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Yayın Neural network as a forecasting tool for financial decision-making(Işık Üniversitesi, 2008-09-18) Görgün, Onur; Perdahçı, Nazım Ziya; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Enformasyon Teknolojileri Yüksek Lisans ProgramıFor the last decade, machine learning techniques have been applied to financial tasks such as portfolio management, risk assessment and stock market prediction. Among these techniques artificial neural network as a machine learning algorithm is the most widely used model. In stock market environment, multi layer perceptron with backpropagation model is dominant among others in stock market prediction. This study examines the forecasting power of multi layer perceptron models for predicting the direction of ISE 100 daily index value. The results show that multi layer perceptron has a promising power in predicting the stock market trend. However, it also shows that selection of input variables is dominant factor in stock market prediction to obtain accurate results.Yayın English to Turkish machine translation using synchronous grammars(Işık Üniversitesi, 2022-06-14) Görgün, Onur; Tüysüz Erman, Ayşegül; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora ProgramıMachine translation (MT) has been one of the hot topics in NLP research over recent years. However, most of the related studies have been done for specific languages, and there are a limited number of comprehensive studies for languages with free word order, such as Turkish. English-Turkish is also one of the least frequently studied language pairs in translation due to the morphological and syntactic gaps between the two languages. This also makes it hard to build parallel corpora, which is crucial for the machine translation task. This thesis aims to be the first statistical syntax tree-based machine translation approach to the English-Turkish language pair, as well as a parallel corpus for translation tasks. We construct an English-Turkish parallel treebank of approximately 17K sentences by following a three-phased approach: manual transformation of English trees from Penn Treebank (PTB) by constraining the translated trees to the reordering of the children and gloss replacement; morphological analysis of the translated gloss; and morphological enrichment of the target tree. For translation consistency, we also developed a set of tools. We also apply the transformation schema to the closed-domain and build 8.3K sentences corpus. We employ both corpora on machine translation task. In our experiments, we obtained a 12.8 BLEU score in the open-domain and a 26.8 BLEU score in the closed-domain. We also evaluate both corpora intrinsically through perplexity analysis. The results show that our studies on making a corpus can be repeated, and studies on machine translation using the small corpus look promising.
