Era of Artificial Intelligence: The 21st Century Practitioners' Approach

Description

This text has attempted to collate quality research articles ranging from A Mathematical Disposition for Neural Nets, to Cognitive Computing, to Quantum Machine Learning, to a Multimodal Emotion Recognition System, to Responsible AI, to AI for Accessibility and Inclusion, to Artificial-Enabled Intelligence Enabled Applications in the sectors of Health, Pharma and Education. Features Focus on AI research and interdisciplinary research that exhibits AI inclusion to a greater degree Focus on application of disruptive technology in the context of the twenty-first century human and machine approach Focus on role of disruptive technology such as cognitive computing, quantum machine learning, IOT enabled-recognition systems Focus on unravelling the powerful features of artificial intelligence for societal benefits including accessibility This volume will cater as a ready reference to an individual’s quest for deep diving into the ocean of artificial intelligence-enabled solution approaches. The book will serve as a useful reference for researchers, innovators, academicians, entrepreneurs, and professionals aspiring to gain expertise in the domain of cognitive and quantum computing, IOT-enabled intelligent systems and so on. Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Editor’s Biography List of Contributors 1 Artificial Intelligence for Accessibility and Inclusion 1.1 Introduction: Background and Driving Forces 1.2 AI in Daily Life 1.2.1 Productivity 1.2.2 Accessing Technology 1.3 AI in Higher Education and the Workplace 1.4 AI in Healthcare 1.4.1 Automated Reminders 1.4.2 AI in Radiology 1.4.3 Treatment Algorithms 1.4.4 Selfies Healthcare 1.4.5 Privacy 1.5 Expectations of AI 1.6 Diversity On AI Teams 1.7 Conclusions References 2 Artificial Intelligence—Applications Across Industries (Healthcare, Pharma, Education) 2.1 Introduction 2.2 AI in Healthcare 2.3 Applications of AI in Healthcare 2.3.1 AI for Early Disease Identification and Diagnosis 2.3.2 Patient-Specific Management of Disease 2.3.3 Using AI in Medical Imaging 2.3.4 Reliability of Clinical Trials 2.3.5 Accelerated Improvement of Medication 2.3.6 Intelligent Patient Care 2.3.7 Minimising Errors 2.3.8 Reduction in Healthcare Expenses 2.3.9 Increasing Patient-Doctor Interaction 2.3.10 Providing Relevant Context 2.3.11 Robot-Assisted Surgery 2.4 AI in Education 2.5 Applications of Artificial Intelligence in Education 2.5.1 Personalised Learning 2.5.2 Automation of Tasks 2.5.3 Creating Smart Content 2.5.4 Visualisation of Data 2.5.5 Creation of Digital Lessons 2.5.6 Accessibility for All 2.5.7 Identifying Classroom Weaknesses 2.5.8 Personalised Feedback Based On Data 2.5.9 Conversational AI Is Available 24-7 2.5.10 Decentralised and Safe Educational Platforms 2.5.11 Using AI in Exams 2.5.12 Frequently Updated Material 2.6 AI in Pharmaceuticals 2.7 Applications AI in the Pharmaceutical Industry (Anon., 2020) 2.7.1 AI in Drug Discovery 2.7.2 Drug Development and Design 2.7.3 Diagnosis 2.7.4 Remote Observation 2.7.5 Improved Manufacturing Process 2.7.6 Marketing 2.8 Conclusion References 3 AI-Enabled Healthcare for Next Generation 3.1 Introduction to AI-Enabled Healthcare 3.1.1 Challenges for Artificial Intelligence in Healthcare 3.1.2 Patient-Centric 3.1.3 Clinical Decision Support 3.1.4 Prevention Is the Buzzword 3.1.5 AI Support for Healthcare 3.1.6 Always Connected 3.1.7 Personalization in the Health-Care Domain 3.1.8 Health-Care On the Cloud 3.1.9 Data Driven 3.2 Hurdles to the Implementation of AI 3.2.1 Data Attributes and Access Limitations 3.3 AI Technologies for Health and Pharma 3.3.1 Rule-Based Expert Systems 3.3.2 Diagnosis and Treatment Applications 3.3.3 Clinical Trials 3.4 Conclusion References 4 Classification of Brain Tumors Using CNN and ML Algorithms 4.1 Introduction 4.2 Related Works 4.3 Proposal of System Approach 4.4 Methodology Used in Proposed System 4.4.1 Convolutional Neural Networks 4.4.1.1 Convolutional Layer 4.4.1.2 Flattening 4.4.1.3 Fully Connected Layer 4.4.2 Random Forest Classifier 4.4.3 SVM 4.4.4 Naive Bayes Classifier 4.4.5 Decision Tree Classifier 4.4.6 K-Nearest Neighbor Classifier 4.5 Results 4.6 Conclusion References 5 Cognitive Computing and Big Data for Digital Health: Past, Present, and the Future 5.1 Introduction 5.2 Big Data Analysis for Cognitive Computing Applications 5.3 Related Studies 5.4 Data and Methodology 5.5 Analysis and Discussion 5.6 Conclusions References 6 Quantum Machine Learning and Its Applications 6.1 Introduction 6.2 Quantum Fundamentals 6.2.1 Quantum Basic Concepts and Quantum Circuits 6.2.2 Quantum Computing 6.2.2.1 Adiabatic Quantum Models 6.3 Quantum Machine Learning Algorithms and Applications 6.3.1 First Phase (1900s ~ 2007): Fundamental Model Formulation 6.3.1.1 Quantum Computational Learning Theory 6.3.1.2 Quantum Generalization of Neural Networks 6.3.2 Second Phase (2008 ~ Present): Implementation 6.3.2.1 Domain One: Time Complexity of Quantum Linear Algorithm 6.3.2.2 Second Domain: Variational Quantum Algorithms 6.3.2.3 Third Domain: Quantum Machine Learning Inspired By the Brain 6.4 Current State of Knowledge in Applications 6.5 Conclusion and Future Directions References 7 Introducing an IoT-Enabled Multimodal Emotion Recognition System for Women Cancer Survivors 7.1 Introduction 7.2 Related Work 7.3 Identified Challenges 7.4 Need for a Multimodal Emotion Recognition System 7.5 Proposed IoT Enabled Multimodal Emotion Recognition System 7.6 Methodology 7.6.1 Design and Development of an Integrated IoT Framework 7.6.2 Module Design for Proposed System a. Module 1 b. Module 2 c. Module 3 d. Module 4 e. Module 5 7.7 Implementation and Results 7.8 Discussion of Results 7.9 Conclusion References 8 Responsible AI in Automatic Speech Recognition 8.1 Introduction 8.1.1 Features of Responsible AI 8.1.2 Deep Neural Networks 8.2 ASR and Current Trends 8.2.1 Why Do We Need ASR? 8.2.2 What Factors Affect the Accuracy of Speech to Text? 8.3 How Responsible AI Can Improve ASR 8.3.1 Architecture of ASR 8.3.2 ASR Using Responsible AI in Customer Operations 8.3.2.1 What Does Responsible AI Add to ASR? 8.4 Scope of Responsible AI 8.4.1 What Impact Can It Have On the Market? 8.4.2 How Can It Benefit Clients? 8.5 Conclusion References 9 A Mathematical Disposition of Optimization Techniques for Neural Networks 9.1 Introduction 9.2 Global and Local Maximum and Minimum Values of Univariate Function 9.3 Fuzzy Goal Programming Approach With Machine Learning 9.4 Convex and Non-Convex Optimization 9.5 Linear Programming Problem 9.6 Loss Functions 9.7 Over-Parameterization and Generalization 9.8 Optimizers for Neural Net 9.9 Conclusion References Index