Çavur, Mahmut
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Çavur, Mahmut
M.,Çavur
M. Çavur
Mahmut, Çavur
Cavur, Mahmut
M.,Cavur
M. Cavur
Mahmut, Cavur
cavur, mahmut
Çavur, Mehmet
Cavur, M.
Çavur, M.
M.,Çavur
M. Çavur
Mahmut, Çavur
Cavur, Mahmut
M.,Cavur
M. Cavur
Mahmut, Cavur
cavur, mahmut
Çavur, Mehmet
Cavur, M.
Çavur, M.
Job Title
Email Address
Main Affiliation
Management Information Systems
Management Information Systems
03. Faculty of Economics, Administrative and Social Sciences
01. Kadir Has University
Management Information Systems
03. Faculty of Economics, Administrative and Social Sciences
01. Kadir Has University
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
4
QUALITY EDUCATION
1
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
10
REDUCED INEQUALITIES
0
Research Products
13
CLIMATE ACTION
0
Research Products
14
LIFE BELOW WATER
1
Research Products
2
ZERO HUNGER
2
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
1
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
1
NO POVERTY
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
2
Research Products
15
LIFE ON LAND
2
Research Products
3
GOOD HEALTH AND WELL-BEING
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
3
Research Products
5
GENDER EQUALITY
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output
19
Articles
13
Views / Downloads
11/0
Supervised MSc Theses
3
Supervised PhD Theses
0
WoS Citation Count
87
Scopus Citation Count
142
WoS h-index
5
Scopus h-index
7
Patents
0
Projects
0
WoS Citations per Publication
4.58
Scopus Citations per Publication
7.47
Open Access Source
13
Supervised Theses
3
| Journal | Count |
|---|---|
| Remote Sensing | 2 |
| Politeknik Dergisi | 2 |
| Journal of Polytechnic | 1 |
| Physical Communication | 1 |
| Powder Technology | 1 |
Current Page: 1 / 3
Scopus Quartile Distribution
Competency Cloud
19 results
Scholarly Output Search Results
Now showing 1 - 10 of 19
Article Tarafsız 3d Mineral Harita Tahminleri Elde Etmek için Random Forest Tree Sınıflandırması Kullanılarak Epoksi Bloklardaki Dikey Kesitlerin Değerlendirilmesi(2021) Camalan, Mahmut; Çavur, MahmutAlansal mineral haritaları, epoksi reçinenin dibine çöken cevher tanelerinin yüzeylerini içeren parlak kesitlerinden yapılmaktadır.Fakat, ağır mineraller nispeten dibe çökebilmekte ve parlak yüzeyi ağır mineraller açısından zengin yapabilmektedir. Bu ise parlakkesitlerden hesaplanan alansal (2D) mineral haritalarının, hacimsel (3D) haritaların taraflı tahminleri haline gelmesine sebepolabilmektedir. Bu çalışma, parlak kesite dik olarak (parçacıkların çökelme yönü boyunca) alınan rastgele bir kesitin bir kromitcevheri numunesinin 3D mineral haritasının tarafsız bir tahmini olarak kullanılıp kullanılamayacağını test etmeyi amaçlamaktadır.Bu çalışmanın amacı için, dikey kesitlerin 2D haritaları, öncesi ve sonrası görüntü işleme araçlarıyla bütünleşmiş Random Forestsınıflandırmasıyla elde edilmiştir. Daha sonra, 2D haritalar, stereolojik hatalar olmadığı varsayılarak 3D mineral haritalarınadönüştürülmüştür. 3D haritalardan tahmin edilen modal mineraloji ve tane boyu dağılımları, sırasıyla XRD ve kuru elemeanalizlerinden tahmin edilen sonuçlarla karşılaştırılmıştır. Herhangi bir 2D harita gerçek analizlere yakın modal mineraloji ve taneboyu dağılımı veriyorsa, bu 2D harita cevher numunesinin 3D haritasının tarafsız bir tahmini olarak seçilmiştir. Bu çalışmanınsonuçları herhangi bir dikey kesitin, ağır minerallerin öncelikli olarak çöktüğü parlak kesitten farklı olarak gerçek 3D haritanıntarafsız bir tahmini olacağını desteklemektedir.Article Citation - WoS: 4Citation - Scopus: 5Development of a Supervised Classification Method To Construct 2d Mineral Maps on Backscattered Electron Images(Tubitak, 2020) Camalan, Mahmut; Çavur, MahmutThe Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.Master Thesis Rssi-Based Hybrid Algorithm for Real-Time Pedestrian Tracking in Indoor Environments by Using Rfid Technology(Kadir Has Üniversitesi, 2019) Demir, Ebubekir; Çavur, MehmetThe usage and importance of Location Based Services for indoor environments are increasing recently. The knowledge of the exact and real-time location is required by many of these services. Since Global Positioning System (GPS) is not designed for indoor environment, new positioning systems based on new technologies and methods are needed for these type of environments. In this thesis, RFID-based real-time indoor positioning systems and algorithm are developed. Received Signal Strength (RSS) based positioning techniques, are studied in detail. A hybrid algorithm is developed which depends on the mainly fingerprinting. The advantages of each method are emphasized. An original and unique hybrid algorithm is developed in this study in order to overcome available algorithm's' drawbacks. The algorithm and methodology is tested in two different indoor environments. As a result, the accuracy of this original and unique methodology and algorithm is 2,5 m.Conference Object A Robust Microservices Framework for Indoor Tracking System Development(Institute of Electrical and Electronics Engineers Inc., 2024) Hayytbayev, G.; Küçük, K.; Çavur, M.The demand for indoor tracking systems is steadily increasing across various applications. While GPS is effective for outdoor localization, indoor localization presents distinct challenges related to hardware, algorithms, architecture, and infrastructure. Many researchers have focused on developing algorithms or hardware solutions to address these challenges. In response, we designed and implemented a robust, innovative framework utilizing microservices to achieve a scalable, fault-tolerant, flexible, and multi-platform indoor localization system. Our system employs RFID hardware for tracking, with data storage managed by a PostgreSQL database. The architecture incorporates RabbitMQ and the Spring framework, utilizing the Java programming language. The proposed framework was tested using a graphical user interface (GUI) within a metallic underground mine, demonstrating scalability by successfully deploying 7 and 22 RFID readers. The system supports development across various platforms, including web, desktop, and mobile, and is compatible with Mac, Linux, and Windows operating systems. The tracking accuracy was measured at 5.12 meters within a 300-meter metallic mining gallery. Overall, the microservices-based framework proved highly suitable for indoor tracking systems. © 2024 IEEE.Article Citation - WoS: 36Citation - Scopus: 50The Geothermal Artificial Intelligence for Geothermal Exploration(Pergamon-Elsevier Science Ltd, 2022) Moraga, J.; Duzgun, H. S.; Cavur, M.; Soydan, H.Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). We processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal/non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.(c) 2022 Elsevier Ltd. All rights reserved.Master Thesis Gelı̇şmekte Olan Ülkelerde Matematı̇k Başarısını Etkı̇leyen Faktörlerı̇n Araştırılmasında Makı̇ne Öğrenme Teknı̇klerı̇nı̇n Kullanılması: Türkı̇ye, Meksı̇ka, Tayland ve Bulgarı̇stan Örneğı̇(2023) Arpa, Tuba; Çavur, MahmutMatematik tüm eğitim sistemlerinin vazgeçilmez bir parçasıdır. Çünkü matematik, hem günlük yaşamın önemli bir unsuru hem de pek çok meslek ve alan için olmazsa olmaz bir temeli teşkil etmektedir. Bu nedenle, matematik başarısını etkileyen unsurları belirlemenin, ülkelerin gelişimine katkı sağlayacağı söylenebilir. Bu doğrultuda, bu çalışmada PISA 2018 verileri kullanılarak, benzer eğitim sistemi ve ekonomik gelişmişliğe sahip dört ülke olan Türkiye, Bulgaristan, Meksika ve Tayland'ın matematik başarılarını etkileyen faktörleri makine öğrenmesi modelleri ile belirlemek, bu modellerin başarılarını karşılaştırmak amaçlanmıştır. İlgili alanyazında bu amaç için sıklıkla sınıflandırma algoritmaları tercih edildiği görülmektedir. Bu çalışmada hem sınıflandırma hem de regresyon modelleri kullanılmıştır. Çalışmada, regresyon algoritması olarak doğrusal regresyon, destek vektör regresyonu, karar ağacı regresyonu ve rastgele orman regresyonu; sınıflandırma algoritması olarak ise lojistik regresyon, destek vektör sınıflandırması, karar ağacı sınıflandırması ve rastgele orman sınıflandırması kullanılmıştır. Ayrıca, matematik başarısını tahmin etmek için en önemli faktörlerin belirlenmesinde XGradient Boosting algoritması kullanılmıştır. Son olarak, eksik verilerin doldurulmasında, K-Means metodu tercih edilmiştir. Çalışmanın sonuçlarına göre, dört ülke için de matematik başarına en büyük katkı sağlayan değişkenlerin öğrencinin ekonomik, sosyal ve kültürel statüsü, öğrencinin evde sahip olduğu çalışma materyali, öğrencinin sahiplik hissi ve ailenin refah düzeyi olduğu bulunmuştur. Model başarısı açısından hem regresyon hem de sınıflandırma açısından en yüksek başarıya sahip algoritmanın rastgele ormanlar olduğu bulunmuştur. Ayrıca, sınıflandırma algoritmaları ikili ve üçlü sınıflandırma üzerinden incelenmiş, ikili sınıflandırmanın daha yüksek başarıya sahip olduğu görülmüştür. Sonuç olarak, çalışmamızda elde edilen bulgular matematik başarısını tahmin etmede kullanılacak en uygun algoritmanın seçimi v konusunda önemli bir öngörü sunmaktadır. Ayrıca, çalışmanın bulguları, eğitim politikalarının geliştirilmesi ve öğrenci başarısını artırmak için uygulayıcı ve politika yapıcılara önemli iç görüler sağlamaktadır.Article Impacts of the Changes in Agriproduction on Rural Heritage in the Case of Müşküle, Iznik, Turkey(Emerald Group Publishing Ltd, 2025) Ulu, Damla Pilevne; Erkan, Yonca; Alkan Reis, Amine Seyhun; Guvenc, Himmet Murat; Cavur, Mahmut; Reis, Amine Seyhun AlkanPurposeAgriculture is both a constituent and an integral part of rural culture. Therefore, agricultural planning is essential to the conservation of rural heritage. However, this relationship has received limited scholarly attention. Focusing on the rural settlement of M & uuml;sk & uuml;le in Turkey, this research paper reveals the vital role of agricultural planning in sustaining rural architectural heritage.Design/methodology/approachWe examine the settlement's history of agricultural production in relation to national agricultural policies and practices from the early 20th century to the present, analyzing how these shifts have affected the built heritage. The research is a combination of literature review, fieldwork and face-to-face interviews. Aerial images, on-site architectural surveys and interviews were used to identify the features of the built environment. These were followed by in-depth thematic analysis.FindingsWe find that the lack of agricultural planning has led to economic decline among rural households, resulting in the neglect of architectural heritage, abandonment of traditional dwellings and increased rural outmigration. As specialized agricultural products shape the character of rural architecture, changes in production can lead to the removal of heritage-valued building elements, degradation of traditional architectural features and loss of traditional knowledge.Originality/valueThis paper demonstrates the strong link between rural production (i.e. agriproduction) and architectural heritage. It shows how agriproduction shapes rural fabric, plan typologies and building elements and underscores the decisive role of agricultural planning in rural heritage conservation.Article Citation - WoS: 7Citation - Scopus: 8Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms(Mdpi, 2024) Cavur, Mahmut; Yu, Yu-Ting; Demir, Ebubekir; Duzgun, SebnemMineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.Article Citation - WoS: 5Citation - Scopus: 7An Evaluation of AI Models' Performance for Three Geothermal Sites(Mdpi, 2024) Demir, Ebubekir; Cavur, Mahmut; Yu, Yu-Ting; Duzgun, H. SebnemCurrent artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites-Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.Master Thesis Forecasting Employees' Promotion Based on the Personal Indicators by Using a Machine Learning Algorithm(Kadir Has Üniversitesi, 2022) IBRIR, YASMINE AYA; cavur, mahmutJob promotion is considered one of the most important issues of importance in any organization, as it is vital for administrative development, and a means of motivating the worker for self-development and willingness to bear the burden and responsibility of work and the position attached to it, and thus it contributes to providing the necessary needs of the forces of mankind to occupy positions higher on the career ladder. Thus, this study aims to set up a sufficient framework to predict the promotion of an employee in an organization based on a variety of characteristics such as, but not limited to, the number of training, previous year rating, duration of service, awards earned, and average training score. Hence, this framework can be used and generalized to all prediction problems, not just our problem of predicting employee promotion. In this study, we used promotion data provided by Analytics Vidhya Data to test and prove the success of the framework. Our methodology is mainly composed of five phases: Input data, Data Pre processing, Data Manipulation, Data Modeling, and finally Data Evaluation. We constructed a new number of features in this study. Then we used several features including creating features and providing insights into the promotion and commitment of employees and using supervised learning techniques, namely XGBoost, Random Forest, Decision Tree, Logistic Regression, AdaBoost, and Gradient Boosting. Experimental results show that the XGBoost model has a higher accuracy of 94%, proving to be the most efficient. The result is accentuated by the high validation score similar to accuracy and efficiency. It is a very important and valuable study as it is the first study to predict employee promotion using the XGBoost classifier method.
