Kemal Tutuncu

Selcuk University (Selçuk Üniversitesi), Electrical and Electronics Engineering, Faculty Member

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He has been working in Electrical and Electronics Department in Technology Faculty since 2000. He got two master degrees; one in Free University of Brussels (Master of Applied of Computer Sciences) and one in Selcuk University (Master in Electronics and Computer Education ). He got his PhD degree in Electronics Engineering. He has been involved in National and international R&D and social development projects since 2005. He was the coordinator of two ETN projects and two Turkish National Agency-supported projects in favor of Selcuk University. His main research areas are Cryptography, Image Steganography, Fuzzy Logic, Fuzzy Logic Control, Machine Learning and Image Processing.
Phone: +903322233335
Address: Selcuk University Technology Faculty Electrical Electronics Engineering Department Campus 42031 Selcuklu KONYA

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Papers by Kemal Tutuncu

Investigation of Extremism Behaviors of Young People in Turkey and Member Countries of European Union

by Suleyman Sulak and Kemal Tutuncu

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...

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy

by Kemal Tutuncu and Ozcan Cataltas

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.

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Comparison of Plant Detection Performance of CNN-based Single-stage and Two-stage Models for Precision Agriculture

by Kemal Tutuncu and Recai Özcan

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.

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Compensation of degradation, security, and capacity of LSB substitution methods by a new proposed hybrid n-LSB approach

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.

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Determination of the ADF and IVOMD Content of Sugarcane Using Near Infrared Spectroscopy Coupled with Chemometrics

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.

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Adaptive LSB Steganography Based on Chaos Theory and Random Distortion

Advances in Electrical and Computer Engineering, 2018

Image steganography is a technique to hide secret information in an image without leaving any app... more

Performance of Classification Techniques on Medical Datasets

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%).

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Segmentation of Capillary Images in Medicine

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...

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Design Of Air Conditioning Automation For Patisserie Shopwindow

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...

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International Conference on Computer Systems and Technologies- CompSysTech’07 Reverse

modeling of a diesel engine performance by FCM and ANFIS

LSB-based pre-embedding video steganography with rotating shifting poly-pattern block matrix

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 ...

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KBM Based Variable Size DCT Block Approaches for Video Steganography

International Journal of Intelligent Systems and Applications in Engineering, 2021

This study focused on fostering the quality reflection displayed in semester self-evaluation repo... more This study focused on fostering the quality reflection displayed in semester self-evaluation reports (SERs) of first-year honours students in a bachelor engineering program Technology, Liberal Arts and Science (ATLAS). In a first pilot study, students' conceptions of reflection and educational needs regarding reflection were explored. Based on the results and relevant theory, an intervention was designed. Twentynine participants, not previously exposed to academic training on reflection before, received a Reflection Guide on how to write reflections in their SERs. Two online interactive lectures were provided as support. Quality of reflection in the SERs was assessed using a standardized rubric and quality scores in the intervention group were compared with scores of the student cohort of the previous academic year (n = 33). Results showed that the intervention group reflected on a higher level than the comparison group. Perceived usefulness and value of the reflection method proposed were measured in both students and assessors. In general, both students and assessors were positive about the reflection method.

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A Review of Data Analysis Techniques Used in Near-Infrared Spectroscopy

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...

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Occupancy Detection Through Light , Temperature , Humidity and Co 2 Sensors Using

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

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International Journal of Intelligent Systems and Applications in Engineering

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.

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Comparison of LSB image steganography technique in different color spaces

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 Using Artificial Intelligence Techniques

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.

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Investigation of Extremism Behaviors of Young People in Turkey and Member Countries of European Union

Kenar Bulma Algoritmaları ile 2 Boyuttan 3 Boyuta Dönüşüm

Bu tez calismasinda 2B bir icerikten 3B bir icerik olusturma teknikleri olacaktir. Bu tekniklerde... more Bu tez calismasinda 2B bir icerikten 3B bir icerik olusturma teknikleri olacaktir. Bu tekniklerde tek bir goruntu mevcuttur ve amac bu goruntu bilgisini kullanarak ikinci goruntuyu olusturmaktir. En sik kullanilan 2B’den 3B’ye cevrim yontemi derinlik haritasinin olusturulmasi ve bu sekilde DIBR (Depth image based rendering) kullanilarak ikinci goruntunun olusturulmasidir. Bu tez calismasin da veri setleri olusturmak amaciyla materyal olarak Matlab yazilimi kullanilacaktir. Kullanilan bu sistemin girdileri 3 boyutlandirilmasi istenen resim, Fark Degiskeni,,Lineer Derinlik Haritasi Perpesktif Yonu, LDH baslangic ve son degerleri ve isteniyorsa obje bulunmasidir. Cikti olarak elde edebilecegimiz Bolutlenmis Goruntu, Derinlik Haritasi, Sol goruntu, Sag goruntu, 3 Boyutlu goruntudur. Dolayisiyla sistem minumum girdi kullanarak 3 boyutlandirma islemi yapabilmektedir. Ayrica , bu yontem es zamanli olmamakla beraber videolar icinde uygulanabilir. Yapilan arastirmalar ve incelemeler sonucund...

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Analysis of ERASMUS staff and student mobility network within a big European project

The academic mobility is one of key factors that enable the globalization of research and educati... more The academic mobility is one of key factors that enable the globalization of research and education. In this paper we study the network of ERASMUS staff and student exchange agreements between academic institutions involved in FETCH - a big European project oriented towards future education and training in computer science. The structure of the network was investigated relying on standard metrics and techniques of social network analysis. Obtained results indicate that the network is in a mature phase of the development in which none of the institutions has a critical role to the overall connectedness of the network. Additionally, the network has a clear core-periphery structure with an active core and mostly inactive periphery.

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