			8.13.6 Traffic off-loading to edge devices
			8.13.7 Application-aware traffic management
			8.13.8 Adaptive multicast
			8.13.9 Dynamic time slot allocation
			8.13.10 User-initiated traffic control
		8.14 Future trends and challenges
			8.14.1 Future trends
			8.14.2 5G network deployment
			8.14.3 AI and machine learning–based traffic optimization
			8.14.4 Security and privacy
			8.14.5 Interoperability and standardization
			8.14.6 Real-time decision-making
		8.15 Conclusion
		References
	9 Artificial intelligence–enabled anomaly IDS for IoT network: trends, solutions, and challenges
		9.1 Introduction
		9.2 Literature study
		9.3 AI-enabled anomaly intrusion detection system for IoT network
		9.4 Machine learning–based anomaly IDS
			9.4.1 SVM-enabled cyberattack detection for IoT network
			9.4.2 Decision tree–enabled cyberattack detection for IoT network
			9.4.3 Random forest–enabled cyberattack detection for IoT network
			9.4.4 Logistic regression–based cyberattack detection for IoT network
		9.5 AI-based anomaly IDS applications
			9.5.1 AI-based anomaly IDS for smart cities
			9.5.2 AI-based anomaly IDS for future transportation
			9.5.3 AI-based anomaly IDS for smart grid
		9.6 AI-enabled anomaly detection use cases
			9.6.1 AI-anomaly detection for health care industry
			9.6.2 AI-anomaly detection for banking
			9.6.3 AI-anomaly detection for defense and government
		9.7 Reducing false positive rate using AI
		9.8 Future trends and challenges
		9.9 Conclusion
		References
Part III Big data analytics applying in current applications
	10 Big data intelligence in health care
		10.1 Introduction
		10.2 Alzheimer’s disease
			10.2.1 AI for AD diagnosis
		10.3 Cardiovascular disease
			10.3.1 AI for CVD diagnosis
			10.3.2 Cardiac imaging analysis
		10.4 Artificial intelligence in cancer disease
			10.4.1 Magnetic resonance imaging
			10.4.2 Future research in cancer disease
		10.5 Diabetes diseases
			10.5.1 AI in diabetes disease
			10.5.2 Future AI prediction in diabetes
		10.6 Tuberculosis diseases
			10.6.1 Pulmonary tuberculosis detection
			10.6.2 Future AI predictions in tuberculosis diseases
		10.7 Stroke detection
			10.7.1 Using AI in stroke disease
		10.8 Hypertension disease detection
			10.8.1 Hypertension disease detection using AI
		10.9 Skin disease
			10.9.1 AI in skin disease
			10.9.2 Future detection in skin disease
		10.10 Chronic liver disease detection
			10.10.1 AI for liver disease
			10.10.2 Radiology image analysis
			10.10.3 Standardization of image analysis
		10.11 Conclusion
		References
	11 Trends and challenges in harnessing big data intelligence for health care transformation
		11.1 Introduction
		11.2 Understanding big data in health care
			11.2.1 Definition and characteristics of big data
			11.2.2 Sources of big data in health care
		11.3 The role of big data intelligence in health care
			11.3.1 Improving patient care and outcome
			11.3.2 Advancing medical research and drug discovery
			11.3.3 Enhancing clinical decision-making
			11.3.4 Transforming health care operations and management
		11.4 Big data analytics in health care
			11.4.1 Data mining and pattern recognition
			11.4.2 Machine learning and artificial intelligence in health care
			11.4.3 Natural language processing for health care data
		11.5 Applications of big data in specific health care areas
			11.5.1 Big data in diagnosis and imaging
			11.5.2 Big data in personalized medicine and genomics
			11.5.3 Big data in health care IoT and wearables
		11.6 Trends and opportunities
			11.6.1 The evaluation of big data in health care
			11.6.2 Emerging technologies and innovations
		11.7 Challenges and future directions
			11.7.1 Storage
			11.7.2 Security
			11.7.3 Opacity of infrastructure
			11.7.4 Data uncertainty
			11.7.5 Ethical and legal issues
		11.8 Conclusion
		References
	12 Improving hepatitis C diagnosis using machine learning techniques: an experimental analysis
		12.1 Introduction
		12.2 Literature review
		12.3 Methodology
			12.3.1 Data collection
			12.3.2 Data preprocessing
		12.4 Experimental result
		12.5 Conclusion and future work
		References
	13 A step toward the detection of alzheimer’s disease using ensemble learning
		13.1 Introduction
		13.2 Literature review
		13.3 Methodology
			13.3.1 Dataset
			13.3.2 Ensemble method
		13.4 Results and discussion
		13.5 Conclusion
		References
	14 Exploring the use of machine learning algorithms in early detection of liver disease
		14.1 Introduction
		14.2 Literature review
		14.3 Methodology
			14.3.1 Data collection and preprocessing
		14.4 Data preprocessing
		14.5 Result and analysis
		14.6 Discussion
		14.7 Conclusion and future work
		References
	15 Intelligent transportation channels for smart cities
		15.1 Introduction
		15.2 Literature
		15.3 Transportation in smart city development
			15.3.1 Importance of transportation in smart city development
			15.3.2 Key goals and objectives of intelligent cities
		15.4 Intelligent transportation channels
			15.4.1 Traffic management systems
			15.4.2 Intelligent traffic control systems
			15.4.3 Incident management systems
			15.4.4 Public transportation systems
			15.4.5 Intelligent parking systems
			15.4.6 Connected vehicles
			15.4.7 Traveler information systems
			15.4.8 How intelligent transportation channels contribute to smart city goals
			15.4.9 Benefits and advantages of implementing intelligent transportation channels
			15.4.10 Technologies enabling intelligent transportation channels
			15.4.11 Artificial intelligence and machine learning in transportation
			15.4.12 Applications of AI and ML in traffic prediction, congestion management, and autonomous vehicles
			15.4.13 Challenges and ethical considerations in AI implementation for transportation
		15.5 Intelligent traffic management systems
			15.5.1 Components of intelligent traffic management systems
			15.5.2 Traffic signal control and optimization
			15.5.3 Incident detection and management
		15.6 Smart mobility solutions
			15.6.1 Connected and autonomous vehicles
			15.6.2 Autonomous vehicles
		15.7 Sustainable transportation solutions
			15.7.1 Electric mobility
			15.7.2 Smart parking systems
			15.7.3 Bike-sharing and micro-mobility solutions
			15.7.4 Flexibility and convenience
		15.8 Challenges and future directions
			15.8.1 Data privacy and security
			15.8.2 Integration and interoperability
			15.8.3 Future trends and emerging technologies
		15.9 Conclusion
		References
	16 Digital twins for industry 4.0 and 5.0
		16.1 Introduction
		16.2 What makes industry 5.0 different from industry 4.0?
		16.3 DT—an emerging technology for industry 4.0 and industry 5.0
			16.3.1 DT technology for manufacturing industry
			16.3.2 Indispensable role of DT technology in industry 4.0 and 5.0
			16.3.3 Case study on DT for industry 4.0 and 5.0
		16.4 Use cases of DT technology for industry 4.0 and 5.0
			16.4.1 Industry 4.0
			16.4.2 Industry 5.0
		16.5 Key components of DT technology
		16.6 Types of DTs being used in industries
		16.7 Benefits of DT technology in manufacturing industries
		16.8 Challenges in implementation of DT technology
		16.9 Recommendations for the successful adoption of DT technology for industry 4.0 and 5.0
		16.10 Conclusion
		References
Index