Çevik, Mesut

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Profile URL
https://hdl.handle.net/20.500.12469/6961
Name Variants
Çevik, Mesut
M.,Çevik
M. Çevik
Mesut, Çevik
Cevik, Mesut
M.,Cevik
M. Cevik
Mesut, Cevik
Cevik, M.
Job Title
Öğr. Gör
Email Address
Mesut.cevı[email protected]
Main Affiliation
Computer Engineering
Status
Former Staff
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Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

4

QUALITY EDUCATION
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6

CLEAN WATER AND SANITATION
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10

REDUCED INEQUALITIES
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13

CLIMATE ACTION
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14

LIFE BELOW WATER
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2

ZERO HUNGER
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8

DECENT WORK AND ECONOMIC GROWTH
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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17

PARTNERSHIPS FOR THE GOALS
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1

NO POVERTY
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SUSTAINABLE CITIES AND COMMUNITIES
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15

LIFE ON LAND
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3

GOOD HEALTH AND WELL-BEING
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3

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7

AFFORDABLE AND CLEAN ENERGY
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5

GENDER EQUALITY
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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Scholarly Output

5

Articles

1

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1/0

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

4

Scopus Citation Count

10

WoS h-index

1

Scopus h-index

2

Patents

0

Projects

0

WoS Citations per Publication

0.80

Scopus Citations per Publication

2.00

Open Access Source

2

Supervised Theses

1

JournalCount
2020 Medical Technologies Congress (TIPTEKNO)1
EURASIP Journal on Advances in Signal Processing1
TIPTEKNO 2019 - Tip Teknolojileri Kongresi1
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Now showing 1 - 5 of 5
  • Thumbnail Image
    Conference Object
    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.
  • Thumbnail Image
    Conference Object
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 6
    A Low-Complexity Time-Domain Mmse Channel Estimator for Space-time/Frequency Block-Coded Ofdm Systems
    (Hindawi Publishing Corporation, 2006) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, Erdal
    Focusing on transmit diversity orthogonal frequency-division multiplexing (OFDM) transmission through frequency-selective channels this paper pursues a channel estimation approach in time domain for both space-frequency OFDM (SF-OFDM) and space-time OFDM (ST-OFDM) systems based on AR channel modelling. The paper proposes a computationally efficient pilot-aided linear minimum mean-square-error (MMSE) time-domain channel estimation algorithm for OFDM systems with transmitter diversity in unknown wireless fading channels. The proposed approach employs a convenient representation of the channel impulse responses based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion no matrix inversion is required in the proposed MMSE estimator. Subsequently optimal rank reduction is applied to obtain significant taps resulting in a smaller computational load on the proposed estimation algorithm. The performance of the proposed approach is studied through the analytical results and computer simulations. In order to explore the performance the closed-form expression for the average symbol error rate (SER) probability is derived for the maximum ratio receive combiner (MRRC). We then consider the stochastic Cramer-Rao lower bound(CRLB) and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. Simulation results confirm our theoretical analysis and illustrate that the proposed algorithms are capable of tracking fast fading and improving overall performance. Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 4
    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.