Cognitive Machine Intelligence : Applications, Challenges, and Related Technologies / edited by Inam Ullah Khan [and four others].

Format:

Book

Contributor:

Khan, Inam Ullah, editor.

Series:

Intelligent Data-Driven Systems and Artificial Intelligence Series

Language:

English

Subjects (All):

Artificial intelligence--Industrial applications.

Artificial intelligence.

Computer networks--Technological innovations.

Computer networks.

Machine learning.

Medical care--Data processing.

Medical care.

Smart cities.

Physical Description:

1 online resource (373 pages)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, [2025]

Summary:

"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.

Contents:

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

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

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

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

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

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

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.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781040097083

1040097081

9781003500865

1003500862

9781040097106

1040097103

OCLC:

1452467198