scholarly journals CLASSIFICATION OF PSYCHIATRIC DISORDERS USING MULTINOMIAL LOGISTIC REGRESSION VERSUS ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 18 (3) ◽  
pp. 395-408
Author(s):  
Elena Martín Pérez ◽  
Amaya Caldero Alonso ◽  
Quintín Martín Martín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Psychiatric Disorders ◽  
Multinomial Logistic Regression ◽  
Artificial Neural

scholarly journals RETRACTED: Analysis and prediction of β-turn types using multinomial logistic regression and artificial neural network

2007 ◽  
Vol 35 (12) ◽  
pp. e8-e15
Author(s):  
Poursheikhali Asgary Mehdi ◽  
Abdolmaleki Parviz ◽  
Kazemnejad Anoshirvan ◽  
Jahandidehs Samad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Artificial Neural

The Validity of Multinomial Logistic Regression and Artificial Neural Network in Predicting Sukuk Rating: Evidence from Indonesian Stock Exchange

2020 ◽  
Vol 23 (04) ◽  
pp. 2050032
Author(s):  
Muhammad Luqman Nurhakim ◽  
Zainul Kisman ◽  
Faizah Syihab
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Stock Exchange ◽  
Multinomial Logistic Regression ◽  
Global Market ◽  
Analysis Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The Sukuk (shariah bond) market is developing in Indonesia and potentially will capture the global market in the future. It is an attractive investment product and a hot current issue in the capital market. Especially, the problem of predicting an accurate and trustworthy rating. As the Sukuk market developed, the issue of Sukuk rating emerged. As ordinary investors will have difficulty predicting their ratings going forward, this research will provide solutions to the problems above. The objective of this study is to determine the Indonesian Sukuk rating determinants and comparing the Sukuk rating predictive model. This research uses Artificial Neural Network (ANN) and Multinomial Logistic Regression (MLR) as the predictive analysis model. Data in this study are collected by purposive sampling and employing Sukuk rated by PEFINDO, an Indonesian rating agency. Findings in this study are debt, profitability and firm size significantly affecting Sukuk rating category and the ANN performs better predictive accuracy than MLR. The implications of the results of the research for the issuer and bondholder are a higher level of credit enhancement, a higher level of profitability, and the bigger size of firm rewarding higher Sukuk rating.

scholarly journals Comparison of Predictive Models in Classification of Nutritional Status among Arsenic Affected People in Rural Areas in Bangladesh: An Artificial Neural Network (ANN) and Logistic Regression Approach

2017 ◽  
Vol 6 (2) ◽  
pp. 71-75
Author(s):  
Azizur Rahman ◽  
Mariam Akter ◽  
Ajit Kumar Majumder ◽  
Md Atiqul Islam ◽  
AFM Arshedi Sattar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Clinical Data ◽  
Predictive Models ◽  
Rural Areas ◽  
Binary Logistic Regression ◽  
Binary Outcome ◽  
Artificial Neural

Background: Clinical data play an important role in medical sector for binary outcome variables. Various methods can be applied to build predictive models for the clinical data with binary outcome variables.Objective: This research was aimed to explore and compare the process of constructing common predictive models.Methodology: Models based on an artificial neural network (the connectionist approach) and binary logistic regressions were compared in their ability to classifying malnourished subjects and those with over-weighted participants in rural areas of Bangladesh. Subjects were classified according to the indicator of nutritional status measured by body mass index (BMI). This study also investigated the effects of different factors on the BMI level of adults of six Villages in Bangladesh. Demographic, anthropometric and clinical data were collected based on aged over 30 years from six Villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic.Result: A total of 460 participants were recruited for this study. Out of 460(140 male and 320 females) participants 186(40.44%) were identified as malnourished (BMK18.5 gm), and the remainder 274(59.56%) were found as over-weighted (BMI>18.5 gm). Among other factors, arsenic exposures were found as significant risk factors for low body mass index (BMI) with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 72.85% of cases with malnourished in the training datasets, 76.08% in the testing datasets and 75.26% of all subjects. The sensitivities of the neural network architecture for the training and testing datasets and for all subjects were 84.28%, 84.78% and 81 .72% respectively, indicate better performance than binary logistic regression model.Conclusion: This study demonstrates a significant performance of artificial neural network than the binary logistic regression models in classification of malnourished participants from over-weighted ones.J Shaheed Suhrawardy Med Coll, 2014; 6(2):71-75

scholarly journals Analysis and identification of -turn types using multinomial logistic regression and artificial neural network

2007 ◽  
Vol 23 (23) ◽  
pp. 3125-3130 ◽  
Author(s):  
M. Poursheikhali Asgary ◽  
S. Jahandideh ◽  
P. Abdolmaleki ◽  
A. Kazemnejad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Artificial Neural

scholarly journals ON COMPARING MULTI-LAYER PERCEPTRON AND LOGISTIC REGRESSION FOR CLASSIFICATION OF DIABETIC PATIENTS IN FEDERAL MEDICAL CENTER YOLA, ADAMAWA STATE

2021 ◽  
Vol 8 (6) ◽  
pp. 24-40
Author(s):  
H. Ahmed ◽  
M. B. Mohammed ◽  
I. A. Baba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Medical Center ◽  
Oral Glucose ◽  
Diabetic Patients ◽  
Data Set ◽  
Artificial Neural

The logistic regression (LR) and Multi-Layer (MLP) are used to handle regression analysis when the dependent response variable is categorical. Therefore, this study assesses the performance of LR and MLP in terms of classification of object/observations into identified component/groups. A data set consists of 553 cases of diabetes were collected at Federal Medical Center, . The variables measured: Age(years), Mass of a patient(kg/meters), glucose level (plasma glucose concentration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure ), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The method used in the study is Logistic regression analysis and the multi-Layer , a type of Artificial Neural Network, confusion matrix, classification, network algorithm and SPSS version 21 for Windows 10.1. The result of the study showed that LP classifies diabetic patients correctly with 91.8% accuracy. it classifies non-diabetic patients with 89.1% accuracy. MLP classifies diabetic patients with 88.6% accuracy while it classifies non-diabetic patients with 93.2% classification accuracy. Overall, MLP classifies better with 91% accuracy while LR classifies with 90.6% accuracy. This study complements other where MLP, a type Artificial neural network classifies and predicts better than other non-neural network classifiers.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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