Emerging Artificial Intelligence Applications in Computer Engineering 1st Edition by Ilias Maglogiannis, Kostas Karpouzis, Manolis Wallace, John Soldatos ISBN 1586037803 9781586037802

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ISBN 10: 1586037803 
ISBN 13: 9781586037802
Author: Ilias Maglogiannis, Kostas Karpouzis, Manolis Wallace, John Soldatos

The ever expanding abundance of information and computing power enables researchers and users to tackle highly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information, based on user preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of this book is to provide insights on how today’s computer engineers can implement AI in real world applications. Overall, the field of artificial intelligence is extremely broad. In essence, AI has found applications, in one way or another, in every aspect of computing and in most aspects of modern life.Consequently, it is not possible to provide a complete review of the field in the framework of a single book, unless if the review is broad rather than deep. In this book we have chosen to present selected current and emerging practical applications of AI, thus allowing for a more detailed presentation of topics. The book is organized in four parts: General Purpose Applications of AI; Intelligent Human-Computer Interaction; Intelligent Applications in Signal Processing and eHealth; and, Real world AI applications in Computer Engineering.

Emerging Artificial Intelligence Applications in Computer Engineering 1st Table of contents:

Chapter 1: Introduction to Artificial Intelligence
1.1 What is Artificial Intelligence?
1.2 History and Evolution of AI
1.3 Key Concepts in AI
1.4 AI vs. Machine Learning vs. Deep Learning
1.5 AI Techniques and Algorithms
1.6 Challenges and Opportunities in AI Development

Chapter 2: Fundamentals of AI in Computer Engineering
2.1 Basics of Machine Learning
2.2 Neural Networks and Deep Learning
2.3 Natural Language Processing (NLP)
2.4 Computer Vision and Image Processing
2.5 Reinforcement Learning and Autonomous Systems
2.6 AI Tools and Frameworks in Computer Engineering

Chapter 3: AI for Hardware Design and Optimization
3.1 AI in Chip Design and Manufacturing
3.2 AI for Hardware Testing and Fault Detection
3.3 Quantum Computing and AI
3.4 AI-Driven Hardware Acceleration (e.g., GPUs, TPUs)
3.5 AI-Based Power Consumption Optimization
3.6 Smart Sensors and Edge Computing

Chapter 4: AI in Software Engineering and Development
4.1 Automating Software Testing and Debugging with AI
4.2 Code Generation and Optimization Using AI
4.3 AI for Software Maintenance and Enhancement
4.4 Natural Language Processing for Code Search and Documentation
4.5 AI in DevOps: Automating Deployment and Monitoring
4.6 Smart Software Debugging and Error Prediction

Chapter 5: AI in Embedded Systems and IoT
5.1 AI-Driven Smart Devices and IoT
5.2 Edge Computing and AI Integration in IoT
5.3 Real-Time Data Processing with AI in Embedded Systems
5.4 Autonomous IoT Systems and Smart Homes
5.5 AI for Predictive Maintenance in IoT Devices
5.6 Security Challenges and Solutions in AI-Based IoT Systems

Chapter 6: AI in Computer Networks
6.1 AI for Network Traffic Analysis and Management
6.2 Machine Learning for Network Optimization and Automation
6.3 AI for Cybersecurity and Intrusion Detection
6.4 AI-Driven Network Management in 5G and Beyond
6.5 Optimizing Network Routing and Load Balancing with AI
6.6 AI in Wireless Networks and Communication Systems

Chapter 7: AI in Robotics and Autonomous Systems
7.1 AI for Robot Perception and Navigation
7.2 Machine Learning Algorithms for Autonomous Systems
7.3 AI in Drone Technology and Control Systems
7.4 Autonomous Vehicles and AI Algorithms
7.5 Human-Robot Interaction and Collaboration
7.6 Safety and Ethics in AI-Driven Robotics

Chapter 8: AI in Cybersecurity
8.1 AI-Powered Threat Detection and Response
8.2 Using AI for Malware and Phishing Detection
8.3 Intrusion Detection Systems and AI
8.4 AI for Privacy and Data Protection
8.5 Secure Authentication and AI Algorithms
8.6 Blockchain and AI for Cybersecurity

Chapter 9: AI in Data Science and Analytics
9.1 Predictive Analytics and Machine Learning
9.2 AI for Big Data Processing and Visualization
9.3 Decision Support Systems Powered by AI
9.4 AI and Data Mining for Engineering Applications
9.5 AI for Real-Time Analytics in Engineering Systems
9.6 Ethical Considerations in AI and Data Science

Chapter 10: Future Trends and Challenges in AI for Computer Engineering
10.1 The Role of AI in Future Computing Architectures
10.2 Emerging Applications of AI in Cloud Computing
10.3 The Integration of AI and Blockchain in Engineering
10.4 Advancements in AI for Quantum Computing
10.5 Ethical, Legal, and Social Implications of AI in Engineering
10.6 The Future of Autonomous Systems and AI in Engineering

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