Ünlütürk, Mehmet Süleyman
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01.01.09.07. Yazılım Mühendisliği Bölümü
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Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
2
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3GOOD HEALTH AND WELL-BEING
1
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
1
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Documents
41
Citations
280
h-index
7
Documents
27
Citations
173
Scholarly Output
16
Articles
12
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Supervised MSc Theses
2
Supervised PhD Theses
2
WoS Citation Count
40
Scopus Citation Count
54
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0
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2
WoS Citations per Publication
2.50
Scopus Citations per Publication
3.38
Open Access Source
3
Supervised Theses
4
| Journal | Count |
|---|---|
| Journal of Testing and Evaluation | 2 |
| Neural Computing and Applications | 2 |
| Studies in Informatics and Control | 2 |
| Engineering Applications of Artificial Intelligence | 1 |
| Advances in Electrical and Computer Engineering | 1 |
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16 results
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Article Citation - WoS: 5Citation - Scopus: 5Fluence (UV dose) distribution assessment of UV-C light at 254 nm on food surfaces using radiochromic film dosimetry integrated with image processing and convolutional neural network (CNN)(Elsevier Ltd, 2023) Yadigar Seyfi Cankal; Mehmet Suleyman Ünlütürk; Sevcan Mehmet Unluturk; Cankal, Yadigar Seyfi; Unluturk, Mehmet S.; Unluturk, SevcanUniform Fluence (UV Dose) distribution on food surfaces is essential for an effective UV process design. In this study the use of radiochromic films (RCFs) with a computer vision system (CVS) integrating image processing and Convolutional Neural Network (CNN) is proposed as an alternative method to assess Fluence distribution of UV-C light at 254 nm on food surfaces. The color difference of RCFs exposed to different UV irradiance and exposure times was correlated with Fluence. The validity of the developed methodology was proved by applying it to the surface of apple fruits of different shapes and sizes. A linear relationship was found between the color difference of RCF and Fluence. The maximum Fluence to be determined using RCFs was ∼60 mJ/cm2. The color of the films after UV irradiation remained stable for up to 15 days in darkness when stored at room and refrigeration temperatures. The results showed that RCF can be used as an alternative UV dosimeter. © 2023 Elsevier B.V. All rights reserved.Doctoral Thesis Çekişmeli üretici ağlar ile yapay zekalı moda tasarımcısı ve değerlendiricisi üretme(2024) Hekimoğlu, Caner Kıvanç; Ünlütürk, Mehmet SüleymanRecently, the fashion industry has been incorporating advanced technologies more and more in order to satisfy the needs of a varied and competitive market. This doctoral thesis investigates the application of Generative Adversarial Networks (GANs) as a fashion design and assessment method. The objective of the study is to create an advanced artificial intelligence system that can produce fashion images of high-quality and realism. This system will enhance the design process and elevate the virtual shopping experience. The study focusses on various approaches of GAN, including CycleGAN, Neural Transfer, and StyleGAN, to improve different aspects of fashion design, such as the transformation of clothes and patterns. The effectiveness of these models is evaluated through detailed computational experiments, demonstrating their ability to revolutionize the creative process in fashion design by providing innovative and efficient solutions. This thesis showcases notable progress in automating the processing of fashion images, providing designers with powerful tools to explore novel ideas and visualize concepts without the necessity of physical prototypes. Integrating GANs not only speeds up the design process but also decreases expenses related to material waste and extended prototyping. By using extensive datasets of past designs and fashion trends, the AI-driven system creates original and cutting-edge fashion ideas, encouraging innovation and helping designers stay ahead of the competition. Furthermore, this study aims to meet the increasing need for sustainability in the fashion sector by reducing material waste through the use of digital sample production. The utilization of AI technology is in line with current environmental objectives, establishing GANs as a significant contributor in promoting sustainable fashion practices. The effective utilization of GANs in this particular situation highlights their ability to not only improve artistic procedures but also contribute to more ecologically conscious fashion design practices.Article Citation - WoS: 2Citation - Scopus: 3Application of Data Mining in Failure Estimation of Cold Forging Machines: An Industrial Research(NATL INST R&D INFORMATICS-ICI, 2019) Buse Turkoglu; Murat Komesli; Mehmet Suleyman Unluturk; Turkoglu, Buse; Unluturk, Mehmet Suleyman; Komesli, MuratThe industrial companies are now reaching out for solutions that would enable them to reduce the number of manufacturing defects in production so that they may be able to compete and maintain their sustainability in the market. All production processes need to be uninterruptible. This study utilizes data mining algorithms to turn the data created by machines into information. These data mining algorithms are effective tools for reducing the cold forging machine downtime. Furthermore the selected data mining methodology the J48 model generates meaningful results for a large real-life data set and predicts the error according to a behavioral model. The J48 model successfully detected 28 failures from this data set which suggests that it can be a promising method for reducing the periods of downtime of the cold machine.Article Citation - WoS: 1Citation - Scopus: 1GPR Raw-Data Analysis to Detect Crack Using Order Statistic Filtering(AMER SOC TESTING MATERIALS, 2016) Gokhan Kilic; Mehmet S. Unluturk; Unluturk, Mehmet S.; Kilic, GokhanGround penetrating radar (GPR) uses data collected with the aid of electromagnetic waves transmitted into a structure by antenna to assess and monitor the structural health of many different kinds of civil infrastructure. With GPR technology promoting their system with promises of the achievement of in excess of 1000 sample points per scan this research demonstrated on the basis of the Nyquist theorem that 256 sample points per scan provided equally reliable inspection results. Furthermore 256 sample points per scan GPR data were further analyzed by order statistic filtering with neural networks to locate cracks within concrete materials. The results showed that the neural network order statistic filters are effective in their use of detecting cracks in noisy environments using 256 sample points per scan GPR data.Article Citation - WoS: 4Citation - Scopus: 4Classification of organic and conventional olives using convolutional neural networks(Springer Science and Business Media Deutschland GmbH, 2021) Mehmet Suleyman Ünlütürk; Seçil Küçükyaşar; Fikret Pazir; Unluturk, Mehmet S.; Pazir, Fikret; Kucukyasar, SecilThis paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method 20 30 and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25 0.5 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences the vivid color occurrence the width and the frequency of bowl occurrence the thin line and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors. © 2021 Elsevier B.V. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 8QUANTIFYING PRODUCTIVITY OF INDIVIDUAL SOFTWARE PROGRAMMERS: PRACTICAL APPROACH(SLOVAK ACAD SCIENCES INST INFORMATICS, 2015) Mehmet Suleyman Unluturk; Kaan Kurtel; Unluturk, Mehmet Suleyman; Kurtel, KaanSoftware measurement is a crucial part of a good software engineering. Software developers quantify the software to see if the use cases are complete if the analysis model is consistent with requirements and if the code is ready to be tested. Software project managers assess the software process and the software product to determine if it is going to be finished on time and within budget. Customers evaluate the final product if it meets their needs. Overall the main purpose of software engineering is to make software systems controllable and foreseeable activities with a solid method rather than intuitional complicated or unprincipled. Software measurement studies are about quantifying the software engineering entities and attributes both of which aim to support software development efforts and quality improvement. In this paper we quantify a set of relationships using the current size defect and object-oriented software metrics practically and pragmatically. Our paper proposes a method to measure the productivity of individual software programmers. Furthermore this method provides a common opinion for understanding controlling and improving the software engineering practices.Article Citation - WoS: 2Citation - Scopus: 3Convolutional Neural Network for Cotton Yield Estimation(National Institute for R and D in Informatics, 2024) Mehmet Suleyman Ünlütürk; Murat Komesli; Asli Keceli; Unluturk, Mehmet Suleyman; Komesli, Murat; Keceli, AsliThe objective of this paper was to estimate the cotton yield potential of different cotton varieties using high-resolution field images based on a convolutional neural network (CNN). The yield estimation for different cotton varieties in grams in breeding studies has a great importance for the determination of superior cultivars to be commercialized. Due to the cost and excessive time consumption typical of traditional methods alternative ways for cotton yield estimation have been investigated over the years. This paper proposes an automated system for cotton yield prediction based on color images obtained by an unmanned aerial vehicle (UAV). Two replicational field experiments including three different cotton genotypes were conducted at May Seed R&D station in Torbali Izmir Turkey. Three different planting patterns including three four and six rows respectively in ten-meter wide areas were used as experimental plots. The ground-truth yield values for a total of six hundred planted areas were obtained by weighing the harvested cotton bolls after field images were taken. Achieving an absolute difference of no more than 350 grams for 114 out of 120 planted areas which were randomly selected only for testing purposes indicates that the CNN can effectively capture important features related to cotton yield from the field images obtained by the UAV. The combination of drone technology with reliable CNN models holds great potential for optimizing agricultural practices improving agricultural productivity and reducing operational costs. © 2024 Elsevier B.V. All rights reserved.Article Citation - WoS: 5Citation - Scopus: 6Neural network-supported patient-adaptive fall prevention system(Springer, 2020) Mehmet Hilal Özcanhan; Semih Utku; Mehmet Suleyman Ünlütürk; Unluturk, Mehmet Suleyman; Utku, Semih; Özcanhan, Mehmet HilalPatient falls due to unattended bed-exits are costly to patients healthcare personnel and hospitals. Numerous researches based on up to three predetermined factors have been conducted for preventing falls. The present comprehensive proposal is based on four sub-systems that synthesize six factors. A parameter is assigned to each factor with a coefficient specifically determined for each individual patient and per admittance. The parameters are aggregated in equations that lead to an early warning about a probable bed-exit or an alarm about an imminent bed-exit. The ultimate aim of our proposal is the generation of the earliest possible warning to grant the longest time for nurse intervention. Thus the probable fall of high-risk patients can be prevented by stopping the unattended bed-exits. The proposal is supported by a prototype multi-tier system design and the results of laboratory patient bed-exit scenarios carried out using the design. Comparison of the obtained results with previous work shows that our proposed solution is unmatched in providing the longest time for nurse intervention (up to 15.7 ± 1.1 s) because of the comprehensive six-factor synthesis specific to each individual patient and each admittance. © 2020 Elsevier B.V. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 2An Ultra-light PRNG Passing Strict Randomness Tests and Suitable for Low Cost Tags(UNIV SUCEAVA FAC ELECTRICAL ENG, 2016) Mehmet Hilal Ozcanhan; Mehmet Suleyman Unluturk; Gokhan Dalkilic; Unluturk, Mehmet Suleyman; Dalkilic, Gokhan; Ozcanhan, Mehmet HilalA pseudo-random number generator for low-cost RFID tags is presented. The scheme is simple sequential and secure yet has a high performance. Despite its lowest hardware complexity our proposal represents a better alternative than previous proposals for low-cost tags. The scheme is based on the well-founded pseudo random number generator Mersenne Twister. The proposed generator takes low-entropy seeds extracted from a physical characteristic of the tag and produces outputs that pass popular randomness tests. Contrarily previous proposal tests are based on random number inputs from a popular online source which are simply unavailable to tags. The high performance and satisfactory randomness of present work are supported by extensive test results and compared with similar previous works. Comparison using proven estimation formulae indicates that our proposal has the best hardware complexity power consumption and the least cost.Master Thesis Yazılım algoritmalarının verimlilik tekniklerinin titiz analizi(2020) Ayaydın, Atabarış; Ünlütürk, Mehmet SüleymanEfficiency, in programming, generally treated as a concept of 'on-demand' rather than an integral part of the programming. However, as it is a part of the software quality measurements, the programmer also responsible to write a program that will meet the requirements. Since there is no known technique to find the least time or space complexity for the problem on the hand, augmenting the programmer's knowledge with the known techniques is essential. As the meaning of efficiency changes throughout the time, these mentioned techniques must be reevaluated to adapt to current necessities. This thesis address, the categorization of the mentioned techniques as well as the expansion of them. The runtime comparison between the different versions of the solutions states that efficiency is not a lesser subject to deal with, instead, it requires more attention than it gets.
