Kemal Tutuncu | Selcuk University (Selçuk Üniversitesi) (original) (raw)

Papers by Kemal Tutuncu

nternational Staff Week--AI applications agriculture, health, financial technologies and international trade and logistics, 2024

We’re at beginning of a golden age of AI. Recent advancements have already led to invention that ... more We’re at beginning of a golden age of AI. Recent advancements have already led to invention that previously lived in the realm of science fiction – and we have only scratched the surface of what’s possible. Today, artificial intelligence techniques are used in almost all areas of our lives, from agriculture to financial technologies. It is not possible to cover all artificial intelligence applications that can learn and reason with high accuracy using available examples in this workshop. In the workshop, we wanted to discuss successful applications of artificial intelligence in 4 different fields. These areas; they were determined as agriculture, health, financial technologies and international trade and logistics. For more information please visit http://staffmobility.eu/staffweek/international-staff-week-building-blocks

The young people are not only in the most efficient position at the countries but also the assura... more The young people are not only in the most efficient position at the countries but also the assurance of the future of the countries. It is very important to grow up young people whose personalities are balanced in today's world where everything is changing quickly. Young people are facing with a lot of problems due to be in puberty stage or development properties. They can also have tendency to the extremism at this stage. In this research, it's been aimed to investigate the extremism behaviors of young people in Turkey and member countries of European Union (EU). The sample of the research consists of the young people from Turkey and 9 EU member countries. According to the results of this study; the young people think that people from different nations are as good as people in their own country, they check the source and the credibility of the videos they watch on the Internet and social networks and, they break their relationships with their friends who are in a violent te...

Peerj Computer Science , 2023

Background. Analysis of the nutritional values and chemical composition of grain products plays a... more Background. Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area. Methods. This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirtytwo latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models. Results. R 2 , RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.

Selcuk Journal of Agriculture and Food Sciences, 2022

The fact that arable land is not increasing in proportion to the ever-increasing population will ... more The fact that arable land is not increasing in proportion to the ever-increasing population will increase the need for food in the coming years. For this reason, it is necessary to increase the yield of crops to make optimum use of arable land. One of the most important reasons for the decrease in yield and quality of crops is weeds. Herbicides are generally preferred for weed management. Due to deficiencies in herbicide application methods, only 0.015-6% of herbicides reach their target. The use of herbicides, which is an important part of the agricultural system, is an issue that needs to be emphasized, considering the risk of residue and environmental damage. In parallel with the rapid development of electronic and computer technologies, artificial intelligence applications have had the opportunity to develop. In this context, the use of artificial intelligence for plant detection in the subsystems of herbicide application machines will contribute to the development of precision agriculture techniques. In this study, the plant detection performances of single-stage and two-stage Convolutional Neural Network (CNN)-based deep learning (DL) models are evaluated. In this context, a dataset was created by taking images of Zea mays, Rhaponticum repens (L.) Hidalgo, and Chenopodium album L. plants in agricultural lands in Konya. With this dataset, the training of the models was carried out by the transfer learning method. The evaluation metrics of the trained models were calculated using the error matrix. In addition, training time and prediction time were used as quantitative metrics in the evaluation of the models. The plant detection performance, training time, and prediction time of the models were 85%, 8 h, 1.21 s for SSD MobileNet v2 and 99%, 22 h, 2.32 s for Faster R-CNN Inception v2, respectively. According to these results, Faster R-CNN Inception v2 is outperform in terms of accuracy. However, in cases where training time and prediction time are important, the SSD MobileNet v2 model can be trained with more data to increase its accuracy.

Computer Science and Information Systems , 2021

This study proposes a new hybrid n-LSB (Least Significant Bit) substitution-based image steganogr... more This study proposes a new hybrid n-LSB (Least Significant Bit) substitution-based image steganography method in the spatial plane. The previously proposed n-LSB substitution method by authors of this paper is combined with the Rivest-Shamir-Adleman (RSA), RC5, and Data Encryption Standard (DES) encryption algorithms to improve the security of the steganography, which is one of the requirements of steganography, and the Lempel-Ziv-Welch (LZW), Arithmetic and Deflate lossless compression algorithms to increase the secret message capacity. Also, embedding was done randomly using a logistic map-based chaos generator to increase the security more. The classical n-LSB substitution method and the proposed hybrid approaches based on the previously proposed n-LSB were implemented using different secret messages and cover images. When the results were examined, it has been seen that the proposed hybrid n-LSB approach showed improvement in all three criteria of steganography. The proposed hybrid approach that consists of previously proposed n-LSB, RSA, Deflate, and the logistic map had the best results regarding capacity, security, and imperceptibility.

Selcuk Journal of Agriculture and Food Sciences, 2022

Sugarcane is a plant whose quality parameters are required to be determined both for being one of... more Sugarcane is a plant whose quality parameters are required to be determined both for being one of the substances used in sugar production and for being used as animal feed. Near-infrared spectroscopy is a technique that has already been used for predicting the parameters of various plants and has gained popularity in recent years. This study proposes a near-infrared spectroscopy-based model for the rapid and effortless analysis of acid detergent fiber fraction and vitro organic matter digestibility parameters of the sugarcane plant. Partial least squares regression was combined with common preprocessing methods for modeling. This model yielded an R 2 CV value of 0.935 and 0.953 for the acid detergent fiber fraction and vitro organic matter digestibility parameters, respectively. Then, the spectra from three handheld spectrometers were combined using a proposed combination method to generate new spectra with higher spectral resolution. New models were built using these generated spectra and compared to the previous result. Obtained results showed that combining spectra from different spectrometers can improve model performance.

It's our great honor to announce that International Congress of Climate Change Effects on Health,... more It's our great honor to announce that International Congress of Climate Change Effects on Health, Life, Engineering and Social Sciences (ICLIC 2022) with the themes 'Collaboration in Health, Natural, Engineering and Social Sciences for Climate Changes', 'Zero Waste Supported Climate Change' and 'Carbon management' will take place during September 27-30, 2022 in Konya, Turkey. You may find all the details about the congress on the web address: www.icliccongress.org.

Advances in Electrical and Computer Engineering, 2018

Image steganography is a technique to hide secret information in an image without leaving any app... more Image steganography is a technique to hide secret information in an image without leaving any apparent evidence of image alteration. Hiding capacity, perceptual transparency, robustnes ...

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015, 2015

The definition of the data mining can be told as to extract information or knowledge from large v... more The definition of the data mining can be told as to extract information or knowledge from large volumes of data. One of the main challenging area of data mining is classification. There are so many different classification algorithm in literature ranging from statistical based to artificial intelligence based. This study make use of Waikato Environment for Knowledge Analysis or in short, WEKA to compare the different classification techniques on different medical datasets. 23 different classification techniques were applied to three different medical datasets namely EEG Eye State, Fertility and Thoracic Surgery Medical Datasets that were taken from UCI Machine Learning Repository. The results showed that Multilayer Perceptron (MLP) had highest accuracy for Fertility Dataset (90%), three different techniques namely Bagging, Dagging and Grading had highest and same accuracies for Thoracic Surgery Data Set (85.1064%) and finally Kstar had highest accuracy for EEG Eye State Dataset (96.7757%).

Özet-Teknoloji insan gücüne olan ihtiyacı azaltmasından ve zamandan tasarruf sağlamasından dolayı... more Özet-Teknoloji insan gücüne olan ihtiyacı azaltmasından ve zamandan tasarruf sağlamasından dolayı günümüzde çok tercih edilir hale gelmiştir. Artık teknolojik cihazlara hayatımızın her köşesinde rastlamak mümkündür. Günümüzde bilgi teknolojileri tıp ve sağlık bakımında gittikçe yaygınlaşmakta, sağlık bakımı giderek teknolojiye bağımlı hale gelmektedir. Tıpta kullanılan bu teknolojilerden biri de kapilleroskopi kullanımıdır. Kapilleroskopi, gelişmiş bir mikroskop yardımıyla tırnak yatağında yer alan kapiller adı verilen küçük damarların görüntülenmesi işlemidir. Tırnak yatağındaki küçük damarlarda görülen bazı değişiklikler başta skleroderma olmak üzere bazı romatizmal hastalıkların erken dönemde tanınmasına yardımcı olabilir. Günümüzde romatolojik hastalıkların mikrovasküler tutulumlarını belirlemek amacıyla sıkça kullanılmaktadır. Tırnak kıvrımı kapiller sistemini değerlendirmede hızlı ve etkili bir tanısal araç olan dermatoskopi cihazlarının ucuz, kolay uygulanabiliyor olması ve z...

Having done in this study, air-conditioning automation for patisserie shopwindow was de... more Having done in this study, air-conditioning automation for patisserie shopwindow was designed. In the cooling sector it is quite important to cooling up the air temperature in the shopwindow within short time interval. Otherwise the patisseries inside of the shopwindow will be spoilt in a few days. Additionally the humidity is other important parameter for the patisseries kept in shopwindow. It must be raised up to desired level in a quite short time. Traditional patisserie shopwindows only allow controlling temperature manually. There is no humidity control and humidity is supplied by fans that are directed to the water at the bottom of the shopwindows. In this study, humidity and temperature sensors (SHT11), PIC, AC motor controller, DC motor controller, ultrasonic nebulizer and other electronic circuit members were used to simulate air conditioning aut...

modeling of a diesel engine performance by FCM and ANFIS

PeerJ Computer Science, 2022

Background In terms of data-hiding areas, video steganography is more advantageous compared to ot... more Background In terms of data-hiding areas, video steganography is more advantageous compared to other steganography techniques since it uses video as its cover medium. For any video steganography, the good trade-off among robustness, imperceptibility, and payload must be created and maintained. Even though it has the advantage of capacity, video steganography has the robustness problem especially regarding spatial domain is used to implement it. Transformation operations and statistical attacks can harm secret data. Thus, the ideal video steganography technique must provide high imperceptibility, high payload, and resistance towards visual, statistical and transformation-based steganalysis attacks. Methods One of the most common spatial methods for hiding data within the cover medium is the Least Significant Bit (LSB) method. In this study, an LSB-based video steganography application that uses a poly-pattern key block matrix (KBM) as the key was proposed. The key is a 64 × 64 pixel ...

International Journal of Intelligent Systems and Applications in Engineering, 2021

European Journal of Science and Technology, 2021

Although the analysis of the structure of objects and the components that makeup them has been do... more Although the analysis of the structure of objects and the components that makeup them has been done for decades, it is one of today's research topics to do this analysis quickly and without damaging the sample. Near-infrared spectroscopy is used in many areas due to its non-contact measurement, fast analysis, and high accuracy features. Near-infrared spectroscopy is used in the classification or quality analysis of products, especially in the agriculture and food sector, due to the chemical bonds interacting in this region. The most critical part of achieving a successful result in near-infrared spectroscopy is pre-processing and analyzing the spectral data using the correct method. In this review, we perform a survey of recent studies that use near-infrared spectroscopy in food production and agriculture. Since there are many studies in this field in the literature, the survey is limited to cover works in the last five years. The review's main question is the pre-processing...

Previous studies showed that knowing occupancy certainly can save energy in the control system of... more Previous studies showed that knowing occupancy certainly can save energy in the control system of building. In this regard, occupancy detection has a significant role in many smart building applications such as heating, cooling, ventilation (HVAC) and lighting system. In this paper, various Artificial Neural Network algorithms were applied to the dataset composed by samples obtained from light, temperature, humidity and CO2 sensors. When the results were compared, it was seen that Limited Memory Quasi-Newton algorithm has the highest accuracy rate with 99.061%. The lowest accuracy rate was obtained from Batch Back algorithm with 80.324%. KeywordsOccupancy Detection, Classification, Artificial Neural Network

Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environment... more Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio.

2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017

The incredible progress of technology has made the use of communication and information technolog... more The incredible progress of technology has made the use of communication and information technologies indispensable because of the possibilities it offers. These possibilities increased the security issues on personal information and communication security problems such as phone calls, retrieving e-mail contents, copying private information on computers. Encryption algorithms used in classical security approaches, while ensuring the confidentiality of information, cannot provide the principle of “imprecision” that has become increasingly important in recent times. A coded or encrypted text can be solved by advanced machines when focused on it. So the key point is “do not raise suspicion”. For this reason, steganography and watermarking methods that put the invisibility of the existence of a secret message into the primary goal are especially the focus of interest after 2000's years. In this study, Least Significant Bit (LSB) technique, which is the most basic and commonly used te...

Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict q... more Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict qualitative bankruptcy. The objective of this research is predicting qualitative bankruptcy using 4 different Artificial Intelligence (AI) techniques Qualitative Bankruptcy namely; Naive Bayes Classifier (NBC), Multilayer Perceptron (MLP), J48 and Classification via Regression (CR). Correctly Classified Instances were found as 96.5714 %, 94.8571 %, 95.4286 % and 96% for NBC, MLP, J48 and CR, respectively. These results have shown that NBC has the most successful prediction ratio among the four techniques regarding to classification. By using NBCs we can generate better rules with more qualitative factors and redundancy and overlapping of the rules can also be avoided.

nternational Staff Week--AI applications agriculture, health, financial technologies and international trade and logistics, 2024

Peerj Computer Science , 2023

Selcuk Journal of Agriculture and Food Sciences, 2022

Computer Science and Information Systems , 2021

Selcuk Journal of Agriculture and Food Sciences, 2022

Advances in Electrical and Computer Engineering, 2018

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015, 2015

modeling of a diesel engine performance by FCM and ANFIS

PeerJ Computer Science, 2022

International Journal of Intelligent Systems and Applications in Engineering, 2021

European Journal of Science and Technology, 2021

2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017

PRroceedings of International Conference on Science, Technology, and Interdiscipclinary Research (ICSTAR-24) , 2025

Agriculture is fundamental to sustaining the global population, with okra (Abelmoschus esculentus... more Agriculture is fundamental to sustaining the global population, with okra (Abelmoschus esculentus) playing a key role in global food production, particularly in tropical and subtropical regions such as India. This nutrient rich vegetable is highly valued for its health benefits, and its maturity is crucial in determining its quality, with tender, immature pods being the most desirable for consumption.
Traditionally, okra maturity has been assessed manually, a process that is both time-consuming and prone to human error. However, advancements in machine learning have provided opportunities for automating this classification process. In this study, we investigated the use of machine learning algorithms as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Logistic Regression (LR), combined with various feature extraction methods (DeepLoc, SqueezeNet, and VGG19) to classify okra maturity using thermal images. Logistic Regression consistently achieved the highest.
classification accuracy, reaching 97.00% with SqueezeNet features and 95.40% with VGG19
features. SVM also demonstrated strong performance across different feature sets, while kNN
showed relatively lower results. The findings highlight that machine learning models, when paired with
thermal imaging, offer a promising approach for automating okra maturity classification, improving
accuracy and efficiency, and addressing the growing quality control demands within the agricultural
sector.

The 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 7-9 September 2023, Dortmund, Germany, 2023

Industrial production and packaging face significant challenges, such as product damage, color ch... more Industrial production and packaging face significant challenges, such as product damage, color changes, and the presence of foreign bodies. These issues greatly impact product quality, profitability, and marketability, leading to increased consumer complaints. To address these concerns, this study presents a novel method for classifying Taralli biscuits using image processing techniques. The research encompasses a dataset of 4,900 images, featuring four types of defects: no defect, defectshape, defect-object, and defect-color. Leveraging advanced deep learning architectures, including MobileNet-v2 and DenseNet-201, the classification process achieves impressive accuracy rates of 98.71% and 99.39% respectively. By automating the detection of biscuit damage, the proposed method enhances quality control and inspection processes within the food industry. The combination of state-of-the-art image processing and deep learning techniques in this research provides an effective solution for automatically detecting and categorizing biscuit defects.

1st International Conference on RecentAcademic StudiesMay 2-4, 2023 : Konya, Turkey, 2023

Elektronik ve bilgisayar teknolojilerinin gelişimine paralel olarak yapay zekâ uygulamaları birço... more Elektronik ve bilgisayar teknolojilerinin gelişimine paralel olarak yapay zekâ uygulamaları birçok alanda gelişme imkânı bulmuştur. Bu alanlardan birisi de hassas tarımda yapay zekanın kullanımıdır. Yapay zekanın alt dalı olan derin öğrenme teknikleriyle güçlü donanıma sahip bilgisayarlar kullanılarak hassas tarım için birçok başarılı bilgisayarlı görü uygulamaları geliştirilmiştir. Ancak bu uygulamaların gerçek zamanlı çalışabilen bir robotik sisteme entegre edilmesi yüksek maliyet gerektirmektedir. Bu sebeple hassas tarıma yönelik gerçek zamanlı uygulamalar tasarlayabilmek ve robotik makinelerin alt sistemlerinde yapay zekayı kullanabilmek için düşük maliyetli çözümlere ihtiyaç duyulmaktadır. Bu çalışmada hassas tarımda robotik makinelerin bilgisayarlı görü sistemlerinde kullanmak için Raspberry Pi 4 ile gerçek zamanlı bir bitki algılama sistemi gerçekleştirilmiştir. Ayrıca sistemin algılama hızını artırmak için Coral USB hızlandırıcı kullanılarak sonuçlar algılama hızı bakımından değerlendirilmiştir. Coral USB hızlandırıcı ile Raspberry Pi 4’ün birlikte kullanımıyla 30 FPS’lere varan algılama hızı elde edilmiştir. Bu sonuçlar bir mikrobilgisayar üzerinde gerçek zamanlı bitki algılamanın yapılabileceğinin mümkün olduğunu göstermektedir.

International Conference on New Approaches (ICNAE’22) Proceedings, 2022

Steganography refers to techniques that hide information into the cover object. In this study, a ... more Steganography refers to techniques that hide information into the cover object. In this study, a new technique for the F5 method was performed with the Discrete Cosine Transform (DCT) method, Key Block Matrix (KBM) consisting of different sub-pattern blocks, and additional security algorithms. This paper evaluates the applications of different algorithms and their results which can be chosen by users for video steganography using F5 methods. To evaluate the results Mean Square Error (MSE), Structure Similarity Index Measurement (SSIM), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM) quality metrics were used. The fact that the KBM structures are variable increases data security in the proposed algorithms and fills the gap of security issues in the video steganography. The quality metric values obtained showed that the proposed approach can compete with the results obtained in the literature regarding PSNR, MSE, and SSIM values.

2022 11th MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING, 2022

Of the millions of mushroom species growing all around the world, one type is edible, while the o... more Of the millions of mushroom species growing all around the world, one type is edible, while the other is poisonous. It is not easy to distinguish edible and poisonous mushrooms from each other and it is a condition that requires expertise. The classification of poisonous and edible mushrooms is therefore important. Machine learning algorithms are an alternative method for classifying poisonous and edible mushrooms using morphological or physical features of fungi. The dataset used in this study is the Mushroom dataset available in the UC Irvine Machine Learning Repository. Based on 22 features in the Mushroom dataset and four different machine learning algorithms, models have been created for the classification of edible and poisonous fungi. The classification success rates of these models were obtained from Naive Bayes, Decision Tree, Support Vector Machine and AdaBoost algorithms with 90.99%, 98.82%, 99.98% and 100%, respectively. When these results were examined, taking into account the physical appearance features of the mushrooms, it was determined whether the mushrooms were edible and poisonous by 100% with the AdaBoost model.

I am pleased to announce that the International Congress on Engineering and Agricultural Sciences... more I am pleased to announce that the International Congress on Engineering and Agricultural Sciences (ENAG 2022, www.enagcongress.org) will be held from September 26 to 29, 2022 in Konya, Turkey.
Selected papers will be published in;
- Turkish Journal of Entomology (Science Citation Index Expanded)
- Selcuk Journal of Agriculture and Food Sciences (TR Dizin Indexed)
- Turkish Journal of Agriculture - Food Science and Technology (TR Dizin Indexed)
- International Journal of Engineering and Geosciences (ESCI and TR Dizin Indexed)
- Turkish Journal of Engineering (TR Dizin Indexed)
- Science International (Scopus)
- International Journal of Phytopathology (Scopus)
- Eurasian Journal of Family Medicine (TR Dizin Indexed)
* Negotiations and deals with the journals indexed in SCI, SCI-E or TR index still continue.
For more information please visit http://enagcongress.org/index.html

4 th International Advanced Technologies Symposium September 28-30, 2005 Konya / Türkiye , 2005

4 th International Advanced Technologies Symposium September 28-30, 2005 Konya / Türkiye, 2005

4 th International Advanced Technologies Symposium September 28-30, 2005 Konya / Türkiye , 2005

URSI-TÜRKİYE 2021 X. Bilimsel Kongresi, 2021

Bu çalışmada, sürekli dalga doppler radarı verilerinden kalp atışı tespitine yönelik sığ yapay si... more Bu çalışmada, sürekli dalga doppler radarı verilerinden kalp atışı tespitine yönelik sığ yapay sinir ağı modeli oluşturulmuştur. 24 GHz sürekli dalga radarından alınan I (in-phase) ve Q (quadrature) sinyalleri dengesizlik düzeltme ve daha düşük frekansta yeniden örnekleme işleminin ardından 250ms uzunluğunda örneklere bölünerek ağ modelinin girdi verileri oluşturulmuştur. Çıkış katmanı tek nörona sahip sinir ağı verilen girdinin kalp atış aralığında olma olasılığını çıktı olarak vermektedir. Ardışık girdilere karşılık gelen bu çıktılarla oluşturulan grafikte tepe detektörü kullanılmış ve elde edilen tepe noktaları kalp atışı olarak işaretlenmiştir. Sonuçlar değerlendirildiğinde modelin ölçüm alınan birey değişse bile kararlı ve başarılı sonuçlar verdiği görülmüştür.

2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017

International Conference on Engineering Technologies (ICENTE'17) , 2017

Because of the technological advances in communication between people, information security is so... more Because of the technological advances in communication between people, information security is so important that it is not at any stage in history. Steganography is a science that is used in the field of information security and aims to conceal the existence of a confidential message. In this study, the effect of text compression algorithms on image steganography is examined. Three different text files on different sizes are compressed using 4 different compression algorithms namely LZW, MTF, Arithmetic and Deflate and embedded with LSB substitution method on 3 different images. When the results are examined, it is seen that the Deflate algorithm has a higher compression ratio than the other algorithms. In this way, a significant increase in image capacities has been achieved and the distortion in the image has been reduced.

International Conference on Engineering Technologies (ICENTE'19), 2019

Today, it is possible to find many types of compressors according to the manufacturing capacity a... more Today, it is possible to find many types of compressors according to the manufacturing capacity and size. However, the diversity for mini-size compressors is drastically reduced. It causes serious problems for small systems where compressed air is needed. In this study, design and manufacturing of a mini-size rotary swing type compressor were carried out in order to contribute to the solution of the related problem. In the phase of design, the value of pressure to be created by the compressor and the energy to be consumed by it were calculated. After the manufacturing was carried out by using industrial counters, various pressure experiments were performed on the compressor. As a result of the experiments carried out by using brushless DC motor and semi-hermetic tank at 4000 rpm, the compressor operating with 23,7% efficiency created 6 bar pressure by consuming 120 W power. The compressor manufactured for small systems will be able to be used easily in various sectors that can work up to 6 bar pressure. By changing the piston size without changing the compressor size, high pressures such as 12-13 bar will be obtained and it will be possible to be used in many sectors including cooling systems.

International Conference on Engineering Technologies (ICENTE'19), 2019

In this study, the closed loop speed control of the separately excited Direct Current (DC) motor ... more In this study, the closed loop speed control of the separately excited Direct Current (DC) motor controlled by a four-quadrant DC motor drive circuit was performed in MATLAB / SIMULINK software. For this purpose, two control circuits were designed using PI controller. The first one is the bipolar switching circuit and the second one is the unipolar switching circuit. The control signals obtained from these circuits were applied to the single-phase four-quadrant DC chopper power circuit to drive the separately excited DC motor at reference speed. Comparison of the both switching methods has been implemented based on the data obtained from the simulation results of fourquadrant operation of the DC motor. As a result of the comparison, it has been seen that output voltage and frequency responses were better than the bipolar switching method (BSM) due to doubling of switching frequency of output voltage of the unipolar switching method (USM).

ICENTE'19, 2019

Capillaroscopy device shoot videos of capillaries of oral mucosa and nailfold of patient over the... more Capillaroscopy device shoot videos of capillaries of oral mucosa and nailfold of patient over the related skin without any pain. The image frames of videos are used by experts for early detection or treatment of some diseases such as diabetics, rheumatism and etc. Since this process is implemented in manually, decision support systems that helps the experts for diagnosis have been subjects of studies of biomedical researches. First step of these systems is the successful segmentation process on these images that will be used for classification of disease depending on 8 parameters such as the number of capillaries in a certain area, the distance between the two vessels, the size of the capillaries and etc. This study aims to contribute decision support system for experts by presenting a successful segmentation. In this study Otsu, Fuzzy C-mean, Fast Marching, Region Growing and H-Minima methods have been used for segmentation of capillarocopic images. The segmentation accuracy ratios of upper mentioned methods were obtainedas %80,47, %67,44, %63,23, %44,11 and 96.76%, respectively. When the results were examined, it was observed that the H-Minima method, which had not previously been applied in capillary images, reached the highest accuracy parameter value.

International Conference on challenges in IT, Engineering and Technology (ICCIET’2014), 2014

IOP Conference Series: Materials Science and Engineering, 2019

Latest smart buildings are not only be intelligent to allow occupant to control the light, heatin... more Latest smart buildings are not only be intelligent to allow occupant to control the light, heating, cooling, gas and other systems but also focuses on occupancy detection since accurate occupancy detection can result in saving energy up to 42% as can be seen in literature. For this aim, different autonomous systems including sensors, actuators, microcontroller and etc. are at the development phase for smart buildings. At this point, determination of classification methods to detect the occupancy together with hardware plays crucial role. Having done in this study 3 different classification methods that is based on Machine Learning Methods were applied on benchmark dataset named Occupancy by UCI Machine Learning Repository, 2016. The classifiers are Random Forest, Decision Tree and Bagging. They were chosen by following two principals. First one is to have classifier methods that were not use in literature for benchmark dataset and the second one almost never usage of tree based classifiers in the literature. The Occupancy dataset consists of light, temperature, humidity, CO2 and occupancy. It has been seen that the highest accuracy or prediction ratio was obtained as 99,368% by Decision Tree method namely; Random Forest. This result was compared with the results of the studies on the same benchmark dataset. It has been seen that it is the second best accuracy ratio after Fuzzy Granulation (Fgf) method among 16 different Machine Learning Based classification methods. Additionally, Decision Tree and Bagging had the accuracy ratio of 99.222% and 99.207, respectively. These ratios are also higher than other methods used in literature but Fgf. Thus this study showed how decision tree can be promising for occupancy detection.

COMPSYSTECH'10, 2010

In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimi... more In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multiobjective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters.

Proceedings of the 2007 international conference on Computer systems and technologies - CompSysTech '07, 2007

The paper includes reverse modeling of a diesel engine performance and emission characteristics. ... more The paper includes reverse modeling of a diesel engine performance and emission characteristics. Modeling is done by fuzzy clustering method (FCM) and Adaptive Neural Fuzzy Inference System (ANFIS). Firstly, outputs and inputs parameters of a diesel engine were replaced as part of system. Later, these parameters were grouped into optimal numbers independently by using FCM and K-means clustering algorithm. Later on, these optimal numbers of clustered parameters were used as inputs and outputs of ANFIS to model engine performance and emissions characteristic. Input of the systems were power, torque, specific fuel consumption (sfc), nox, co2 and hc whereas outputs were air flow ratio, fuel rate, pboost, load and cycle. It has been seen that the best results obtained from ANFIS system by using FCM. What the proposed system makes different from pioneers are to be first study of reverse modeling and finding results as intervals instead of points. One more thing is that the load factor has never been implemented in previous studies but included in this study. Last but not least, the proposed system finds outputs in correct optimal interval as 100% ratio by FCM clustering and ANFIS.

Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '09, 2009

In this study, performance and emission characteristics of an internal combustion (IC) diesel eng... more In this study, performance and emission characteristics of an internal combustion (IC) diesel engine and petrol-driven engine were modeled by Artificial Neural Network (ANN). Diesel engine input parameters are air flow rate (Aflr), boost pressure (Pb), fuel rate (Frt), cycle (Cy) and load (L) whereas input parameters of the petrol-driven engine are advance (A) and cycle (Cy). Engine torque (Tq), power (P), specific fuel consumption (Sfc), emission values such as HC, CO2 and NOx of diesel engine and engine torque (Tq), power (P), specific fuel consumption (Sfc) and HC of petrol-driven have been investigated. R square values of Tq, P, Sfc, HC, CO2 and NOx of diesel engine were %99.9, %99.45, %99.32, %99.84, %99.71 and %99.26 respectively when ANN was used for modeling. R square values of Tq, P, Sfc and Hc of petrol-driven engine %97.24, %99.56, %98.19 and %97.19 respectively. The back-propagation learning algorithm with Hyperbolic tangent activation functions (for hidden layer neurons and output neuron) and 5:12:1 combination have been used in the topology of the network of diesel engine. The back-propagation learning algorithm with Logistic-Hyperbolic tangent activation functions (hidden layer neurons and output neuron) and 2:6:1 combination have been used in the topology of the network of petrol-driven engine. After having statistical t-test for outputs of both ANN, it has been seen that the obtained results are approximately %99.5 and %98.5 consisted (matched) with experimental data of diesel and petrol-driven engine. Main contribution of this work includes; 1) Dynamic load value was used as input parameters for diesel engine and so engine performance modeling and emission characteristic determination were done by regarding changing load, 2) The highest prediction values of output parameters are reached for both engine type regarding to the previous studies and 3) None of the previous studies include modeling of diesel and petrol-driven engine.