Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8455-2.ch001
R. Segall
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.
{"title":"Overview of Multi-Factor Prediction Using Deep Neural Networks, Machine Learning, and Their Open-Source Software","authors":"R. Segall","doi":"10.4018/978-1-7998-8455-2.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch001","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127559327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8455-2.ch012
M. I. Rapoo, M. Chanza, Gomolemo Motlhwe
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.
{"title":"Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network","authors":"M. I. Rapoo, M. Chanza, Gomolemo Motlhwe","doi":"10.4018/978-1-7998-8455-2.ch012","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch012","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"45 S212","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120833758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8455-2.ch007
Fangjun Li, G. Niu
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 (MAPE), 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.
{"title":"US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model","authors":"Fangjun Li, G. Niu","doi":"10.4018/978-1-7998-8455-2.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch007","url":null,"abstract":"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 (MAPE), 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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116725325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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