Cognitive Machine Intelligence : Applications, Challenges, and Related Technologies

Cognitive Machine Intelligence Applications, Challenges, and Related Technologies

"Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies" offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing.

Detalles Bibliográficos
Otros Autores: Khan, Inam Ullah, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2025]
Edición:First edition
Colección:Intelligent Data-Driven Systems and Artificial Intelligence Series
Materias:
Artificial intelligence > Industrial applications.
Computer networks > Technological innovations.
Machine learning.
Medical care > Data processing.
Smart cities.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869091006719
Tabla de Contenidos:
  • 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.

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