Yönetim Bilişim Sistemleri Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/68

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Now showing 1 - 4 of 4

Citation - WoS: 4
Citation - Scopus: 5
Development of a Supervised Classification Method To Construct 2d Mineral Maps on Backscattered Electron Images
(Tubitak, 2020) Camalan, Mahmut; Çavur, Mahmut
The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.
Citation - WoS: 18
Citation - Scopus: 38
The Impact of Text Preprocessing on the Prediction of Review Ratings
(Tubitak, 2020) Işık, Muhittin; Dağ, Hasan
With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing strategies are found out to increase the classification accuracy. In this paper, we concentrate on the impact of simple text preprocessing decisions in order to predict fine-grained review rating stars whereas the majority of previous work focused on the binary distinction of positive vs. negative. Therefore, the aim of this research is to analyze preprocessing techniques and their influence, at the same time explain the interesting observations and results on the performance of a five-class-based review rating classifier.
Konutların Günlük Elektrik Güç Tüketimi Tahmini İçin Uygun Model Seçimi
(Fırat Üniv. Fen Bilimleri Enst., 2018) Çayır, Aykut; Dağ, Hasan; Yenidoğan, Işıl
Zamana bağlı değişim gösteren olayların modellenmesi zorlu bir veri analizi problemidir. Bu olaylardan biri olan elektrik güç tüketiminde ise veriden mevsimsel etki ve tatil günleri gibi örüntülerin öğrenilerek bir tüketim tahmin modelinin geliştirilebilmesi için klasik makine öğrenmesi ve derin öğrenme yöntemlerinden yararlanılmaktadır. Bu çalışmada, İngiltere’nin Londra şehrindeki belirli bir bölgede 30 farklı eve ait yaklaşık 3 yıllık elektrik güç tüketimi veri kümesi kullanılarak uygun bir kısa vadeli tüketim tahmin modelinin makine öğrenmesi algoritmaları ile bulunması amaçlanmıştır.
A Sparsity-Preserving Spectral Preconditioner for Power Flow Analysis
(TUBITAK Scientific & Technical Research Council Turkey, 2016) Yetkin, Emrullah Fatih; Dağ, Hasan
Due to the ever-increasing demand for more detailed and accurate power system simulations the dimensions of mathematical models increase. Although the traditional direct linear equation solvers based on LU factorization are robust they have limited scalability on the parallel platforms. On the other hand simulations of the power system events need to be performed at a reasonable time to assess the results of the unwanted events and to take the necessary remedial actions. Hence to obtain faster solutions for more detailed models parallel platforms should be used. To this end direct solvers can be replaced by Krylov subspace methods (conjugate gradient generalized minimal residuals etc.). Krylov subspace methods need some accelerators to achieve competitive performance. In this article a new preconditioner is proposed for Krylov subspace-based iterative methods. The proposed preconditioner is based on the spectral projectors. It is known that the computational complexity of the spectral projectors is quite high. Therefore we also suggest a new approximate computation technique for spectral projectors as appropriate eigenvalue-based accelerators for efficient computation of power flow problems. The convergence characteristics and sparsity structure of the preconditioners are compared to the well-known black-box preconditioners such as incomplete LU and the results are presented.

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