Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Ospanova, Tleugaisha
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Forecasting creditworthiness in credit scoring using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Amirov, Azamat; Tassuov, Bolat; Makhatova, Valentina; Kaipova, Assemgul; Makhazhanova, Ulzhan; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5534-5542

Abstract

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Development of an algorithm for identifying the autism spectrum based on features using deep learning methods Amirbay, Aizat; Mukhanova, Ayagoz; Baigabylov, Nurlan; Kudabekov, Medet; Mukhambetova, Kuralay; Baigusheva, Kanagat; Baibulova, Makbal; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5513-5523

Abstract

The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.
Co-Authors Amirbay, Aizat Amirov, Azamat Baibulova, Makbal Baigabylov, Nurlan Baigusheva, Kanagat Baitemirov, Madiyar Kaipova, Assemgul Kudabekov, Medet Makhatova, Valentina Makhazhanova, Ulzhan Mukhambetova, Kuralay Mukhanova, Ayagoz Tassuov, Bolat