Review of digital heart sound classification methods via Artificial Neural Networks

In the field of medicine, several recent studies have shown the value of Artificial Neural Networks, decision trees, logistic regression are playing a major role as the predictor, and classification methods. The research has been expanded to estimate the incidence of breast, lung, liver, ovarian, cervical, bladder and skin cancer. The main aim of this paper is to develop models of logistic regression, Artificial Neural Networks, and Decision trees using the same input and output variables and to compare their success in predicting breast cancer survival in woman. To find the best model for breast cancer survival, the sensitivity and specificity of all these models are measured and evaluated with their respective confidence intervals and the ROC values.

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Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

Journal of Risk and Financial Management ◽

10.3390/jrfm13030060 ◽

2020 ◽

Vol 13 (3) ◽

pp. 60 ◽

Cited By ~ 2

Author(s):

Jakub Horak ◽

Jaromir Vrbka ◽

Petr Suler

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Prediction Models ◽

Bankruptcy Prediction ◽

Support Vector ◽

Classification Methods ◽

Neural Structure ◽

Artificial Neural ◽

Business Entities

Bankruptcy prediction is always a topical issue. The activities of all business entities are directly or indirectly affected by various external and internal factors that may influence a company in insolvency and lead to bankruptcy. It is important to find a suitable tool to assess the future development of any company in the market. The objective of this paper is to create a model for predicting potential bankruptcy of companies using suitable classification methods, namely Support Vector Machine and artificial neural networks, and to evaluate the results of the methods used. The data (balance sheets and profit and loss accounts) of industrial companies operating in the Czech Republic for the last 5 marketing years were used. For the application of classification methods, TIBCO’s Statistica software, version 13, is used. In total, 6 models were created and subsequently compared with each other, while the most successful one applicable in practice is the model determined by the neural structure 2.MLP 22-9-2. The model of Support Vector Machine shows a relatively high accuracy, but it is not applicable in the structure of correct classifications.

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Detecting Fake Twitter Accounts with using Artificial Neural Networks

Artificial Intelligence Studies ◽

10.30855/ais.2018.01.01.03 ◽

2018 ◽

Vol 1 (1) ◽

pp. 26-29

Author(s):

Mehmet ŞİMŞEK ◽

Oğuzhan YILMAZ ◽

Asena Hazal KAHRİMAN ◽

Levent SABAH

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Online Social Networks ◽

Classification Methods ◽

Activation Functions ◽

The Social ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Ann Classifier ◽

Or Organization

Online Social Networks (OSNs) are great environments for sharing ideas, following news, advertising products etc., and they have been widely using by people. Although these advantages of OSNs, it is difficult to understand whether an account in OSNs really belongs to a person or organization. Through created fake accounts, unwanted content can spread over the social network. Therefore, the identification of fake accounts is an important problem. In this study, we applied Artificial Neural Network (ANN) classifier to this problem and we evaluated performances of different activation functions. According to the experimental results, use of artificial neural networks in detecting fake accounts yielded successful results. The use of various activation functions in different layers on the ANN significantly affects the results. In the literature, other classification methods have been widely used for detecting fake accounts and spammers on OSNs. To the best of our knowledge, there is no detailed study that classifies fake accounts using ANNs with different activation functions.

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