Biomedical and business applications using artificial neural networks and machine learning:
"This book covers applications of artificial neural networks (ANN) and machine learning (ML) aspects of artificial intelligence to applications to the biomedical and business world including their interface to applications for screening for diseases to applications to large-scale credit card pu...
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Hershey, PA
Engineering Science Reference
[2022]
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| Zusammenfassung: | "This book covers applications of artificial neural networks (ANN) and machine learning (ML) aspects of artificial intelligence to applications to the biomedical and business world including their interface to applications for screening for diseases to applications to large-scale credit card purchasing patterns"-- |
| Beschreibung: | xxiv, 394 Seiten Illustrationen, Diagramme 28 cm |
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| adam_text | Table of Contents Foreword............................................................................................................................................. xv Preface............................................................................................................................................... xvii Acknowledgement........................................................................................................................... xxiv Section 1 Introduction Chapter 1 Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software Richard S. Segall, Arkansas State University, USA 1 Section 2 Biomedical Applications Chapter 2 Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions......... 30 Richard S. Segall, Arkansas State University, USA Chapter 3 Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays.................................................................................................................................................. 58 Son Nguyen, Bryant University, USA Matthew Quinn, Harvard University, USA Alan Olinsky, Bryant University, USA John Quinn, Bryant University, USA Chapter 4 Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management......................................................................................................................................... 80 Viswanathan Rajagopalan, New York Institute of Technology College of Osteopathic
Medicine at Arkansas State University, USA Arkansas State University, USA Center for No-Boundary Thinking at Arkansas State University, USA Houwei Cao, New York Institute of Technology, USA
Chapter 5 Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM).... 128 Fan Wu, Purdue University, USA Juan Shu, Purdue University, USA Chapter б Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)...................................................... 154 Zizhe Gao, Columbia University, USA Hao Lin, Northeastern University, USA Chapter 7 US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model................................................................................................................................................................ 177 FangjunLi, University of Connecticut, USA Gao Niu, Bryant University, USA Section 3 Business Applications Chapter 8 Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques.............. 209 Xiang Li, Cornell University, USA Jingxi Liao, University of Central Florida, USA Tianchuan Gao, Columbia University, USA Chapter 9 Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network.......233 Xiangming Liu, University of Connecticut, USA Gao Niu, Bryant University, USA Chapter 10 U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression.................................................. 263 Ziehen Zhao, Yale University, USA Guanzhou Hou, Johns Hopkins University, USA Chapter 11 Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost
Savings............................................................................................................................................................. 285 Siddharth Vinod Jain, Liverpool John Moores University, UK Manoj Jayabalan, Liverpool John Moores University, UK Chapter 12 Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network................................................................................................ 306 Magari Ishmael Rapoo, North-West University, South Africa
Martin Chanza, North-West University, South Africa Gomolemo Motlhwe, North-West University, South Africa Chapter 13 Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands................................................................................................................................................ 323 Jason Michaud, Bryant University, USA Compilation of References..........................................................................................................................346 About the Contributors............................................................................................................................... 388 Index................................................................................................................................................................. 392
Detailed Table of Contents Foreword........................................................................................................................................................... xv Preface............................................................................................................................................................. xvii Acknowledgement....................................................................................................................................... xxiv Section 1 Introduction Chapter 1 Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software Richard S. Segall, Arkansas State University, USA 1 This chapter first provides an overview with examples of what neural networks (NN), machine learning (ML), and artificial intelligence (AI) are and their applications in biomedical and business situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trail download for limited time use, and open-source software (OSS) for machine learning (ML) are provided. Characteristics of artificial intelligence (AI) technologies for machine learning available as open source are discussed. Illustrations of applications of neural networks, machine learning, and artificial intelligence are presented as used in the daily operations of a large internationally-based software company for optimal configuration
of their Helix Data Capacity system. Section 2 Biomedical Applications Chapter 2 Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions.......... 30 Richard S. Segall, Arkansas State University, USA The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for СОѴГО-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19
and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data. Chapter 3 Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays................................................................................................................................................................ 58 Son Nguyen, Bryant University, USA Matthew Quinn, Harvard University, USA Alan Olinsky, Bryant University, USA John Quinn, Bryant University, USA In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this
study is done using cloud computing services offered by Google Colab Pro. Chapter 4 Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management....................................................................................................................................................... 80 Viswanathan Rajagopalan, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, USA Arkansas State University, USA Center for No-Boundary Thinking at Arkansas State University, USA Houwei Cao, New York Institute of Technology, USA Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc. Chapter 5 Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM).... 128 Fan Wu, Purdue
University, USA Juan Shu, Purdue University, USA COVID-19, one of the most contagious diseases and urgent threats in recent times, attracts attention
across the globe to study the trend of infections and help predict when the pandemic will end. A reliable prediction will make states and citizens acknowledge possible consequences and benefits for the policymaker among the delicate balance of reopening and public safety. This chapter introduces a deep learning technique and long short-term memory (LSTM) to forecast the trend of COVID-19 in the United States. The dataset from the New York Times (NYT) of confirmed and deaths cases is utilized in the research. The results include discussion of the potential outcomes if extreme circumstances happen and the profound effect beyond the forecasting number. Chapter 6 Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)...................................................... 154 Zizhe Gao, Columbia University, USA Hao Lin, Northeastern University, USA Entering the 21st century, computer science and biological research have entered a stage of rapid development. With the rapid inflow of capital into the field of significant health research, a large number of scholars and investors have begun to focus on the impact of neural network science on biometrics, especially the study of biological interactions. With the rapid development of computer technology, scientists improve or perfect traditional experimental methods. This chapter aims to prove the reliability of the methodology and computing algorithms developed by Satyajit Mahapatra and Ivek Raj Gupta’s project team. In this chapter, three datasets take the responsibility to testify the computing algorithms, and they are S.
cerevisiae, H. pylori, and Human-В. Anthracis. Among these three sets of data, the S. cerevisiae is the core subset. The result shows 87%, 87.5%, and 89% accuracy and 87%, 86%, and 87% precision for these three data sets, respectively. Chapter 7 US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model................................................................................................................................................................ 177 FangjunLi, University of Connecticut, USA Gao Niu, Bryant University, USA For the purpose of control health expenditures, there are some papers investigating the characteristics of patients who may incur high expenditures. However fewer papers are found which are based on the overall medical conditions, so this chapter was to find a relationship among the prevalence of medical conditions, utilization of healthcare services, and average expenses per person. The authors used bootstrapping simulation for data preprocessing and then used linear regression and random forest methods to train several models. The metrics root mean square error (RMSE), mean absolute percent error (МАРЕ), mean absolute error (MAE) all showed that the selected linear regression model performs slightly better than the selected random forest regression model, and the linear model used medical conditions, type of
services, and their interaction terms as predictors. Section 3 Business Applications Chapter 8 Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques.............. 209 Xiang Li, Cornell University, USA Jingxi Liao, University of Central Florida, USA Tianchuan Gao, Columbia University, USA Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the К-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model. Chapter 9 Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network.......233 Xiangming Liu, University of Connecticut, USA Gao Niu, Bryant University, USA This chapter presents a thorough descriptive analysis of automobile fatal accident and insurance claims data. Major components of the artificial neural network (ANN) are discussed, and parameters are
investigated and carefully selected to ensure an efficient model construction. A prediction model is constructed through ANN as well as generalized linear model (GLM) for model comparison purposes. The authors conclude that ANN performs better than GLM in predicting data for automobile fatalities data but does not outperform for the insurance claims data because automobile fatalities data has a more complex data structure than the insurance claims data. Chapter 10 U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression.................................................. 263 Ziehen Zhao, Yale University, USA Guanzhou Hou, Johns Hopkins University, USA Artificial neural network (ANN) has been showing its superior capability of modeling and prediction. Neural network model is capable of incorporating high dimensional data, and the model is significantly complex statistically. Sometimes, the complexity is treated as a Blackbox. However, due to the model complexity, the model is capable of capture and modeling an extensive number of patterns, and the prediction power is much stronger than traditional statistical models. Random forest algorithm is a combination of classification and regression trees, using bootstrap to randomly train the model from a set of data (called training set) and test the prediction by a testing set. Random forest has high prediction
speed, moderate variance, and does not require any rescaling or transformation of the dataset. This study validates the relationship between the U.S. unemployment rate and economic indices during the COVID-19 pandemic and constructs three different predictive modeling for unemployment rate by economic indices through neural network, random forest, and generalized linear regression model. Chapter 11 Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings..............................................................................................................................................................285 Siddharth Vinod Jain, Liverpool John Moores University, UK Manoj Jayabalan, Liverpool John Moores University, UK The credit card has been one of the most successful and prevalent financial services being widely used across the globe. However, with the upsurge in credit card holders, banks are facing a challenge from equally increasing payment default cases causing substantial financial damage. This necessitates the importance of sound and effective credit risk management in the banking and financial services industry. Machine learning models are being employed by the industry at a large scale to effectively manage this credit risk. This chapter presents the application of the various machine learning methods like time series models and deep learning models experimented in predicting the credit card payment defaults along with identification of the significant features and the most effective evaluation criteria. This chapter also
discusses the challenges and future considerations in predicting credit card payment defaults. The importance of factoring in a cost function to associate with misclassification by the models is also given. Chapter 12 Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network................................................................................................ 306 Magari Ishmael Rapoo, North-West University, South Africa Martin Chanza, North-West University, South Africa Gomolemo Motlhwe, North-West University, South Africa This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance. Chapter 13 Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball
Brands...............................................................................................................................................................323 Jason Michaud, Bryant University, USA For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This
can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands. Compilation of References......................................................................................................................... 346 About the Contributors............................................................................................................................... 388 Index.................................................................................................................................................................392
|
| adam_txt |
Table of Contents Foreword. xv Preface. xvii Acknowledgement. xxiv Section 1 Introduction Chapter 1 Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software Richard S. Segall, Arkansas State University, USA 1 Section 2 Biomedical Applications Chapter 2 Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions. 30 Richard S. Segall, Arkansas State University, USA Chapter 3 Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays. 58 Son Nguyen, Bryant University, USA Matthew Quinn, Harvard University, USA Alan Olinsky, Bryant University, USA John Quinn, Bryant University, USA Chapter 4 Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management. 80 Viswanathan Rajagopalan, New York Institute of Technology College of Osteopathic
Medicine at Arkansas State University, USA Arkansas State University, USA Center for No-Boundary Thinking at Arkansas State University, USA Houwei Cao, New York Institute of Technology, USA
Chapter 5 Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM). 128 Fan Wu, Purdue University, USA Juan Shu, Purdue University, USA Chapter б Protein-Protein Interactions (PPI) via Deep Neural Network (DNN). 154 Zizhe Gao, Columbia University, USA Hao Lin, Northeastern University, USA Chapter 7 US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model. 177 FangjunLi, University of Connecticut, USA Gao Niu, Bryant University, USA Section 3 Business Applications Chapter 8 Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques. 209 Xiang Li, Cornell University, USA Jingxi Liao, University of Central Florida, USA Tianchuan Gao, Columbia University, USA Chapter 9 Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network.233 Xiangming Liu, University of Connecticut, USA Gao Niu, Bryant University, USA Chapter 10 U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression. 263 Ziehen Zhao, Yale University, USA Guanzhou Hou, Johns Hopkins University, USA Chapter 11 Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost
Savings. 285 Siddharth Vinod Jain, Liverpool John Moores University, UK Manoj Jayabalan, Liverpool John Moores University, UK Chapter 12 Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network. 306 Magari Ishmael Rapoo, North-West University, South Africa
Martin Chanza, North-West University, South Africa Gomolemo Motlhwe, North-West University, South Africa Chapter 13 Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands. 323 Jason Michaud, Bryant University, USA Compilation of References.346 About the Contributors. 388 Index. 392
Detailed Table of Contents Foreword. xv Preface. xvii Acknowledgement. xxiv Section 1 Introduction Chapter 1 Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software Richard S. Segall, Arkansas State University, USA 1 This chapter first provides an overview with examples of what neural networks (NN), machine learning (ML), and artificial intelligence (AI) are and their applications in biomedical and business situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trail download for limited time use, and open-source software (OSS) for machine learning (ML) are provided. Characteristics of artificial intelligence (AI) technologies for machine learning available as open source are discussed. Illustrations of applications of neural networks, machine learning, and artificial intelligence are presented as used in the daily operations of a large internationally-based software company for optimal configuration
of their Helix Data Capacity system. Section 2 Biomedical Applications Chapter 2 Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions. 30 Richard S. Segall, Arkansas State University, USA The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for СОѴГО-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19
and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data. Chapter 3 Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays. 58 Son Nguyen, Bryant University, USA Matthew Quinn, Harvard University, USA Alan Olinsky, Bryant University, USA John Quinn, Bryant University, USA In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this
study is done using cloud computing services offered by Google Colab Pro. Chapter 4 Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management. 80 Viswanathan Rajagopalan, New York Institute of Technology College of Osteopathic Medicine at Arkansas State University, USA Arkansas State University, USA Center for No-Boundary Thinking at Arkansas State University, USA Houwei Cao, New York Institute of Technology, USA Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc. Chapter 5 Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM). 128 Fan Wu, Purdue
University, USA Juan Shu, Purdue University, USA COVID-19, one of the most contagious diseases and urgent threats in recent times, attracts attention
across the globe to study the trend of infections and help predict when the pandemic will end. A reliable prediction will make states and citizens acknowledge possible consequences and benefits for the policymaker among the delicate balance of reopening and public safety. This chapter introduces a deep learning technique and long short-term memory (LSTM) to forecast the trend of COVID-19 in the United States. The dataset from the New York Times (NYT) of confirmed and deaths cases is utilized in the research. The results include discussion of the potential outcomes if extreme circumstances happen and the profound effect beyond the forecasting number. Chapter 6 Protein-Protein Interactions (PPI) via Deep Neural Network (DNN). 154 Zizhe Gao, Columbia University, USA Hao Lin, Northeastern University, USA Entering the 21st century, computer science and biological research have entered a stage of rapid development. With the rapid inflow of capital into the field of significant health research, a large number of scholars and investors have begun to focus on the impact of neural network science on biometrics, especially the study of biological interactions. With the rapid development of computer technology, scientists improve or perfect traditional experimental methods. This chapter aims to prove the reliability of the methodology and computing algorithms developed by Satyajit Mahapatra and Ivek Raj Gupta’s project team. In this chapter, three datasets take the responsibility to testify the computing algorithms, and they are S.
cerevisiae, H. pylori, and Human-В. Anthracis. Among these three sets of data, the S. cerevisiae is the core subset. The result shows 87%, 87.5%, and 89% accuracy and 87%, 86%, and 87% precision for these three data sets, respectively. Chapter 7 US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model. 177 FangjunLi, University of Connecticut, USA Gao Niu, Bryant University, USA For the purpose of control health expenditures, there are some papers investigating the characteristics of patients who may incur high expenditures. However fewer papers are found which are based on the overall medical conditions, so this chapter was to find a relationship among the prevalence of medical conditions, utilization of healthcare services, and average expenses per person. The authors used bootstrapping simulation for data preprocessing and then used linear regression and random forest methods to train several models. The metrics root mean square error (RMSE), mean absolute percent error (МАРЕ), mean absolute error (MAE) all showed that the selected linear regression model performs slightly better than the selected random forest regression model, and the linear model used medical conditions, type of
services, and their interaction terms as predictors. Section 3 Business Applications Chapter 8 Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques. 209 Xiang Li, Cornell University, USA Jingxi Liao, University of Central Florida, USA Tianchuan Gao, Columbia University, USA Machine learning is a broad field that contains multiple fields of discipline including mathematics, computer science, and data science. Some of the concepts, like deep neural networks, can be complicated and difficult to explain in several words. This chapter focuses on essential methods like classification from supervised learning, clustering, and dimensionality reduction that can be easily interpreted and explained in an acceptable way for beginners. In this chapter, data for Airbnb (Air Bed and Breakfast) listings in London are used as the source data to study the effect of each machine learning technique. By using the К-means clustering, principal component analysis (PCA), random forest, and other methods to help build classification models from the features, it is able to predict the classification results and provide some performance measurements to test the model. Chapter 9 Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network.233 Xiangming Liu, University of Connecticut, USA Gao Niu, Bryant University, USA This chapter presents a thorough descriptive analysis of automobile fatal accident and insurance claims data. Major components of the artificial neural network (ANN) are discussed, and parameters are
investigated and carefully selected to ensure an efficient model construction. A prediction model is constructed through ANN as well as generalized linear model (GLM) for model comparison purposes. The authors conclude that ANN performs better than GLM in predicting data for automobile fatalities data but does not outperform for the insurance claims data because automobile fatalities data has a more complex data structure than the insurance claims data. Chapter 10 U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression. 263 Ziehen Zhao, Yale University, USA Guanzhou Hou, Johns Hopkins University, USA Artificial neural network (ANN) has been showing its superior capability of modeling and prediction. Neural network model is capable of incorporating high dimensional data, and the model is significantly complex statistically. Sometimes, the complexity is treated as a Blackbox. However, due to the model complexity, the model is capable of capture and modeling an extensive number of patterns, and the prediction power is much stronger than traditional statistical models. Random forest algorithm is a combination of classification and regression trees, using bootstrap to randomly train the model from a set of data (called training set) and test the prediction by a testing set. Random forest has high prediction
speed, moderate variance, and does not require any rescaling or transformation of the dataset. This study validates the relationship between the U.S. unemployment rate and economic indices during the COVID-19 pandemic and constructs three different predictive modeling for unemployment rate by economic indices through neural network, random forest, and generalized linear regression model. Chapter 11 Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings.285 Siddharth Vinod Jain, Liverpool John Moores University, UK Manoj Jayabalan, Liverpool John Moores University, UK The credit card has been one of the most successful and prevalent financial services being widely used across the globe. However, with the upsurge in credit card holders, banks are facing a challenge from equally increasing payment default cases causing substantial financial damage. This necessitates the importance of sound and effective credit risk management in the banking and financial services industry. Machine learning models are being employed by the industry at a large scale to effectively manage this credit risk. This chapter presents the application of the various machine learning methods like time series models and deep learning models experimented in predicting the credit card payment defaults along with identification of the significant features and the most effective evaluation criteria. This chapter also
discusses the challenges and future considerations in predicting credit card payment defaults. The importance of factoring in a cost function to associate with misclassification by the models is also given. Chapter 12 Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network. 306 Magari Ishmael Rapoo, North-West University, South Africa Martin Chanza, North-West University, South Africa Gomolemo Motlhwe, North-West University, South Africa This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance. Chapter 13 Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball
Brands.323 Jason Michaud, Bryant University, USA For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This
can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands. Compilation of References. 346 About the Contributors. 388 Index.392 |
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| discipline | Informatik |
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| id | DE-604.BV048206753 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:48:00Z |
| indexdate | 2024-07-10T09:31:59Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033587631 |
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| physical | xxiv, 394 Seiten Illustrationen, Diagramme 28 cm |
| publishDate | 2022 |
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| publisher | Engineering Science Reference |
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| spelling | Biomedical and business applications using artificial neural networks and machine learning Richard S. Segall and Gao Niu, editor Hershey, PA Engineering Science Reference [2022] xxiv, 394 Seiten Illustrationen, Diagramme 28 cm txt rdacontent n rdamedia nc rdacarrier "This book covers applications of artificial neural networks (ANN) and machine learning (ML) aspects of artificial intelligence to applications to the biomedical and business world including their interface to applications for screening for diseases to applications to large-scale credit card purchasing patterns"-- Medicine / Research / Data processing Neural networks (Computer science) Neural Networks, Computer Médecine / Recherche / Informatique Réseaux neuronaux (Informatique) Medicine / Research / Data processing fast Neural networks (Computer science) fast Medizin (DE-588)4038243-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Medizin (DE-588)4038243-6 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Segall, Richard S. 1949- Sonstige (DE-588)1079222448 oth Niu, Gao Sonstige (DE-588)120416360X oth Online version Biomedical and business applications using artificial neural networks and machine learning Hershey, PA : Engineering Science Reference, [2022] Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033587631&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Biomedical and business applications using artificial neural networks and machine learning Medicine / Research / Data processing Neural networks (Computer science) Neural Networks, Computer Médecine / Recherche / Informatique Réseaux neuronaux (Informatique) Medicine / Research / Data processing fast Neural networks (Computer science) fast Medizin (DE-588)4038243-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4038243-6 (DE-588)4193754-5 |
| title | Biomedical and business applications using artificial neural networks and machine learning |
| title_auth | Biomedical and business applications using artificial neural networks and machine learning |
| title_exact_search | Biomedical and business applications using artificial neural networks and machine learning |
| title_exact_search_txtP | Biomedical and business applications using artificial neural networks and machine learning |
| title_full | Biomedical and business applications using artificial neural networks and machine learning Richard S. Segall and Gao Niu, editor |
| title_fullStr | Biomedical and business applications using artificial neural networks and machine learning Richard S. Segall and Gao Niu, editor |
| title_full_unstemmed | Biomedical and business applications using artificial neural networks and machine learning Richard S. Segall and Gao Niu, editor |
| title_short | Biomedical and business applications using artificial neural networks and machine learning |
| title_sort | biomedical and business applications using artificial neural networks and machine learning |
| topic | Medicine / Research / Data processing Neural networks (Computer science) Neural Networks, Computer Médecine / Recherche / Informatique Réseaux neuronaux (Informatique) Medicine / Research / Data processing fast Neural networks (Computer science) fast Medizin (DE-588)4038243-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Medicine / Research / Data processing Neural networks (Computer science) Neural Networks, Computer Médecine / Recherche / Informatique Réseaux neuronaux (Informatique) Medizin Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033587631&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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