Biomedical and business applications using artificial neural networks and machine learning / Richard S. Segall and Gao Niu, editor.

Format:

Book

Contributor:

Segall, Richard, 1949- editor.

Niu, Gao, 1987- editor.

Language:

English

Subjects (All):

Neural networks (Computer science).

Physical Description:

1 online resource (419 pages)

Place of Publication:

Hershey, Pennsylvania : Engineering Science Reference, [2022]

Summary:

During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.

Contents:

Title Page

Copyright Page

Book Series

List of Reviewers

Table of Contents

Detailed Table of Contents

Foreword

Preface

Acknowledgement

Section 1: Introduction

Chapter 1: Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software

Section 2: Biomedical Applications

Chapter 2: Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions

Chapter 3: Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays

Chapter 4: Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

Chapter 5: Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)

Chapter 6: Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)

Chapter 7: US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model

Section 3: Business Applications

Chapter 8: Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques

Chapter 9: Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network

Chapter 10: U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression

Chapter 11: Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings

Chapter 12: Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network

Chapter 13: Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands

Compilation of References

About the Contributors

Index.

Notes:

Description based on print version record.

ISBN:

1-7998-8457-0

OCLC:

1290486326