Yazar "Akyokuş, Selim" için Makale Koleksiyonu listeleme
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Deep learning- and word embedding-based heterogeneous classifier ensembles for text classification
Kilimci, Zeynep Hilal; Akyokuş, Selim (Wiley-Hindawi, 2018)The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble ... -
A hybrid deep model using deep learning and dense optical flow approaches for human activity recognition
Tanberk, Senem; Kilimci, Zeynep Hilal; Bilgin Tükel, Dilek; Uysal, Mitat; Akyokuş, Selim (IEEE - Institute of Electrical and Electronics Engineers, Inc., 2020)Human activity recognition is a challenging problem with many applications including visual surveillance, human-computer interactions, autonomous driving and entertainment. In this study, we propose a hybrid deep model to ... -
An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
Kilimci, Zeynep Hilal; Akyüz, A. Okay; Uysal, Mitat; Akyokuş, Selim; Uysal, Mustafa Ozan; Atak Bülbül, Berna; Ekmiş, Mehmet Ali (Wiley-Hindawi, 2019)Demand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand ... -
Modeling traders' behavior with deep learning and machine learning methods: Evidence from BIST 100 index
Hasan, Afan; Kalıpsız, Oya; Akyokuş, Selim (Wiley-Hindawi, 2020)Although the vast majority of fundamental analysts believe that technical analysts' estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and ... -
Mood detection from physical and neurophysical data using deep learning models
Kilimci, Zeynep Hilal; Güven, Aykut; Uysal, Mitat; Akyokuş, Selim (Wiley, 2019)Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like ...