Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning最新文献

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Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network 基于人工神经网络的汽车致命事故与保险理赔分析
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch009
Xiangming Liu, G. Niu
{"title":"Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network","authors":"Xiangming Liu, G. Niu","doi":"10.4018/978-1-7998-8455-2.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch009","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"7 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":"124068671","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}
引用次数: 0
Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management 人工智能在心血管研究、诊断和疾病管理中的应用
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch004
V. Rajagopalan, Houwei Cao
{"title":"Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management","authors":"V. Rajagopalan, Houwei Cao","doi":"10.4018/978-1-7998-8455-2.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch004","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"43 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":"122546868","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}
引用次数: 0
Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands 基于机器学习技术的热门篮球品牌价值分析与预测
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch013
Jason P. Michaud
{"title":"Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands","authors":"Jason P. Michaud","doi":"10.4018/978-1-7998-8455-2.ch013","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch013","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"55 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":"116537352","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}
引用次数: 0
Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays 比较深度神经网络和梯度增强在胸部x射线肺炎检测中的应用
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch003
Son Nguyen, Matthew Quinn, A. Olinsky, John T. Quinn
{"title":"Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays","authors":"Son Nguyen, Matthew Quinn, A. Olinsky, John T. Quinn","doi":"10.4018/978-1-7998-8455-2.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch003","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"2 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":"129902468","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}
引用次数: 0
Protein-Protein Interactions (PPI) via Deep Neural Network (DNN) 基于深度神经网络(DNN)的蛋白质-蛋白质相互作用(PPI)
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch006
Zizhe Gao, Hao Lin
{"title":"Protein-Protein Interactions (PPI) via Deep Neural Network (DNN)","authors":"Zizhe Gao, Hao Lin","doi":"10.4018/978-1-7998-8455-2.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch006","url":null,"abstract":"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-B. 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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"41 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":"116621073","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}
引用次数: 0
Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings 将机器学习方法应用于信用卡支付违约预测并节省成本
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch011
Siddharth Vinod Jain, M. Jayabalan
{"title":"Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings","authors":"Siddharth Vinod Jain, M. Jayabalan","doi":"10.4018/978-1-7998-8455-2.ch011","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch011","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"50 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":"133940683","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}
引用次数: 3
Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions 神经网络和机器学习在COVID-19预测中的应用综述
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch002
R. Segall
{"title":"Survey of Applications of Neural Networks and Machine Learning to COVID-19 Predictions","authors":"R. Segall","doi":"10.4018/978-1-7998-8455-2.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch002","url":null,"abstract":"The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"32 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":"128237422","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}
引用次数: 2
Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM) 基于长短期记忆(LSTM)深度学习模型的COVID-19预测
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch005
Fan Wu, Juan Shu
{"title":"Predictions For COVID-19 With Deep Learning Models of Long Short-Term Memory (LSTM)","authors":"Fan Wu, Juan Shu","doi":"10.4018/978-1-7998-8455-2.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch005","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"33 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":"128136874","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}
引用次数: 22
U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression 基于神经网络、随机森林和广义线性回归的经济指标预测新冠肺炎疫情下美国失业率
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch010
Zichen Zhao, Guanzhou Hou
{"title":"U.S. Unemployment Rate Prediction by Economic Indices in the COVID-19 Pandemic Using Neural Network, Random Forest, and Generalized Linear Regression","authors":"Zichen Zhao, Guanzhou Hou","doi":"10.4018/978-1-7998-8455-2.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch010","url":null,"abstract":"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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"12 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":"128307938","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}
引用次数: 0
Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques 通过机器学习技术分析Airbnb(空气床和早餐)的房源
Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8455-2.ch008
Xiang Li, Jingxi Liao, Tianchuan Gao
{"title":"Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques","authors":"Xiang Li, Jingxi Liao, Tianchuan Gao","doi":"10.4018/978-1-7998-8455-2.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-8455-2.ch008","url":null,"abstract":"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 K-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.","PeriodicalId":250689,"journal":{"name":"Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning","volume":"22 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":"115844757","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}
引用次数: 1
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