Recent Advances in Artificial Intelligence Research and Development 1st edition by Jordi Vitrià , Petia Radeva, Isabel Aguiló ISBN 1586034669 978-1586034665
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Authors:Jordi Vitrià ; Petia Radeva; Isabel Aguiló , Series:Artificial Intelligence [4] , Tags:Computers; Artificial Intelligence; General , Author sort:Vitrià , Jordi & Radeva, Petia & Aguiló, Isabel , Ids:Google; 9781586034665 , Languages:Languages:eng , Published:Published:Apr 2004 , Publisher:IOS Press , Comments:Comments:Artificial Intelligence (AI) is a scientific field of longstanding tradition, with origins in the early years of computer science. Today AI has reached a level of maturity that allows us to build highly sophisticated systems which perform very different tasks. Nevertheless, its evolution has opened up a number of new problems, ranging from specific algorithms to system integration, which remain elusive and assure a long life for this research field. Research progress in this area is today an international challenge that must be supported by world-class meetings and organizations, but in spite of this fact, there is also an objective need for meetings and organizations that support and disseminate research at other levels. This book focuses on new and original research on Artificial Intelligence.
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ISBN 10: 1586034669
ISBN 13: 978-1586034665
Author: Jordi Vitrià, Petia Radeva, Isabel Aguiló
Recent Advances in Artificial Intelligence Research and Development 1st Table of contents:
Part I: Foundations of Artificial Intelligence
Chapter 1: Introduction to Artificial Intelligence
1.1. History and Evolution of AI
1.2. Key Concepts and Definitions
1.3. Approaches to AI: Symbolic vs. Sub-symbolic
1.4. AI in Modern Computing
1.5. AI Applications and Their Impact
Chapter 2: Machine Learning Fundamentals
2.1. Introduction to Machine Learning
2.2. Supervised, Unsupervised, and Reinforcement Learning
2.3. Algorithms and Models
2.4. Data Preprocessing and Feature Engineering
2.5. Evaluation Metrics and Cross-validation
Part II: Advanced AI Techniques
Chapter 3: Deep Learning
3.1. Overview of Neural Networks
3.2. Convolutional Neural Networks (CNNs)
3.3. Recurrent Neural Networks (RNNs) and LSTMs
3.4. Generative Adversarial Networks (GANs)
3.5. Deep Reinforcement Learning
3.6. Applications of Deep Learning
Chapter 4: Natural Language Processing (NLP)
4.1. Introduction to NLP
4.2. Text Representation Techniques
4.3. Language Models and Transformers
4.4. Machine Translation and Sentiment Analysis
4.5. Question Answering and Dialogue Systems
4.6. Challenges and Future Directions in NLP
Chapter 5: Computer Vision
5.1. Image Recognition and Classification
5.2. Object Detection and Tracking
5.3. Image Generation and Enhancement
5.4. Vision for Autonomous Vehicles
5.5. Applications in Healthcare and Surveillance
Chapter 6: Reinforcement Learning
6.1. Introduction to Reinforcement Learning
6.2. Markov Decision Processes (MDPs)
6.3. Policy Gradient Methods
6.4. Deep Q-Learning and AlphaGo
6.5. Applications in Robotics and Game Theory
Part III: AI Applications and Future Trends
Chapter 7: AI in Robotics
7.1. Autonomous Robots and Their Components
7.2. AI for Robot Perception and Control
7.3. Human-Robot Interaction
7.4. AI in Manufacturing and Healthcare Robotics
7.5. Challenges and Ethical Issues in Robotics
Chapter 8: AI in Healthcare
8.1. AI for Medical Diagnosis and Imaging
8.2. Personalized Medicine and Drug Discovery
8.3. AI in Clinical Decision Support Systems
8.4. Wearables and Health Monitoring
8.5. Ethical Considerations in AI for Healthcare
Chapter 9: AI in Finance and Business
9.1. AI Applications in Financial Markets
9.2. Algorithmic Trading and Risk Management
9.3. Customer Service Automation (Chatbots)
9.4. AI in Fraud Detection and Cybersecurity
9.5. Business Intelligence and Decision Support
Chapter 10: AI for Social Good
10.1. AI for Environmental Sustainability
10.2. Disaster Response and Management
10.3. AI in Education and Poverty Alleviation
10.4. Ethical and Social Implications of AI
10.5. AI for Public Policy and Governance
Part IV: Challenges and Future Directions
Chapter 11: Ethics in Artificial Intelligence
11.1. Ethical Challenges in AI Development
11.2. Bias in AI Algorithms
11.3. AI and Privacy Concerns
11.4. Governance, Transparency, and Accountability
11.5. The Role of AI in Society
Chapter 12: Future Trends in AI Research
12.1. Advances in Cognitive Computing
12.2. The Rise of Quantum Computing in AI
12.3. Explainable AI (XAI)
12.4. AI and the Future of Work
12.5. Long-term Vision: Artificial General Intelligence (AGI)
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