Artificial intelligence (AI) : recent trends and applications / edited by S. Kanimozhi Suguna, M. Dhivya, and Sara Paiva.

Format:

Book

Contributor:

Suguna, S. Kanimozhi, editor.

Dhivya, M., editor.

Paiva, Sara, 1979- editor.

Series:

Artificial Intelligence (AI): Elementary to Advanced Practices

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 online resource (331 pages)

Edition:

1st ed.

Place of Publication:

Boca Raton, Florida ; London, England ; New York : CRC Press, [2021]

Summary:

"This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Artificial Intelligence. The book provides a premier interdisciplinary platform to present practical challenges and adopted solutions. The book addresses the complete functional framework workflow in Artificial Intelligence technology. It explores the basic and high-level concepts and can serve as a manual for the industry for beginners and the more advanced. It covers intelligent and automated systems and its implications to the real-world, and offers data acquisition and case studies related to data-intensive technologies in AI-based applications. The book will be of interest to researchers, professionals, scientists, professors, students of computer science engineering, electronics and communications, as well as information technology"-- Provided by publisher.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Preface

Acknowledgements

Editor biographies

List of Contributors

Chapter 1 Advances in Large-Scale Systems Simulation Modelling Using Multi-Agent Architectures Optimized with Artificial Intelligence Techniques for Improved Concurrency-Supported Scheduling Mechanisms with Application to Wireless Systems Simulation

1.1 Literature Review

1.1.1 Simulation Methodologies Applied in Wireless Communication Systems (WCS)

1.1.1.1 Simulation of WCS

1.1.1.2 Discrete Event Simulation

1.1.1.3 Event Scheduling

1.1.2 Channel Assignment in WCS

1.1.3 Multi-Agent Systems in WCS

1.1.3.1 Agent and Multi-Agent Systems

1.1.3.2 Multi-Agent Systems in WCS

1.1.4 The Concept of Cellular Network

1.1.5 Simulation Languages (SLs)

1.2 The Proposed Simulation Model

1.2.1 Network Structure

1.2.1.1 Operational Parameters

1.2.2 Modelled Network Services and Channel Allocation

1.2.2.1 Network Services

1.2.2.2 Channel Allocation

1.2.2.3 Traffic Generation

1.2.3 The Multi-Agent/Multilayered Model

1.2.4 Theoretical Analysis of Agents Adapted to Modelled Network Services

1.2.4.1 Network Agent Definition

1.2.4.2 Architecture of the Intelligent Network Agents

1.2.4.3 Network Agent Interface

1.2.4.4 Network Agents Which Maintain State

1.2.4.5 Network Agent Utility Functions

1.2.4.6 Multi-Agent Encounters

1.2.5 Event Interleaving as Scheduling Technique Based on Real-Time Scheduling Theory

1.2.5.1 Real-Time Scheduling Algorithms for Implementing Synchronized Processes or Events

1.2.5.2 Process Life Span in a Real-Time Scheduling Set-Up

1.2.5.3 Scheduling Concurrent Events in WCS

1.2.5.4 Response Time Analysis

1.2.5.5 Pre-emptive Stationary Priority Scheduling (PSPS).

1.2.6 Supported DCA Variations

1.2.6.1 The Conventional Unbalanced Variation (Classical DCA)

1.2.6.2 The Conventional Balanced Variation (Min Cell Congestion)

1.2.6.3 The Conventional Best CNR Variation

1.2.6.4 The Conventional Round Blocking Variation

1.2.6.5 The Proposed Novel Artificial Intelligence Based Balanced and Best CNR DCA Variation for Concurrent Channel Assignment

1.2.7 Implementation Architectures

1.2.7.1 Conventional Model

1.2.7.2 Concurrent Models

1.3 Simulation Model Evaluation

1.3.1 Network Behaviour

1.3.2 Monte Carlo Simulation Method

1.3.3 Simulation Model Behaviour

1.3.4 Results Accuracy

1.3.5 Reference Analysis Model Employing One Cell Only

1.4 Experimental Results

1.4.1 Indicative Results Based on Five Days of Network Operation

1.4.2 Model Behaviour Based on Architectural Variations

1.4.3 Scheduling Mechanism Comparison

1.4.4 Response Time Analysis Results

1.5 Conclusions and Future Work

References

Chapter 2 Let's Find Out: Why Do Users React Differently to Applications Infused with AI Algorithms?

2.1 Introduction

2.2 Related Work and Hypothesis Formulation

2.2.1 Excitement

2.2.2 Anger

2.2.3 Desire

2.2.4 Happiness

2.2.5 Relax

2.3 Methodology

2.3.1 Participants

2.3.2 Procedure

2.4 Findings

2.4.1 Descriptive Statistics and Hypothesis Testing Outcomes

2.4.2 Qualitative Feedback

2.5 Discussions

2.6 Limitations and Future Work

2.7 Conclusion

Chapter 3 AI vs. Machine Learning vs. Deep Learning

3.1 Introduction: Background and Driving Forces

3.2 Overview of Artificial Intelligence

3.3 Steps to Implement Artificial Intelligence Algorithms

3.4 When/Where/How/Why to Use Artificial Intelligence?

3.5 Examples for Artificial Intelligence Applications

3.6 Overview of Machine Learning.

3.7 Steps to Implement Machine Learning Algorithms

3.8 When/Where/How/Why to Use Machine Learning?

3.9 Examples for Machine Learning Applications

3.10 Overview of Deep Learning

3.11 Steps to Implement Deep Learning Algorithms

3.12 When/Where/How/Why to Use Deep Learning?

3.13 Examples for Deep Learning Applications

3.14 Comparisons of Artificial Intelligence, Deep Learning, and Machine Learning

3.15 Summary

Chapter 4 AI and Big Data: Ethical Reasoning and Responsibility

4.1 Introduction

4.2 Ethics Reasoning in Artificial Intelligence

4.3 Ethical Responsibility in AI

Chapter 5 Online Liquid Level Estimation in Dynamic Environments Using Artificial Neural Network

5.1 Introduction

5.2 Liquid Level Measurement in Dynamic Environments

5.2.1 Influence of Temperature

5.2.2 Influence of Inclination

5.2.3 Influence of Sloshes

5.3 Sensor Design

5.3.1 Fibre Bragg Grating Sensor

5.3.2 Cantilever Beam

5.3.3 Float Sensor

5.3.4 System and Working Principle

5.4 Introducing Neural Networks for Accurate Level Prediction

5.4.1 Sampling of Sensor Output

5.4.2 Artificial Neural Networks

5.4.3 Activation Function

5.5 Wavelet Neural Network

5.5.1 Training of WNN

5.6 Results

5.7 Conclusion

Chapter 6 Computer Vision Concepts and Applications

6.1 Introduction

6.1.1 Evolution of Computer Vision

6.2 Feature Extraction

6.2.1 Types of Features

6.2.2 Feature Extraction Methods

6.2.2.1 I. Low-Level Features

6.2.2.2 Texture Estimator

6.2.2.3 Colour Histogram

6.2.2.4 Colour Descriptor

6.3 Object Detection

6.3.1 Image Classification

6.3.1.1 Classification and Localization

6.3.2 Image Segmentation

6.3.2.1 Semantic Segmentation

6.3.2.2 Demerits of Sliding Window

6.3.2.3 Instance Segmentation.

6.3.3 Region-based Methods

6.3.3.1 Region Proposal

6.3.3.2 Region-based Convolutional Neural Network(R-CNN)

6.3.3.3 Fast Region-based Convolutional Neural Network

6.3.3.4 Faster Region-based Convolutional Neural Network

6.3.4 Alternative Methods

6.3.4.1 HOG Features

6.3.4.2 You Only Look Once (YOLO)

6.3.4.3 Demerits of YOLO

6.4 Computer Vision Hardware, Software, and Services

6.4.1 Computer Vision Hardware

6.4.2 Software Libraries and Tools

6.4.3 Computer Vision Services

6.5 Applications of Computer Vision

6.5.1 Healthcare

6.5.2 Augmented Reality

6.5.3 Vision-based Self-Driving Cars

6.5.4 Automatic Target Recognition and Detection

6.5.4.1 Case Study: Robotic Path Planning Using Visual Percepts

6.6 Conclusion and Future Directions

Bibliography

Chapter 7 Generative Adversarial Network: Concepts, Variants, and Applications

7.1 Introduction

7.2 Overview

7.2.1 Deep Learning

7.2.2 Deep Generative Models

7.2.3 Generative Adversarial Networks

7.3 GAN Architecture

7.3.1 General Structure

7.3.2 Adversarial Process

7.3.3 Background Mathematics

7.4 GAN Variations

7.4.1 Overview

7.4.2 Techniques

7.4.2.1 Architecture-based Variant Class

7.4.2.2 Formulation-based Variant Class

7.5 Applications

7.5.1 Image Generation and Prediction

7.5.2 Image Translation

7.5.3 Image Editing

7.5.4 3D Object Generation

7.5.5 Video Manipulation

7.5.6 Audio Generation and Translation

7.5.7 Medical Image Processing

7.6 Conclusion and Future Directions

Chapter 8 Detection and Classification of Power Quality Disturbances in Smart Grids Using Artificial Intelligence Methods

8.1 Introduction

8.1.1 Signal Processing (SP)-based PQD Detection Methods

8.1.2 Artificial Intelligent (AI) Methods for PQD Detection.

8.2 Wavelet Transform (WT)-based PQD Detection Methods

8.2.1 Wavelet Transform (WT)

8.2.2 Proposed DWT-based PQD Detection Method

8.3 AI-based PQD Classification Methods

8.3.1 Deep Learning Structures

8.3.1.1 SAE-based Methods

8.3.1.2 DNN-based Methods

8.3.1.3 DBN (Deep belief network)-based Methods

8.3.1.4 CNN-based Methods

8.3.2 Proposed Deep Learning and WT-based Hybrid PQD Classification Method

8.4 Results

8.5 Conclusion

Chapter 9 Robust Design of Artificial Neural Network Methodology to Solve the Inverse Kinematics of a Manipulator of 6 DOF

9.1 Introduction

9.1.1 Kinematics of Robotic Manipulators

9.1.2 Artificial Neural Networks

9.1.3 Inverse Kinematics Solution with Artificial Neural Networks

9.1.4 Robust Design of Artificial Neural Networks

9.2 Robust Design of Artificial Neural Networks Methodology

9.3 Kinematics Analysis of Robotic Manipulator Called Ketzal

9.3.1 Data Set Description

9.3.2 Description of Reduction Data Filter Algorithm

9.3.3 Data Set Analysis of Training and Test

9.3.4 Planning and Experimentation Stage

9.3.5 Analysis and Confirmation Stage

9.4 Conclusions and Discussions

Future Scope

Chapter 10 Generative Adversarial Network and Its Applications

10.1 Introduction

10.2 Discriminative Learning vs. Generative Learning

10.3 Deep Generative Model

10.4 Variational Auto Encoders

10.5 Generative Adversarial Network

10.6 Architecture of Generative Adversarial Network

10.7 Variations of GAN Architectures

10.7.1 Fully Connected GAN (FCGAN)

10.7.2 Laplacian Pyramid of Adversarial Networks (LAPGAN)

10.7.3 Deep Convolutional GAN (DCGAN)

10.7.4 Conditional GAN

10.7.5 Least-Square GAN

10.7.6 Auxiliary Classifier GAN

10.7.7 InfoGAN

10.8 Applications of GAN.

10.8.1 Image generation.

Notes:

Description based on print version record.

ISBN:

1-00-300562-4

1-003-00562-4

1-000-37552-8

9781003005629

OCLC:

1246578674