Artificial Intelligence: Models, Algorithms and Applications

Table of Contents

Chapter 1 From AIS Data to Vessel Destination Through Prediction

• With Machine Learning Techniques
• Wells Wang, Chengkai Zhang, Fabien Guillaume, Richard Halldearn, Terje Solsvik
• Kristensen and Zheng Liu
• Introduction
• AIS Data Preprocessing Approach
• Trajectory Extraction
• Trajectory Resampling
• Noise Filtering
• Trajectory Segmentation
• Vessel Destination Prediction Approaches
• Sequence Prediction Approach
• Classification Approach
• Classification of Ports
• Classification of Trajectories
• Concluding Remarks
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 2 Artificial Intelligence in Mental Health

• Suresh Kumar Mukhiya, Amin Aminifar, Fazle Rabb, Violet Ka I. Pun And
• Yngve Lamo
• Introduction
• Mental Health Treatment
• Motivation
• Adaptiveness and Adherence
• Automation of the Treatment Process
• Scalability
• Personal Stigma (Self-Aware Treatment Systems)
• Ai for a Personalized Recommendation
• Data Collection and Preparation
• Challenges in Data Collection
• Mental Health and Ai
• Natural Language Processing (NLP)
• Virtual Reality (VR) and Augmented Reality (AR)
• Affective Computing
• Robotics
• Brain Computer Interface (BCI)
• Machine Perception and Ambient Intelligence
• Challenges
• Technical Issues
• Security and Privacy Issues
• Ethical Issues
• Design Issues
• Discussion About Future Development
• Conclusion
• Notes
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 3 Deep Learning in Radiology

• Madhura Ingalhalikar
• Introduction
• Motivation
• Deep Learning in Radiology
• Diagnostic Predictions
• Detecting Abnormalities on Chest X-Rays
• Screening for Lung Cancer on Low Dose Ct
• Genotype Detection in Gliomas on Multi-Modal MRI
• Prostrate Cancer Detection
• Segmentation
• 2D and 3D CNNS
• U-Nets
• Registration
• Image Generation
• Other Applications
• Limitations and Ways Forward
• Conclusion
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 4 Ai in Instrumentation Industry

• Ajay V. Deshmukh
• Introduction
• A Systematic Approach to Applied Ai
• Artificial Intelligence and Its Need
• Ai in Chemical Process Industry
• Ai in Manufacturing Process Industry
• Ai for Quality Control
• Ai in Process Monitoring
• Ai in Plant Safety
• Conclusion
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 5 Ai in Business and Education

• Tarjei Alvær Heggernes
• Introduction
• The Industrial Revolution and the Long Economic Waves
• Artificial Intelligence and Industry 4.0
• What Can Ai Do?
• Definitions
• Machine Learning
• Sense, Understand and Act
• How Do Systems Learn?
• Deep Learning and Neural Networks
• Generative Adversary Networks
• Ai in Business Operations
• Ai in Business Management
• Ai in Marketing
• Use of Reinforcement Learning in Real-Time Auctions for Online Advertising
• Ai in Education
• Systems for Intelligent Tutoring and Adaptive Learning
• Evaluation of Assignments with Neural Networks
• Conclusion
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 6 Extreme Randomized Trees for Real Estate Appraisal With

• Housing and Crime Data
• Junchi Bin, Bryan Gardiner, Eric Li and Zheng Liu
• Introduction
• Related Works
• Machine Learning in Real Estate Appraisal
• Real Estate Appraisal Beyond House Attributes
• Methodology
• Overall Architecture of Proposed Method
• Data Collection and Description
• House Attributes
• Comprehensive Crime Intensity
• Extremely Randomized Trees
• Experiments
• Experimental Setup
• Evaluation Metrics
• Performance Comparison
• Conclusions
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 7 The Knowledge-Based Firm and Ai

• Ove Rustung Hjelmervik and Terje Solsvik Kristensen
• Introduction
• Ai - a Creative Destruction Technology
• Schumpeter's Disruptive Technology and Radical Innovation
• It and the Productivity Paradox
• Alan Turing's Disruptive Research and Innovation
• Turing Machine
• Turing Test
• Problem Solving
• Turing's Connectionism
• Gødel and Ai
• The Knowledge-Based Organization
• The Resource-Based View of the Firm
• Organizational Learning
• Bounded Rationality
• Discussion
• Conclusion
• Notes
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References

Chapter 8 a Mathematical Description of Artificial Neural Networks 117

• Hans Birger Drange
• Introduction
• Artificial Neural Networks, Ann
• Neurons in the Brain
• A Mathematical Model
• The Synapse
• A Mathematical Structure
• The Network as a Function
• Description of the Weights
• Turning to the Matrices Themselves
• The Functions of the Network
• The Details of What the Functions Fk Do to Their Arguments
• Study of the Function F of the Whole Network
• Determination of the Correct Weight Matrices
• The Actual Mathematical Objects That We Manipulate
• Perceptron
• A Special Notation for Two Layers and an Output Layer of Only One Neuron
• Training of the Network
• About the Threshold B
• Not All Logic Functions Can be Defined by a Simple Perceptron
• Solving Pattern Classification with a Simple Perceptron
• A Geometric Criterion for the Solution of the Classification Problem
• Regression as a Neural Network
• Solving by Standard Linear Regression
• Solving by Using the Perceptron
• A Little More About the Learning Rate and Finding the Minimum
• Multilayer Perceptrons, MLP
• Backpropagation
• Computation of the Weight Updates
• Updates for the Weights in the First Layer of Connections
• Definition of the Local Error Signals
• Updates of the Weights in the Second Layer of Connections
• The Final Conclusion
• Propagation of the Error Signals
• Updating the Weights for All Layers of Weights
• Using Number Indices
• Finding the Weights Themselves
• Conclusion
• Notes
• Consent for Publication
• Conflict of Interest
• Acknowledgements
• References
• Subject Index

Author

• Terje Solsvik Kristensen