TR-Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1927
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Article Citation - WoS: 1Facial Emotion Recognition Using Residual Neural Networks(Aves, 2024) Kırbız, Serap; 02.05. Department of Electrical and Electronics Engineering; 02. Faculty of Engineering; 01. MEF UniversityFacial emotion recognition (FER) has been an emerging research topic in recent years. Recent automatic FER systems generally apply deep learning methods and focus on two important issues: lack of sufficient labeled training data and variations in images such as illumination, pose, or expression-related variations among different cultures. Although Convolutional Neural Networks (CNNs) are widely used in automatic FER, they cannot be used when the number of layers is large. Therefore, a residual technique is applied to CNNs and this architecture is named residual neural network. In this paper, an automatic facial emotion recognition method using residual networks with random data augmentation is proposed on a merged FER dataset consisting of 41,598 facial images of size 48 × 48 pixels from seven basic emotion classes. Experimental results show that ResNet34 with data augmentation performs better than CNN with a classification accuracy of 81%.Article Citation - WoS: 1Citation - Scopus: 2Determining and Evaluating New Store Locations Using Remote Sensing and Machine Learning(Tübitak, 2021) Ünsalan, Cem; Küçükaydın, Hande; Küçükaydın, Hande; Höke, Berkan; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF UniversityDecision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model, which calculates store revenues, shouldbe rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques, which provide a novel way for evaluating new store locations.Article Citation - WoS: 4Citation - Scopus: 6Consumer Loans' First Payment Default (fpd) Detection and Predictive Model(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL, 2020) Sevgili, Türkan; Koç, Utku; Koç, Utku; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe project is based on the opinion that whether the loan applications which are profitable could be granted instead of prone the default (FPD) ones by using predictive models in machine learning by the credit decision authorities in banking sector. Default Loan (also called non-performing loan) occurs when there is a failure to meet bank conditions and cannot be repaid in accordance with the terms of the loan which has reached its maturity. This report is a research effort in the analysis of default loan applicants, especially FPD, from a real dataset obtained from a bank. Expectation from the study is that increase the efficiency of consumer loan allocation by providing predictive analysis of the consumer behavior concerning loan’s first payment default. FPD detection analysis is a crucial role for the determination of consumer loans at the application level. The study also provides an understanding on the reasons of non-performing loans and helps to manage credit risks more consciously. The methods proposed in this study can be extended to other individual consumer loans such as car credits and mortgage.