Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies (Intelligent Data-Driven Systems and Artificial Intelligence)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editors
List of contributors
Preface
Part I: AI trends and challenges
	Chapter 1: AI-based computing applications in future communication
		1.1 Introduction
		1.2 Artificial Intelligence
			1.2.1 Why is artificial intelligence important?
		1.3 Artificial and social networks
			1.3.1 Network theory
			1.3.2 Network analysis
		1.4 Scholarly investigation into social network intelligence
		1.5 AI as it is portrayed in the media
			1.5.1 2013: AlexNet and variational autoencoders
			1.5.2 In 2018
			1.5.3 Last three year’s review
		1.6 Latest developments in AI
			1.6.1 Computer vision
			1.6.2 Features of computer vision
			1.6.3 AI in education
			1.6.4 AI-optimized hardware
		1.7 Definition of artificial superintelligence (ASI)
			1.7.1 The state of artificial intelligence at the moment
		1.8 The future of digital communications using AI
		1.9 The benefits of AI-powered automation for digital communication
			1.9.1 Increased efficiency
			1.9.2 Improved accuracy
			1.9.3 Enhanced personalization
			1.9.4 Increased security
		1.10 How does AI impact digital communications?
			1.10.1 Artificial Intelligence’s effect on communication
		1.11 What’s next for AI in digital communications?
			1.11.1 Source
			1.11.2 Input transducer
			1.11.3 Encoder of source
			1.11.4 Encoder of channels
		1.12 Prediction for the future of digital communications
			1.12.1 In-app messaging becomes dominant
			1.12.2 VR adoption: Make or break
			1.12.3 The need for human contact and validation
		1.13 What will the future of AI look like?
		1.14 Few predictions for AI
			1.14.1 In 2030
			1.14.2 In 2050
		1.15 Predictions on future technologies
			1.15.1 Robotics
			1.15.2 Augmented reality and virtual reality
			1.15.3 Nanotech
			1.15.4 Space exploration
			1.15.5 Superconductors
			1.15.6 3D printing
			1.15.7 Autonomous vehicle
		1.16 Conclusion
		References
	Chapter 2: Advances of deep learning and related applications
		2.1 Introduction
		2.2 Deep learning techniques
		2.3 Multilayer perceptron
		2.4 Convolutional neural network
		2.5 Recurrent neural network
		2.6 Long-term short-term memory
		2.7 GRU
		2.8 Autoencoders
		2.9 Attention mechanism
		2.10 Deep generative models
		2.11 Restricted Boltzmann machine
		2.12 Deep belief network
		2.13 Modern deep learning platforms
			2.13.1 PyTorch
			2.13.2 TensorFlow
			2.13.3 Keras
			2.13.4 Caffe (Convolutional architecture for fast feature embedding) and Caffe2
			2.13.5 Deeplearning4j
			2.13.6 Theano
			2.13.7 Microsoft cognitive toolkit (CNTK)
		2.14 Challenges of deep learning
		2.15 Applications of deep learning
		2.16 Conclusion
		References
	Chapter 3: Machine learning for big data and neural networks
		3.1 Introduction
		3.2 Machine learning and fundamentals
			3.2.1 Supervised learning
			3.2.2 Decision trees
			3.2.3 Linear regression
			3.2.4 Naive Bayes
			3.2.5 Logistic regression
		3.3 Unsupervised learning
			3.3.1 K-Means algorithm
			3.3.2 Principal component analysis
		3.4 Reinforcement learning
		3.5 Machine learning in large-scale data
		3.6 Data analysis in big data
		3.7 Predictive modelling
			3.7.1 Understanding customer behavior and preferences
			3.7.2 The role of supply chain and performance management in organizational success
			3.7.3 Management of quality and enhancement
			3.7.4 Risk mitigation and detection of fraud
		3.8 Neural networks
			3.8.1 Artificial neural network
			3.8.2 RNN
			3.8.3 CNN
			3.8.4 Deep learning using convolutional neural networks to find building defects
		3.9 Data generation and manipulation
			3.9.1 Generative Adversarial Networks
			3.9.2 Domains of real-world applications
			3.9.3 Financial applications
			3.9.4 Medical and data science
			3.9.5 Internet of Things
		3.10 Conclusion
		References
Part II: Machine intelligence in network technologies
	Chapter 4: Deformation prediction and monitoring using real-time WSN and machine learning algorithms: A review
		4.1 Introduction
		4.2 Causes of landslides
			4.2.1 Climate changes
			4.2.2 Earthquake
			4.2.3 Deforestation
		4.3 Early warning system
			4.3.1 Risk Knowledge
			4.3.2 Monitoring and warning services
			4.3.3 Dissemination and communication
			4.3.4 Response capability
			4.3.5 Classification of early warning system
		4.4 Landslide monitoring techniques
			4.4.1 Multi-antenna GPS deformation monitoring systems
			4.4.2 Monitoring landslide deformation using InSAR Technique
			4.4.3 Electro-Mechanical System (MEMS) tilt sensor
			4.4.4 Low-cost vibration sensor network
			4.4.5 Extensometer
			4.4.6 Rain gauge
		4.5 Landside prediction modeling and forecasting using machine learning and statistical analysis
		4.6 Conclusion
		Acknowledgments
		References
	Chapter 5: Unmanned aerial vehicle: Integration in healthcare sector for transforming interplay among smart cities
		5.1 Introduction
			5.1.1 Objectives of the chapter
			5.1.2 Significance of study
		5.2 UAVs in healthcare: Applications and benefits
			5.2.1 Specific applications of UAVs in healthcare sector
				5.2.1.1 Transportation
				5.2.1.2 Livestock monitoring
				5.2.1.3 Disaster relief
				5.2.1.4 Public health surveillance and medical research
			5.2.2 Benefits of UAVs in healthcare sector
		5.3 Communication protocols for UAVs in healthcare
			5.3.1 Diverse communication protocols suitable for UAVs in healthcare settings
			5.3.2 Addressing challenges and requirements of real-time data transmission
		5.4 Deployment strategies and logistics
			5.4.1 Different deployment strategies for UAVs in healthcare
			5.4.2 Logistical considerations
		5.5 Security challenges and solutions
			5.5.1 Security challenges associated with UAVs in healthcare
			5.5.2 Potential solutions and mitigation strategies
			5.5.3 Importance of regulatory compliance and adherence to safety standards
		5.6 Regulatory and legal framework
			5.6.1 Need for standardized regulations and guidelines to ensure safe and ethical use of UAVs
		5.7 Conclusion and future scope
		References
	Chapter 6: Blockchain technologies using machine learning
		6.1 Introduction
		6.2 Understanding blockchain technologies
			6.2.1 Introduction to blockchain
			6.2.2 Key components of a blockchain network
			6.2.3 Consensus mechanisms and their impact
			6.2.4 Benefits and limitations of BCT
				6.2.4.1 Benefits of BCT
				6.2.4.2 Limitations of BCT
		6.3 ML fundamentals
			6.3.1 Overview of ML
			6.3.2 Types of ML algorithms
				6.3.2.1 Supervised learning algorithms
				6.3.2.2 Unsupervised learning algorithms
				6.3.2.3 Semi-supervised learning algorithms
				6.3.2.4 Reinforcement learning algorithms
				6.3.2.5 Deep learning algorithms
			6.3.3 Data pre-processing and feature engineering
				6.3.3.1 Data pre-processing
				6.3.3.2 Feature engineering
		6.4 Evaluating ML models
			6.4.1 Common evaluation metrics
		6.5 Synergies between blockchain and ML
			6.5.1 Combining ML models on the blockchain
		6.6 Applications of blockchain and ML integration
		6.7 Challenges and limitations in BCT and ML integration
			6.7.1 Scalability issues
			6.7.2 Data availability and quality
			6.7.3 Regulatory and legal challenges
			6.7.4 Trusted oracles and data feeds
			6.7.5 Energy efficiency concerns
		6.8 Future prospects and research directions
			6.8.1 Federated learning on blockchain networks
			6.8.2 Integration of privacy-preserving techniques
			6.8.3 AI-driven smart contracts
		6.9 Conclusion
		References
	Chapter 7: Q-learning and deep Q networks for securing IoT networks, challenges, and solution
		7.1 Introduction
		7.2 Methodology
			7.2.1 Proposed algorithm for training DQNs as agents in IoT networks for security
				7.2.1.1 The algorithm
				7.2.1.2 Program
				7.2.1.3 Various security actions
			7.2.2 Algorithm for applying security actions using a DQN in IoT network security
				7.2.2.1 Program
		7.3 Result and conclusion
		References
	Chapter 8: The application of artificial intelligence and machine learning in network security using a bibliometric study
		8.1 Introduction
		8.2 Analysis of state-of-art network security AI/ML models
			8.2.1 Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection
			8.2.2 A novel online incremental and decremental learning algorithm based on variable support vector machine
			8.2.3 An effective intrusion detection framework based on SVM with feature augmentation, knowledge-based systems
			8.2.4 A novel hybrid KPCA and SVM with GA model for intrusion detection
			8.2.5 A novel SVM-KNN-PSO ensemble method for intrusion detection system
			8.2.6 SVM-DT-based adaptive and collaborative intrusion detection
			8.2.7 Random forest modeling for network intrusion detection system
		8.3 Analysis of the state-of-art malware detection AL/ML models
			8.3.1 Malware detection classification using machine learning
			8.3.2 A review of Android malware detection approaches based on machine learning
			8.3.3 A two-layer deep learning method for Android malware detection using network traffic
			8.3.4 A lightweight network-based Android malware detection system
			8.3.5 Phishing website classification and detection using machine learning
			8.3.6 Static and dynamic malware analysis using machine learning
		8.4 Research findings in AI/ML-based network security models
		8.5 Research findings in AL/ML-based malware detection systems
		8.6 Conclusion
		References
	Chapter 9: Machine learning approaches for intrusion detection: Enhancing cybersecurity and threat mitigation
		9.1 Introduction
		9.2 Traditional intrusion detection methods
		9.3 Machine learning algorithms for intrusion detection
		9.4 Related works
		9.5 Addressing the research gap: Adaptive intrusion detection
		9.6 Challenges of integrating machine learning in IDS
		9.7 Feature engineering for intrusion detection
		9.8 Enhancing robustness with ensemble learning
		9.9 Future research directions
		9.10 Conclusion
		References
Part III: Cognitive machine intelligence applications
	Chapter 10: The rise of AI in the field of healthcare
		10.1 Introduction
		10.2 2 Types of AI
			10.2.1 AI type 1: skill base
				10.2.1.1 Weak AI or narrow AI
				10.2.1.2 General-purpose AI
				10.2.1.3 Super AI
			10.2.2 AI Type: function-based
				10.2.2.1 Reactive apparatus
				10.2.2.2 Memory machines with limited memory
				10.2.2.3 Mind theory
				10.2.2.4 Confidence
		10.3 Features of artificial intelligence
			10.3.1 Eliminate monotonous and tedious tasks
			10.3.2 Data acquisition
			10.3.3 A copy of human cognition
			10.3.4 Avoid natural disaster
			10.3.5 Chatbots and facial recognition
		10.4 Artificial intelligence: unraveling the shade of innovation
			10.4.1 Machine learning: the art of adaptation
			10.4.2 Neural networks: mirroring the human brain
			10.4.3 Deep learning: navigating complexity
			10.4.4 Natural language processing
			10.4.5 Computer vision: seeing the unseen
			10.4.6 Reinforcement learning: learning from experience
			10.4.7 Generative adversarial networks: fostering creativity
			10.4.8 Explainable AI: illuminating the black box
			10.4.9 Ethics and bias in AI: navigating a moral compass
			10.4.10 Artificial general intelligence: the quest for human-level AI
			10.4.11 Quantum AI: bridging new realities
			10.4.12 AI in creativity: collaborating with machines
			10.4.13 AI in finance: predicting the economic future
			10.4.14 AI and climate change: a greener tomorrow
		10.5 Revolutionizing healthcare through technology: a comprehensive overview
			10.5.1 Introduction: healthcare in the digital age
			10.5.2 AI in diagnostics: enhancing precision and early detection
			10.5.3 Personalized medicine: tailoring treatment to individuals
			10.5.4 Drug discovery and development: accelerating breakthroughs
			10.5.5 Electronic health records and AI: enabling informed decision-making
			10.5.6 Telemedicine and virtual health assistants: expanding access to care
			10.5.7 Robotics in surgery: advancing precision and minimally invasive procedures
			10.5.8 Mental health and AI: revolutionizing approaches
			10.5.9 Ethical considerations: balancing progress and privacy
			10.5.10 The human touch: AI as a collaborator
		10.6 Conclusion
		References
	Chapter 11: A comprehensive survey of machine learning applications in healthcare
		11.1 Introduction
		11.2 Machine learning in healthcare
			11.2.1 Machine learning algorithms in healthcare
			11.2.2 Supervised, unsupervised, and reinforcement learning techniques
				11.2.2.1 Supervised learning
					11.2.2.1.1 Support vector machines
					11.2.2.1.2 Random Forest
					11.2.2.1.3 Neural Networks
					11.2.2.1.4 K-Nearest neighbours
					11.2.2.1.5 Gaussian Naive Bayes
				11.2.2.2 Unsupervised learning
				11.2.2.3 Reinforcement learning
		11.3 Medical imaging and diagnostic applications
			11.3.1 Image classification and segmentation
			11.3.2 Computer-aided detection and diagnosis
			11.3.3 Radiomics and radiogenomics in cancer diagnosis
			11.3.4 Neuroimaging for brain disorder diagnosis
		11.4 Clinical decision support systems
			11.4.1 ML-driven risk prediction models
			11.4.2 Decision support for treatment planning
			11.4.3 Early warning systems for patient deterioration
		11.5 Electronic Health Records analysis
			11.5.1 Predictive modelling using EHR data
			11.5.2 Natural Language Processing for extracting medical information
			11.5.3 Clinical data integration and interoperability
		11.6 Disease prediction and prevention
			11.6.1 ML-based models for disease risk assessment
			11.6.2 Predictive analytics for patient outcomes
			11.6.3 Population health management using ML
		11.7 Personalised medicine and treatment
			11.7.1 Pharmacogenomics and drug response prediction
			11.7.2 Precision oncology and targeted therapies
			11.7.3 Individualised treatment recommendations
		11.8 Drug discovery and development
			11.8.1 AI-driven drug screening and design
			11.8.2 ML in clinical trials and drug efficacy evaluation
			11.8.3 Repurposing existing drugs with ML
		11.9 Ethical, legal, and privacy considerations
			11.9.1 Ethical challenges in using ML in healthcare
				11.9.1.1 Fairness and bias
				11.9.1.2 Transparency and explainability
				11.9.1.3 Informed consent
				11.9.1.4 Data security and privacy
				11.9.1.5 Clinical validation
				11.9.1.6 AI can assist in making medical decisions
			11.9.2 Legal implications and regulatory frameworks
			11.9.3 Privacy-preserving ML techniques for healthcare data
		11.10 Challenges and future directions
			11.10.1 Data quality, quantity, and interoperability
			11.10.2 Interpretability and explainability of ML models
			11.10.3 Integration of ML algorithms into clinical workflows
			11.10.4 Addressing bias and fairness in healthcare AI
		11.11 Conclusion
		References
	Chapter 12: A deep learning approach for the early diagnosis of melanoma cancer: Study and analysis
		12.1 Introduction
		12.2 Relevant work
		12.3 Theoretical framework
		12.4 Proposed methodology
		12.5 Results
			12.5.1 Results of identification of melanoma cancer using dermatoscopy by physicians
			12.5.2 Results of identification of melanoma cancer by CNN
		12.6 Conclusion
		References
	Chapter 13: A study and analysis on nowcasting: Forms of precipitation using improvised random forest classifier
		13.1 Introduction
		13.2 Relevant work
		13.3 IRFC model for weather forecasting
			13.3.1 Dataset used
			13.3.2 Data preprocessing
			13.3.3 Training set
			13.3.4 Testing set
			13.3.5 Proposed model: Random forest
			13.3.6 Model evaluation
		13.4 Results and discussion
		13.5 Conclusion
		References
	Chapter 14: A study and comparative analysis on prediction of tsunami using convolutional neural network
		14.1 Introduction
		14.2 Relevant work
		14.3 Proposed methodology
			14.3.1 Architecture
			14.3.2 Dataset description
			14.3.3 Data preprocessing
			14.3.4 Training dataset
			14.3.5 Testing dataset
			14.3.6 CNN model
			14.3.7 Model evaluation
		14.4 Results and discussions
			14.4.1 Accuracy
			14.4.2 Sensitivity
			14.4.3 Specificity
			14.4.4 Precision
		14.5 Conclusion
		References
	Chapter 15: Towards smarter Chatbots: Unravelling the capabilities of ChatGPT
		15.1 Introduction
		15.2 ChatGPT summary compilation
			15.2.1 Background of ChatGPT
		15.3 Architecture of ChatGPT
		15.4 Training ChatGPT
			15.4.1 Data sources used in training ChatGPT
		15.5 Applications of ChatGPT
			15.5.1 Advantages
			15.5.2 Disadvantages
		15.6 Conclusion
		References
Index