Çevik, Mesut

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Çevik, Mesut
M.,Çevik
M. Çevik
Mesut, Çevik
Cevik, Mesut
M.,Cevik
M. Cevik
Mesut, Cevik
Cevik, M.
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Öğr. Gör
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Mesut.cevık@khas.edu.tr
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Scholarly Output

4

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Now showing 1 - 4 of 4
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    Master Thesis
    Anomaly Detection Via Machine Learning
    (Kadir Has Üniversitesi, 2023) ERDEM, GÖRKEM; Kerestecioglu, Feza; Çevik, Mesut
    Retail companies monitor inventory stock levels regularly and manage stock levels based on forecasted sales to sustain their market position. The accuracy of inventory stocks is critical for retail companies to create a correct strategy. Many retail com- panies try to detect and prevent inventory record inaccuracy caused by employee or customer theft, damage or spoilage and wrong shipments. This study is aimed to detect inaccurate stocks using machine learning methods. It uses the real inven- tory stock data of Migros Ticaret A.S¸. of Turkey’s largest supermarket chains. A multiple of machine learning algorithms such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM) were used to detect abnormal stock values. On the other hand, generally, researchers use public data to develop methods, and it is challenging to apply machine learning algorithms to real-life data, especially in unsupervised learning. This thesis shows how to handle real-life data noises, missing values etc. The experimental findings show the perfor- mances of machine learning methods in detecting anomalies in low and high level inventory stock.
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    Conference Object
    Citation Count: 0
    Smart Stethoscope
    (IEEE, 2020) Türker, Mehmet Nasuhcan; Çagan, Yagiz Can; Yıldırım, Batuhan; Demirel, Mücahit; Özmen, Atilla; Tander, Baran; Çevik, Mesut
    In this study, a device named smart stethoscope that uses digital sensor technology for sound capture, active acoustics for noise cancellation and artificial intelligence (AI) for diagnosis of heart and lung diseases is developed to help the health workers to make accurate diagnoses. Furthermore, the respiratory diseases are classified by using Deep Learning and Long Short-Term Memory (LSTM) techniques whereas the probability of these diseases are obtained.
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    Conference Object
    Citation Count: 0
    Classification of Adhd Using Ensemble Algorithms With Deep Learning and Hand Crafted Features
    (IEEE, 2019) Çiçek, Gülay; Çevik, Mesut; Akan, Aydın
    Attention Deficit Hyperactivity (ADHD) is a common neurodevelopmental disorder that typically appears in early childhood. Methods developed for diagnosing gives different results at different times. This is a major obstacle in the diagnosis of disease. Diagnosis model of ADHD must be unique, objective, and reliable. In this study, comparative evaluations of both manual and deep features for classification of structural magnetic resonance images is presented. For this purpose, datasets of NPIstanbul Neuropsychiatry Hospital and public datasets of ADHD-200 is used. In order to characterize MRI images First Order, Second Order statictical features and the Alexnet architecture is used. Images are classified with the ensemble algorithm. In order to determine classification performance, accuracy, sensitivity, specificity, tp rate, fp rate and F-measure values are taken into consideration. It was observed that the combination of three manually extracted data sets yielded more successful results in characterizing the data.
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    Conference Object
    Citation Count: 0
    Classification of Adhd Using Ensemble Algorithms With Deep Learning and Hand Crafted Features
    (Institute of Electrical and Electronics Engineers Inc., 2019) Cicek, G.; Cevik, M.; Akan, A.
    Attention Deficit Hyperactivity (ADHD) is a common neurodevelopmental disorder that typically appears in early childhood. Methods developed for diagnosing gives different results at different times. This is a major obstacle in the diagnosis of disease. Diagnosis model of ADHD must be unique, objective, and reliable. In this study, comparative evaluations of both manual and deep features for classification of structural magnetic resonance images is presented. For this purpose, datasets of NPIstanbul Neuropsychiatry Hospital and public datasets of ADHD-200 is used. In order to characterize MRI images First Order, Second Order statictical features and the Alexnet architecture is used. Images are classified with the ensemble algorithm. In order to determine classification performance, accuracy, sensitivity, specificity, tp rate, fp rate and F-measure values are taken into consideration. It was observed that the combination of three manually extracted data sets yielded more successful results in characterizing the data. © 2019 IEEE.