Ahmet Esad Top

Ahmet Esad Top
Ankara Yildirim Beyazit University | AYBU · Department of Computer Engineering

Master of Science

About

5
Publications
1,352
Reads
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4
Citations
Citations since 2016
5 Research Items
4 Citations
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20162017201820192020202120220.00.51.01.52.0
20162017201820192020202120220.00.51.01.52.0
20162017201820192020202120220.00.51.01.52.0

Publications

Publications (5)
Conference Paper
Full-text available
In the age of globalization where everything is data driven, complex data generation has increased. Internet is flooded with all types of textual data. Consequently, extraction of information from such bulks of data has become very important and text classification has made this task relatively easier. In this paper, deep CNN, deep LSTM and their h...
Article
Full-text available
In digital image processing, image segmentation is an essential step in which an image is partitioned into groups of pixels. k-means clustering algorithm, which is often considered as fast and efficient, is one of the most widely used clustering algorithms to segment an image. However, as the problem size gets larger, the k-means starts to spend a...
Conference Paper
Full-text available
Image segmentation, which is the process of partitioning an image into multiple sets of pixels, is used in many application areas. Clustering is one popular technique for image segmentation. The clustering task can be done by various algorithms, one of the most popular algorithms is K-means clustering algorithm. Although the K-means clustering algo...
Thesis
Full-text available
Previous studies on classifying Electroencephalography (EEG) sleep data generally use the signal itself. Many of these studies need series of pre-processing operations, manual feature extraction, complex and hard application processes. There is no need for lots of pre-processing stages in Convolutional Neural Networks (CNN) and features can be lear...
Conference Paper
Full-text available
Previous studies on classifying Electroencephalography (EEG) sleep data generally use the signal itself. Many of these studies need series of pre-processing operations, manual feature extraction, complex and hard application processes. There is no need for lots of pre-processing stages in Convolutional Neural Networks (CNN) and features can be lear...

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