Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Browsing Bilgisayar ve Bilişim Fakültesi Koleksiyonu by Department "KATÜN"

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    Article
    Citation - WoS: 1
    Academic Text Clustering Using Natural Language Processing
    (2022) Taşkıran, Fatma; Kaya, Ersin
    Accessing data is very easy nowadays. However, to use these data in an efficient way, it is necessary to get the right information from them. Categorizing these data in order to reach the needed information in a short time provides great convenience. All the more, while doing research in the academic field, text-based data such as articles, papers, or thesis studies are generally used. Natural language processing and machine learning methods are used to get the right information we need from these text-based data. In this study, abstracts of academic papers are clustered. Text data from academic paper abstracts are preprocessed using natural language processing techniques. A vectorized word representation extracted from preprocessed data with Word2Vec and BERT word embeddings and representations are clustered with four clustering algorithms.
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    Classification of Sleep Stages Using Psg Recording Signals
    (2020) Koca, Yasin; Özşen, Seral; Göğüş, Fatma Zehra; Tezel, Gülay; Küççüktürk, Serkan; Vatansev, Hülya
    Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
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    Article
    Citation - WoS: 1
    Clustering Neighborhoods According To Urban Functions and Development Levels by Different Clustering Algorithms: a Case in Konya
    (2022) Akar, Alı Utku; Uymaz, Sait Ali
    Urban functions/activities, which emerged under the influence of the human factor and are in the process of development over time, play a crucial role in the development of neighborhoods. To ensure balanced development status among the neighborhoods, it is necessary to know the development levels of the neighborhoods in advance. This study focuses on the clustering of the 167 central neighborhoods in Konya in terms of urban functions and reveals the similarities or differences in the development status of these neighborhoods. K-means, Hierarchical (agglomerative) and OPTICS clustering analyzes were used to cluster central neighborhoods. 18 features related to urban functions were determined as input parameters in the clustering analyzes. Results showed that cluster analysis can be used in urban studies and determine the development status of cities. It is important to carry out clustering studies to make urban planning by revealing the development differences between the neighborhoods and to provide more appropriate service delivery.
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    Nesnelerin İnterneti Kapsamında Kullanılan Ara Katman Yazılımlarına Yönelik Ağ Benzetimi
    (2022) Kılıç, Alper
    Nesnelerin İnterneti (IoT) kapsamında çok sayıda veri üretici sistem belirli bir ağ üzerinde veri alışverişinde bulunurlar. Veri transferi için çeşitli avantajları bulunan DDS (Data Distribution Service) ara katman mimarisi veri merkezli ağ haberleşmesi için sıklıkla kullanılmaktadır. Ölçeklendirme, yönetim ve izleme amaçlarına yönelik olarak kullanılan ara katman mimarisinin sağladığı birçok servis kalitesi (QoS) özelliği ile güvenilir veri aktarımı gerçekleştirilir. Bununla birlikte, olası ağ kesintileri, yavaşlama ya da veri kaybı oluşturabilecek senaryolar için yazılım geliştirme aşamasında ağ benzetimi yapılması, olası hataların erken tespiti ve düzeltilmesi maliyet ve zaman açısından faydalı olacaktır. Bu çalışmada DDS ara katman mimarisine yönelik ağ kesintisi, bant genişliği daralması, paket kaybı ve ağ topolojisine yönelik olası incelemeler için bir benzetim modeli ve yazılım mimarisi önerilmiştir. Buna göre, veri iletim ağının belirli noktalarının davranışı değiştirilerek ağ gecikmesi, paket kaybı ya da servis kesintisi durumlarında yazılım davranışlarının incelenebilmesi sağlanmıştır. Ağ benzetimi ve test sistemi için bir arayüz yazılımı geliştirilerek ağ bağlantısının farklı durumlar için benzetimi amaçlanmıştır.