CONTENTS
PREFACE
List of Contributors
From AIS Data to Vessel Destination Through Prediction with Machine Learning Techniques
Wells Wang1, Chengkai Zhang1, Fabien Guillaume2, Richard Halldearn3, Terje Solsvik Kristensen4 and Zheng Liu1,
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
Artificial Intelligence in Mental Health
Suresh Kumar Mukhiya1,, Amin Aminifar1, Fazle Rabbi1,3, Violet Ka I. Pun1,2 and Yngve Lamo1
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
Deep Learning in Radiology
Madhura Ingalhalikar1,
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
AI in Instrumentation Industry
Ajay V. Deshmukh1,
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
AI in Business and Education
Tarjei Alvær Heggernes1,
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
Extreme Randomized Trees for Real Estate Appraisal with Housing and Crime Data
Junchi Bin1, Bryan Gardiner2, Eric Li3 and Zheng Liu1,
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
The Knowledge-based Firm and AI
Ove Rustung Hjelmervik1 and Terje Solsvik Kristensen1,2,
INTRODUCTION
AI - A CREATIVE DESTRUCTION TECHNOLOGY
Schumpeterβs Disruptive Technology and Radical Innovation
IT and The Productivity Paradox
ALAN TURINGβS DISRUPTIVE RESEARCH AND INNOVATION
Turing Machine
Turing Test
Problem Solving
Turingβs Connectionism
GΓΈdel 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
A Mathematical Description of Artificial Neural Networks
Hans Birger Drange1,
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