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Toplam kayıt 8, listelenen: 1-8
The analysis of text categorization represented with word embeddings using homogeneous classifiers
(Institute of Electrical and Electronics Engineers Inc., 2019)
Text data mining is the process of extracting and analyzing valuable information from text. A text data mining process generally consists of lexical and syntax analysis of input text data, the removal of non-informative ...
Sentiment analysis based churn prediction in mobile games using word embedding models and deep learning algorithms
(Institute of Electrical and Electronics Engineers Inc., 2020)
Customer churn is one of the most important problems for many industries, including banking, telecommunications, and gaming. In the gaming market, it is observed that the demand on game applications rises with the usage ...
The evaluation of word embedding models and deep learning algorithms for Turkish text classification
(IEEE (Institute of Electrical and Electronics Engineers), 2019)
The use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system. Word embedding models are ...
A hybrid deep model using deep learning and dense optical flow approaches for human activity recognition
(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 ...
Deep learning- and word embedding-based heterogeneous classifier ensembles for text classification
(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 ...
An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
(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
(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
(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 ...