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Tolun, Mehmet Reşit

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12416/8813
Name Variants
Tolun, Mehmet
Tolun, Mehmet Resit
Tolun, Mehmet R.
Tolun, M.R.
Job Title
Prof. Dr.
Email Address
tolun@cankaya.edu.tr
Main Affiliation
Yazılım Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-8478-7220
Scopus Author ID
6603446979
Turkish CoHE Profile ID
1863
Google Scholar ID
VMq3WowAAAAJ
WoS Researcher ID
KCJ-5958-2024
Files
00564.jpg (679.45 KB)

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
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1

Research Products

3

GOOD HEALTH AND WELL-BEING
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3

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

Research Products

6

CLEAN WATER AND SANITATION
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0

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14

LIFE BELOW WATER
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0

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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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0

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8

DECENT WORK AND ECONOMIC GROWTH
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0

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1

NO POVERTY
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0

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4

QUALITY EDUCATION
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0

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5

GENDER EQUALITY
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0

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10

REDUCED INEQUALITIES
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0

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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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0

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15

LIFE ON LAND
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0

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7

AFFORDABLE AND CLEAN ENERGY
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1

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13

CLIMATE ACTION
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1

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17

PARTNERSHIPS FOR THE GOALS
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1

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2

ZERO HUNGER
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0

Research Products
Documents

43

Citations

389

h-index

7

Go to Scopus profile
Documents

35

Citations

281

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Scholarly Output

40

Articles

13

Views / Downloads

3953/2401

Supervised MSc Theses

3

Supervised PhD Theses

0

WoS Citation Count

136

Scopus Citation Count

190

WoS h-index

3

Scopus h-index

4

Patents

0

Projects

0

WoS Citations per Publication

3.40

Scopus Citations per Publication

4.75

Open Access Source

6

Supervised Theses

3

JournalCount
Journal of Medical Systems2
1st International Conference on Advances in Multimedia -- JUL 20-25, 2009 -- Colmar, FRANCE1
1st International Conference on Electronic Healthcare -- SEP 08-09, 2008-2009 -- London, ENGLAND1
2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 -- 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 -- 31 March 2009 through 2 April 2009 -- Los Angeles, CA -- 783681
2015 10th International Conference on Information, Communications and Signal Processing (ICICS)1
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Scopus Quartile Distribution

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Scholarly Output Search Results

Now showing 1 - 10 of 40
  • Thumbnail Image
    Conference Object
    Production and Retrieval off Rough Classes in Multi Relations
    (IEEE Computer Soc, 2007) Sever, Hayri; Gorur, A. Kadir; Tolun, Mehmet R.
    Organizational memory in today's business world forms basis for organizational learning, which is the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures. This basically requires consideration of different aspects of knowledge that may reside on top of a conventional information management system. Of them, representation, retrieval and production issues of meta patterns constitute to the main theme of this article. Particularly we are interested in a formal approach to handle rough concepts. We utilize rough classifiers to propose a preliminary framework based on minimal term sets with p-norms to extract meta patterns. We describe a relational rule induction approach, which is called rila. Experimental results are provided on the mutagenesis, and the KDD Cup 2001 genes data sets.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    An Intelligent System for Detecting Mediterranean Fruit Fly [Medfly; Ceratitis Capitata (Wiedemann)]
    (Pagepress Publ, 2022) Eyyuboglu, Halil Tanyer; Sari, Filiz; Uzun, Yusuf; Tolun, Mehmet Resit
    Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.
  • Thumbnail Image
    Article
    Citation - Scopus: 2
    Damage Detection in Aircraft Engine Borescope Inspection Using Deep Learning
    (Springer Science and Business Media Deutschland GmbH, 2025) Uzun, I.; Tolun, M.R.; Sari, F.; Alpaslan, F.N.
    Aircraft engine inspection is a key pillar of aviation safety as it helps to maintain adequate performance standards to ensure engine airworthiness. In addition, it is also vital for asset value retention. Borescope inspection is currently the most widely used visual inspection method for aircraft engines. However, borescope inspection is a time-consuming, subjective, and complex process that heavily depends on the experience and attention level of the inspector. Moreover, the cost savings of airlines and the maintenance, repair, and overhaul (MRO) centers expose pressure and workload on inspectors. These factors make an automated system to support damage detection during borescope inspection necessary in order to mitigate potential risks. In this paper, we propose a deep learning-based automated damage detection framework that employs aircraft engine borescope inspection images. Faster R-CNN-based deep learning model with Inception v2 feature extractor is utilized for the present architecture. Due to the limited number of images, data augmentation and other overfitting methods are also employed. The framework supports crack, burn, nick, and dent damage types across all modules of turbofan engines. It is trained and validated with moderate to complex borescope images obtained from the field. The framework achieves 92.64% accuracy for crack, 92.05% for nick or dent, and 81.14% for burn damage classes, with an overall 88.61% average accuracy. © The Author(s) 2025.
  • Thumbnail Image
    Conference Object
    Yeşil BHT Bilgi ve Haberleşme Teknolojileri Akademisyen ve Uygulayıcılar Açısından Bir İnceleme
    (2011) Akba, Fırat; Medeni, İhsan Tolga; Medeni, Tunç Durmuş; Tolun, Mehmet Reşit; Öztürk, Mehmet
  • Thumbnail Image
    Publication
    Production and retrieval off rough classes in multi relations
    (IEEE Computer Soc, 2007) Tolun, Mehmet R.; Sever, Hayri; Görür, Abdül Kadir
    Organizational memory in today's business world forms basis for organizational learning, which is the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures. This basically requires consideration of different aspects of knowledge that may reside on top of a conventional information management system. Of them, representation, retrieval and production issues of meta patterns constitute to the main theme of this article. Particularly we are interested in a formal approach to handle rough concepts. We utilize rough classifiers to propose a preliminary framework based on minimal term sets with p-norms to extract meta patterns. We describe a relational rule induction approach, which is called rila. Experimental results are provided on the mutagenesis, and the KDD Cup 2001 genes data sets.
  • Thumbnail Image
    Publication
    Clustering Analysis for Vasculitic Diseases
    (Springer-Verlag Berlin, 2010) Yıldırım, Pınar; Çeken, Çınar; Çeken, Kağan; Tolun, Mehmet R.
    We introduce knowledge discovery for vasculitic diseases in this paper. Vasculitic diseases affect some organs and tissues and diagnosing can be quite difficult. Biomedical literature can contain hidden and useful knowledge for biomedical research and we develop a study based on co-occurrence analysis by using the articles in MEDLINE which is a widely used database. The mostly seen vasculitic diseases are selected to explore hidden patterns. We select PolySearch system as a web based biomedical text mining tool to find organs and tissues in the articles and create two separate datasets with their frequencies for each disease. After forming these datasets, we apply hierarchical clustering analysis to find similarities between the diseases. Clustering analysis reveals some similarities between diseases. We think that the results of clustered diseases positively affect on the medical research of vasculitic diseases especially during the diagnosis and certain similarities can provide different views to medical specialists.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Component-Based Project Estimation Issues for Recursive Development
    (Springer, 2008) Altunel, Yusuf; Tolun, Mehmet R.
    In this paper we investigated the component-based specific issues that might affect project cost estimation. Component-based software development changes the style of software production. With component-based approach the software is developed as the composition of reusable software components. Each component production process must be treated as a stand-alone software project, which needs individual task of management. A typical pure component-based development can be considered as decomposition/integration activities successively applied at different levels and therefore results in recursive style of development. We analyzed and presented our results of studies on the component-based software development estimation issues from recursive point of view.
  • Thumbnail Image
    Master Thesis
    Makine Öğrenmesi Teknikleri Kullanılarak Sybil Botların Tespit Edilmesi
    (2025) Öcel, Cansu Betül; Tolun, Mehmet Reşit
    Bu çalışma, NSL-KDD veri seti kullanılarak ağ tabanlı anomali tespiti amacıyla çeşitli makine öğrenmesi algoritmalarının performansını karşılaştırmalı olarak değerlendirmeyi amaçlamaktadır. NSL-KDD, saldırı türlerini dört ana başlıkta (DoS, Probe, R2L, U2R) toplayan, etiketli ve dengeli yapısıyla denetimli öğrenme yöntemleri için uygun bir veri seti olarak ele alınmıştır. Çalışma kapsamında veri seti üzerinde öncelikle istatistiksel analizler ve veri keşif çalışmaları gerçekleştirilmiş, ardından veri ön işleme adımları uygulanmıştır. Bu süreçte kategorik değişkenler sayısal forma dönüştürülmüş, eksik veriler temizlenmiş ve azınlıkta kalan sınıflar SMOTE yöntemiyle dengelenmiştir. Özellik seçimi için Mutual Information (MI) yöntemi kullanılarak en bilgilendirici 15 değişken belirlenmiş ve model eğitimi bu özellikler kullanılarak gerçekleştirilmiştir. Sonrasında tüm değişkenler kullanılarak modeller tekrar eğitilmiş ve sonuçlar kıyaslanmıştır. Modelleme aşamasında Lojistik Regresyon, Naive Bayes, Random Forest, K En Yakın Komşu (KNN), Destek Vektör Makineleri (SVM), AdaBoost ve Yapay Sinir Ağı (ANN) algoritmaları kullanılmıştır. Her model için hiper parametre optimizasyonu GridSearchCV veya RandomizedSearchCV yöntemleriyle yapılmıştır. Modellerin başarısı doğruluk (accuracy), kesinlik (precision), duyarlılık (recall) ve F1 skoru gibi değerlendirme metrikleri kullanılarak analiz edilmiştir.Elde edilen sonuçlar, NSL-KDD veri seti üzerinde bazı modellerin özellikle DoS gibi baskın sınıflarda yüksek doğruluk sağlarken, azınlıkta kalan R2L ve U2R saldırı türlerinde performans düşüşleri yaşandığını göstermektedir. Bu durum, dengesiz veri setlerinde kullanılacak yöntemlerin dikkatli seçilmesinin gerekliliğine işaret etmektedir.
  • Thumbnail Image
    Master Thesis
    Data Analysis and Model Development of Energy Production in Turkey
    (2025) Namlı, Sefa Yasin; Tolun, Mehmet Reşit
    Bu çalışma, Türkiye'nin elektrik enerjisi üretimi, tüketimi ve dağıtım sistemlerini kapsamlı bir şekilde ele almaktadır. Elektrik enerjisi üretiminde yenilenebilir ve yenilenemez enerji kaynaklarının mevcut durumu analiz edilerek, bu kaynakların enerji arz güvenliğine ve çevresel sürdürülebilirliğe etkisi tartışılmıştır. Türkiye'nin enerji tüketim verileri yıllara göre detaylı bir şekilde değerlendirilmiş, bölgesel ve sektörel farklılıklar istatistiksel yöntemler kullanılarak incelenmiştir. Enerji dağıtım altyapısının mevcut durumu ve karşılaşılan zorluklar analiz edilmiş, özellikle yenilenebilir enerji kaynaklarının elektrik dağıtımına entegrasyonunun potansiyeli ortaya konulmuştur. Veri analizi süreçlerinde doğrusal regresyon modelleri ve diğer istatistiksel yöntemler kullanılmış; bu sayede enerji üretim ve tüketim trendleri analiz edilmiş ve geleceğe yönelik tahminler yapılmıştır. Elde edilen sonuçlar, Türkiye'nin enerji ithalatına bağımlılığını azaltacak, enerji verimliliğini artıracak ve çevresel etkileri minimize edecek stratejik öneriler sunmaktadır. Çalışma, hem enerji sektörüne hem de politika yapıcılara yenilikçi çözümler ve sürdürülebilir enerji yönetimi için yol gösterici bir rehber niteliğindedir.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Clustering Analysis for Vasculitic Diseases
    (Springer-verlag Berlin, 2010) Yildirim, Pinar; Ceken, Cinar; Ceken, Kagan; Tolun, Mehmet R.
    We introduce knowledge discovery for vasculitic diseases in this paper. Vasculitic diseases affect some organs and tissues and diagnosing can be quite difficult. Biomedical literature can contain hidden and useful knowledge for biomedical research and we develop a study based on co-occurrence analysis by using the articles in MEDLINE which is a widely used database. The mostly seen vasculitic diseases are selected to explore hidden patterns. We select PolySearch system as a web based biomedical text mining tool to find organs and tissues in the articles and create two separate datasets with their frequencies for each disease. After forming these datasets, we apply hierarchical clustering analysis to find similarities between the diseases. Clustering analysis reveals some similarities between diseases. We think that the results of clustered diseases positively affect on the medical research of vasculitic diseases especially during the diagnosis and certain similarities can provide different views to medical specialists.