Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis

2011 ◽  
Vol 31 (3) ◽  
pp. 441-468 ◽  
Author(s):  
Soo Y. Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Support Vector ◽  
Multivariate Discriminant Analysis ◽  
Artificial Neural ◽  
Multivariate Discriminant

scholarly journals Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung

2021 ◽  
Vol 7 (3) ◽  
pp. 329
Author(s):  
Fitri Handayani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Support Vector ◽  
Artificial Neural

Penyakit jantung adalah salah satu penyakit yang menyebabkan resiko kematian cukup tinggi di dunia. Kolesterol, diabetes, tekanan darah tinggi merupakan faktor-faktor pemicu terjadinya penyakit jantung. Perlu deteksi sejak ini mengenai prediksi penyakit jantung pada setiap individu agar pencegahan dan pengobatan dapat segera dilakukan demi tingkat Kesehatan yang lebih baik. Berbagai metode dapat dilakukan untuk melakukan deteksi penyakit jantung, baik dengan metode tradisional dan metode yang memanfaatkan teknologi. Saat ini mulai banyak bermunculan system pendeteksi penyakit jantung dengan memanfaatkan algoritma machine learning. Algoritma machine learning dianggap mudah untuk diaplikasikan untuk mengklasifikasikan apakah seseorang terkena penyakit jantung. Penelitian ini mencoba melakukan klasifikasi penyakit jantung menggunakan dataset public dari UCI menggunakan tiga algorima machine learning, yaitu Support Vector Machine (SVM), Logistic Regression (LR) dan Artifiacial Neural Network (ANN). Ketiga algorima tersebut diuji menggunakan empat skenario pembagian data training dan testing yang berbeda, yaitu 90:10, 80:20, 70:40 dan 60:40. Dari hasil eksperimen didapatkan hasil akurasi tertinggi pada metode Logistic Regression sebesar 86% menggunakan skenario pembagian data 80:20.

Bankruptcy prediction for Japanese corporations using support vector machine, artificial neural network, and multivariate discriminant analysis

2019 ◽  
Vol 01 (01) ◽  
Author(s):  
Matsumaru Masanobu ◽  
KANEKO SHOICHI ◽  
Katagiri Hideki ◽  
Kawanaka Takaaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Stock Exchange ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Bankruptcy Risk ◽  
Artificial Neural

This study predicted the bankruptcy risk of companies listed in Japanese stock markets for the entire industry and individual industries using multiple discriminant analysis (MDA), artificial neural network (ANN), and support vector machine (SVM) and compared the methods to determine the best one. The financial statements of the companies listed in the Tokyo Stock Exchange in Japan were used as data. The data of 244 companies that went bankrupt between 1991 and 2015 were used. Additionally, the data of 64,708 companies that did not go bankrupt between 1991 and 2015 (24 years) were used. The data was acquired from the Nikkei NEEDS database. It was found from the results of empirical analysis that the SVM is more accurate than the other models in predicting the bankruptcy risk of companies. In the ANN analysis and MDA, bankruptcy prediction could be made accurately only for some individual industries. In contrast, the SVM could predict the bankruptcy risk of companies almost perfectly for either entire and individual industries. This bankruptcy prediction model can help customers, investors, and financiers prevent losses by focusing on the financial indicators before finalizing transactions.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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