2009-2020 yılları arasında yayımlanmış olan akademik yayınlar..

Automatic detection of petiole border in plant leaves

Plants are our source of oxygen and nutrients on earth. Therefore, conservation of biodiversity is vital for the survival of other species. With the developing technology, plant species can be examined more closely. Image processing, which is a subject of computer science, has an important role in this field. In this study, an image processing–based method has been developed to automatically separate the petiole region of the plant leaves. To determine the boundary line of the petiole region, the cumulative pixel distributions of the input images in binary format according to the X- and Y-axis are analyzed. Accordingly, optimum thresholds and petiole boundary points are determined. The proposed method was tested on 795 leaf images from 90 different plant species that grow both as trees and shrubs in the Czech Republic. According to the results obtained in experimental studies, it is thought that the proposed method will make an important contribution especially in studies such as automatic classification of plants and leaves and determination of plant species in botanical science.


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Automatic detection of petiole border in plant leaves

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Learn from one data set to classify all – A multi-target domain adaption approach for white blood cell classification

Background and Objective: Traditional machine learning methods assume that both training and test data come from the same distribution. In this way, it becomes possible to achieve high successes when modelling on the same domain. Unfortunately, in real-world problems, direct transfer between domains is adversely affected due to differences in the data collection process and the internal dynamics of the data. In order to cope with such drawbacks, researchers use a method called “domain adaptation”, which enables the successful transfer of information learned in one domain to other domains. In this study, a model that can be used in the classification of white blood cells (WBC) and is not affected by domain differences was proposed. Methods: Only one data set was used as source domain, and an adaptation process was created that made possible the learned knowledge to be used effectively in other domains (multi-target domain adaptation). While constructing the model, we employed data augmentation, data generation and fine-tuning processes, respectively. Results: The proposed model has been able to extract “domain-invariant” features and achieved high success rates in the tests performed on nine different data sets. Multi-target domain adaption accuracy was measured as %98.09. Conclusions: At the end of the study, it has been observed that the proposed model ignores the domain differences and it can adapt in a successful way to target domains. In this way, it becomes possible to classify unlabeled samples rapidly by using only a few number of labeled ones.


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Learn from one data set to classify all – A multi-target domain adaption approach for white blood cell classification

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Classifying White Blood Cells Using Machine Learning Algorithms

Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.


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Classifying White Blood Cells Using Machine Learning Algorithms

Diagnosis of Parkinson's Disease Using K-NN Classifier with Various Distance Measurements

Parkinson's disease is a slow progressive neurodegenerative brain disorder with loss of brain cells. It is a disease that is difficult to diagnose early because the symptoms gradually appear. Depending on the stage of this disease, different degrees of voice and speech disorders may occur. In this study, Oxford Parkinson's dataset was used for the diagnosis of Parkinson's disease, consisting of 195 biomedical voice recordings from 32 individuals, 24 of whom were Parkinson's. The k-NN algorithm was used to diagnose the disease, including Euclidean, Manhattan, Minkowski, Chebyshev, Hellinger and Cosine distance measurements. According to the results obtained, it is seen that the Euclidean, Manhattan and Chebyshev distance measurement methods used for k-NN algorithm provide better classification success than the other methods.


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Diagnosis of Parkinson's Disease Using K-NN Classifier with Various Distance Measurements

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Web-based Remote Control of a Digital Microscope System

Today's technologies are examined, remote control systems come to the forefront in terms of increasing user comfort and reducing the time cost. To take advantage of remote-control systems, an internet connection, infrared, bluetooth, GSM, radio frequency receiver/transmitter etc. wireless communication technologies are needed. Devices controlled through such systems can use efficiently as if you were near them even if they are in different locations. It is also important to use this technology for hazardous or risky situations in human health. In this study, remote control of a digital microscope system was developed as a prototype of peripheral blood smear slides. A web-based application was developed to provide this control. The method consists of two parts as server and client application. Control of the microscope system is provided by the client to send commands to the server via the Internet. Thus, a specialist physician will be able to perform blood analysis of patients from his own office without going to the laboratory.


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Web-based Remote Control of a Digital Microscope System

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Yapay Arı Koloni Algoritmasının Kapasite Kısıtlı Araç Rotalama Problemlerindeki Sonuçlarının Yapay Sinir Ağı ile Tahmin Edilmesi

Optimizasyon algoritmaları, popülerliği gün geçtikçe artan ve bugün itibariyle yapay zekâ açısından önemli konuma gelmiş çözüm yöntemlerindendir. Özellikle NP-hard sınıfındaki kombinatoryal optimizasyon problemlerini çözmek için, sürü zekâsı tabanlı birçok farklı çözüm algoritması geliştirilmiştir. Ancak bu çözüm teknikleri kullanılırken, ilgili algoritma için doğru parametre değerlerini seçmek, çok zaman almaktadır. Bu çalışmada yapay arı koloni (YAK) algoritmasının, kapasite kısıtlı eş zamanlı dağıtım toplamalı araç rotalama problemlerinde (EDTARP), farklı parametre değerleriyle elde ettiği sonuçlar analiz edilmiş ve seçilen parametre değerlerine göre ulaşılacak sonuç değer, yapay sinir ağı (YSA) yaklaşımıyla tahmin edilmeye çalışılmıştır. Elde edilen regresyon verileri, YAK ile elde edilebilecek sonuçların YSA ile tahmin edilebilecek seviyede olduğunu göstermiştir.


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Yapay Arı Koloni Algoritmasının Kapasite Kısıtlı Araç Rotalama Problemlerindeki Sonuçlarının Yapay Sinir Ağı ile Tahmin Edilmesi

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Classification of Morphological Iris Properties Using Naïve Bayes Classifier

In recent years, person recognition and identity inquiry have been frequently encountered in many areas. Especially in places such as private office entrances, airports, banks, this type of person recognition is done under much more stringent controls. Biometric systems emerged with the idea that using human beings as a control tool would be the most reliable way. Biometric systems mean the use of human physiological features such as iris, fingerprint, face, hand for identification purposes. In this study, iris classification was performed on an iris database that can be used in biometrics. For the classification process, Naive Bayes classifier which is the machine learning algorithm was used. The result was 96.04% education and 92.11% test success.


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Classification of Morphological Iris Properties Using Naïve Bayes Classifier

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Automatic Student Attendance System with Face Recognition Technique

In recent years, the subject of face recognition has been studied extensively by researchers. The process of taking classroom attendance in schools is a factor increasing the performance of the students in the lessons. The classroom attendance process is in two ways; Either the student names in the classroom must be read out loud or the attendance paper must be distributed. Either way, the polling process can be time consuming and incorrect. Fingerprint, iris and face recognition based, etc. the methods of classroom attendance with different technological methods were tried. In this study, instantaneous and automatic classroom attendance system has been developed with multiple face recognition method. Thanks to the study, teachers will not lose time to take attendance, and false classroom attendance will be eliminated. Thus, during the course, the students will not be distracted, and the course will be processed more effectively. The developed application was tested with different students in different classroom environments. The application was written in C# programming language.


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Automatic Student Attendance System with Face Recognition Technique

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An Application to Control for Computer and Environmental Devices Via Voice Commands

Technology is everywhere in our lives. It is no longer possible to live without technology or to perform certain operations. In this study, an application was developed to facilitate our daily life. With this application, it is possible to control the operations of mouse and keyboard on the computer with voice commands. Voice commands can be given from anywhere with the wireless headset. This application was developed for people who cannot use the computer for any reason, or who do not see enough to use it. In this way, people will be able to perform their computer-related operations without the need for another person. As a result, people will be able to carry out some activities in their lives more comfortably.


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An Application to Control for Computer and Environmental Devices Via Voice Commands

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An Application for Controlling Television with Voice Commands

Using technology is an indispensable requirement in almost every stage of people's lives. Nowadays it is no longer possible to run some applications without technology. In this study, a sample study that can manage the change of television channel with voice commands was made. Our application has been developed to make it easy for people who cannot meet the need for watching television for any reason. The application was implemented in C# programming language. In order to manage the television with voice commands, firstly, voice recognition libraries were used. In the developed application, a total of 8 channel switching operations can be performed with voice commands. Voice commands can be issued from anywhere in the house where the shooting area is through the wireless headset. After the voice recognition process, digital data was sent to the television remote control by the role card control via the parallel port of the computer.


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An Application for Controlling Television with Voice Commands

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Classification of Cardiotocography Records with Naïve Bayes

Cardiotocography provides information about the fetal heart rate during pregnancy and childbirth, monitoring the uterine contractions and the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, Naive Bayes machine learning algorithm can be used to diagnose the model developed. The result was 97.18% classification and 95.68% test success with Naive Bayes machine learning algorithm. The obtained data were presented in detail in the following sections.


Atıflar

Classification of Cardiotocography Records with Naïve Bayes

A New Approach for Fully Automated Segmentation of Peripheral Blood Smears

Peripheral blood smear is microscopically examining technique for blood samples from patients by painting special dyes in clinic laboratories. Blood diseases can be diagnosed by examining morphology, numbers and percentages of leukocyte, erythrocyte and thrombocyte cells in blood samples. However, this method is a considerably time-consuming process and requires an evaluation performed by a hematology specialist. It is not often provided a definitive assessment due to the expert's clinical experience and judgment during review. Although there are considerable studies about the segmentation of blood smear images in the literature, there is no method to segment all blood cells. In this study, a new segmentation algorithm is proposed, which automatically extracts leukocyte, erythrocyte and thrombocyte cells from peripheral blood smear images. Purpose of this study here is to make highly accurate and complete blood count. The algorithm treats each image as a universal set and represents each object in the image as a subset as a result of the applied operations. In the developed method, leukocytes and thrombocytes achieve better success than other studies. However, it has been observed that the average success rate of stacked erythrocytes decreases. Statistical tests of the developed method were performed using 200 blood smear images in experimental studies. According to the obtained results, it is seen that high accuracy (leukocyte 99.86%, thrombocyte 98.4%, erythrocyte 93.4%) and precision (leukocyte 94.77%, thrombocyte 90.14%, erythrocyte 95.88%) were achieved in all three blood cells.


Atıflar

A New Approach for Fully Automated Segmentation of Peripheral Blood Smears

  1. Elen, A., Turan, M. K., “Classifying White Blood Cells Using Machine Learning Algorithms”, International Journal of Engineering Research and Development, 11(1): 141-152 (2019).

  2. Avuçlu, E., Başçiftçi, F., “New approaches to determine age and gender in image processing techniques using multilayer perceptron neural network”, Applied Soft Computing, vol. 70, pp. 157-168 (2018).

Real Time PCA Based Face Recognition for Following Staff

With the development of technology, security has entered our lives as an indispensable element. Nowadays, people are now using some methods that increase safety in every system. Biometrics technologies used in the identification of the physical properties of the body (facial, fingerprint and fingerprint) have become a common security detection approach today. Different methods are used for biometric applications. In this study, an application was developed by using PCA (Principal Component Analysis) method in the literature using face recognition algorithm. In this application, a workplace with hundreds of employees is followed by face recognition of the arrival and departure of the staff. After the follow-up, the persons who are late to the job or who are early to the desired time are reported to the management mail.


Atıflar

Real Time PCA Based Face Recognition for Following Staff

  1. Avuçlu, E., Elen, A., “Determination of Leaf Type by Image Processing Techniques”, International Journal of Computing Academic Research, vol. 6(5), pp. 136-144 (2017).

Determination of Leaf Type by Image Processing Techniques

There have been many studies in various fields such as engineering, medicine, military applications, geographical applications, space studies with image processing methods. In this study, by applying image processing techniques of digitized leaf images, leaf type was determined according to morphological characteristics. The average of each leaf area is taken in itself. Mean area-based leaf type detection was developed in the C # application environment. In the study, 90 leaf types were determined and a total of 795 leaf images were studied.After the application, 85% recognition was done in 25 leaf types. Recognition of 100% in 65 leaf types was performed. The other findings obtained are presented in the conclusions.


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Determination of Leaf Type by Image Processing Techniques

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Analysis of DNA Gel Electrophoresis Images with Backpropagation Neural Network Based Canny Edge Detection Algorithm

Gel Electrophoresis (GE) is one of the most used methods which separate nucleic acid and protein molecules according to electric charge, amount of them, molecule weights and other physical features. GE is used in many fields such as genetic, molecular biology and biochemistry. In this paper, Canny edge detection algorithm based on artificial neural network is used to separate and detect DNA bands automatically. Thus, GE analysis of gel electrophoresis period is realized and many properties of DNA bounds like intensity are extracted from digital images automatically.


Atıflar

Analysis of DNA Gel Electrophoresis Images with Backpropagation Neural Network Based Canny Edge Detection Algorithm

  1. Taylor, D., Powers, D., “Teaching artificial intelligence to read electropherograms”, Forensic Science International: Genetics, vol. 25, pp. 10–18 (2016).

  2. Avuçlu, E., Elen, A., “Determination of Leaf Type by Image Processing Techniques”, International Journal of Computing Academic Research, vol. 6(5), pp. 136-144 (2017).

Identification of Column Edges of DNA Fragments by Using K-Means Clustering and Mean Algorithm on Lane Histograms of DNA Agarose gel Electrophoresis Images

Gel electrophoresis (GE) is one of the most used method to separate DNA, RNA, protein molecules according to size, weight and quantity parameters in many areas such as genetics, molecular biology, biochemistry, microbiology. The main way to separate each molecule is to find borders of each molecule fragment. This paper presents a software application that show columns edges of DNA fragments in 3 steps. In the first step the application obtains lane histograms of agarose gel electrophoresis images by doing projection based on x-axis. In the second step, it utilizes k-means clustering algorithm to classify point values of lane histogram such as left side values, right side values and undesired values. In the third step, column edges of DNA fragments is shown by using mean algorithm and mathematical processes to separate DNA fragments from the background in a fully automated way. In addition to this, the application presents locations of DNA fragments and how many DNA fragments exist on images captured by a scientific camera.


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Identification of Column Edges of DNA Fragments by Using K-Means Clustering and Mean Algorithm on Lane Histograms of DNA Agarose gel Electrophoresis Images

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Genetik Algoritma ile Çoklu Dizi Hizalama

Çoklu dizi hizalama problemi, Biyoinformatik alanında üzerinde en çok durulan önemli ve temel bir araştırma konusudur. DNA, RNA veya protein dizilerinin düzenlenerek benzer bölgelerin tespit edilmesi ve diziler arasında işlevsel, yapısal veya evrimsel olarak ilişki durumlarının incelenmesi zor bir problem olarak görülmektedir. Günümüzde çoğu bilgisayar hızlı hesaplama ve işlem yapabilme yeteneğine sahip olmasına rağmen, geleneksel çözüm yöntemleri ile çoklu dizi hizalama işlemlerinde halen başarısız olmaktadır. Bundan başka, yaklaşık çözümler üreten bazı yöntemler ise yerel optimuma takılmadır. Bu çalışmada, çoklu dizi hizalama probleminin çözümü için genetik algoritma metotları kullanılarak, sorgu dizilerinin evrimsel bir ilişkiye sahip olduğu göz önünde bulundurarak optimum hizalama sonuçları verecek bir yöntemin geliştirilmesi amaçlanmıştır. Bir sonraki çalışmada ise, elde edilen çoklu dizi hizalamalarından, türdeş (homoloji) çıkarımları ve filogenetik analiz ile dizilerin evrimsel kökenlerinin değerlendirilmesi amaçlanmıştır.


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Genetik Algoritma ile Çoklu Dizi Hizalama

  1. Turan, M. K., Günay, Ö. C., Kayış, S. A., Çörtük, M., “Mikrobiyotada 16S rRNA ve Basit Biyoinformatik Analizler”, Journal of Biotechnology and Strategic Health Research, vol. 2(1), pp. 23-34 (2018).

Karabük Üniversitesi Bilgi Sistemi Android Uygulaması

Günümüzde mobil cihazlar başlangıçtaki kullanım amacı olan cep telefonluğu görevini aşmış ve birer bilgisayar görevini üstelenerek, akıllı cep telefonu olarak kullanılmaya başlanmıştır. Android işletim sistemi ise mobil cihazlara yönelik geliştirilmiş ve günümüzde mobil cihazlar üzerinde en yaygın kullanıma sahip mobil işletim sistemlerinden biridir. Bu çalışmada Karabük Üniversitesi'ne yönelik Android cihazlarda kullanılmak üzere bir bilgi sistemi hazırlanmıştır. Bu uygulama sadece Karabük Üniversitesi öğrencileri veya personeline yönelik değil, Karabük Üniversitesi hakkında bilgi sahibi olmak isteyen ya da Karabük Üniversitesi ile ilgili gelişmeleri takip etmek isteyen Android kullanıcılarına da hizmet verecek şekilde geliştirilmiştir.


Atıflar

Karabük Üniversitesi Bilgi Sistemi Android Uygulaması

  1. Hacıyusufoğlu, A. F., Güler, E., “Tarımda Mobil Uygulamalar”, XVIII. Akademik Bilişim Konferansı, Aydın/TÜRKİYE, pp. 1-7 (2016).

  2. Büyükgöze, S., Subaşı, N., Yaman, S., “Android Studio ile Ders İşlenmesi: Proje Destekli Eğitim”, International Conference on Quality in Higher Education (ICQH 2016), Sakarya/TÜRKİYE, pp. 57-59 (2016).

A Heuristic Optimization Approach for A Real-World University Timetabling Problem

In this study, the solution to the problem of automating the course timetabling used at the universities through the student affairs automation tool has been established by applying one of the heuristic optimization approaches, namely, the genetic algorithm method. The scheduling problem involves the optimal placing of tasks in time slots in accordance with designated constraints. Solving this problem through analytical methods at universities is becoming impossible due to the magnitude of the solution space and the wide range of constraints. Therefore, the genetic algorithm method, which is known to yield very good results for such problems, is used as a new approach, along with the investigation of parameters with the best results. The performance of the algorithm is measured using real data from the university. In order to enable the practical use of the algorithm, the genetic algorithm method is implemented within the student affairs automatization tool.


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A Heuristic Optimization Approach for A Real-World University Timetabling Problem

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Solving of Scheduling Problem with Heuristic Optimization Approach

In this study, solution of course scheduling problems in student affairs automation used in the universities was carried out using Genetic Algorithm method which is one of Heuristic Optimization approaches.Scheduling problem is the process of placing the works to be done in time intervals at optimum level withinthe constraints. The solution of this problem at universities becomes impossible with analytic methods sincesolution space is too wide and there are many constraints. Therefore, giving the best solutions for this type of problems, Genetic Algorithm method was used with a new approach by examining the parameters giving thebest results.The performance of the Algorithm was measured by conducting applications over the actual data from theuniversity (504 instructors, 4163 course, 203 classrooms and 10525 students). Algorithm was developed using ASP.NET, C# programming language and SQL Server Database. Interfaceand reporting pages of student affairs automation were also added to the program for practical use of the Algorithm.


Atıflar

Solving of Scheduling Problem with Heuristic Optimization Approach

  1. Karakoc, M., “Supplier-Task Scheduling for the Logistic Support Regarding Supply Chain Management Based on Precedence”, Journal of Management Marketing and Logistics, 5(4): 267-274 (2018).

  2. Ünal, H. T., “AWACS uçaklarında ekip çizelgeleme probleminin genetik algoritmalar yöntemiyle çözümü”, Yüksek Lisans Tezi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, (2018).

  3. Andrade, P. R. De L., Scarpin, C. T., Steiner, M. T. A., “Utilização De Programação Linear Binária Para Elaboração Da Grade Horária Do Curso De Engenharia De Produção Da UFPR”, XXXII Encontro Nacional De Engenharia De Producao, Bento Gonçalves/Brasil, pp. 1-13 (2012).

  4. Andrade, P. R. De L., Scarpin, C. T., Steiner, M. T. A., “Geração Da Grade Horária Do Curso De Engenharia De Produção Da UFPR Através De Programação Linear Binária”, the 44th Brazilian Operations Research Symposium / 16th Latin Ibero American Conference on Operations Research (XLIV SBPO/XVI CLAIO), Rio De Janerio/Brazil, pp. 1052-1063 (2012).

  5. Karakoç, M., Günay, M., Çiğdem, G., Alturjman, F., “A Meta-Heuristic Approach for Course Scheduling in Akdeniz University”, The 28th International Conference of The Jangjeon Mathematical Society (ICJMS'2015), Antalya/TURKEY, pp. 116-117 (2015).

  6. Ertuğrul, İ., Öztaş, G. Z., “Ders Programı Oluşturulmasında 0-1 Tam Sayılı Bulanık Hedef Programlama Yaklaşımı”, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 9(1), pp. 159-177 (2016).

Bilgisayar Destekli Otomotiv Test Standının Internet Üzerinden Uzaktan Eğitim Amaçlı Kullanımı İçin Kullanıcı Arayüzlü Yazılım Tasarımı

Bu çalışmada Bilgisayar Destekli Otomotiv Test Standının (BDOTS) İnternet üzerinden gerçek zamanlı denetimi gerçekleştirilmektedir. İçten yanmalı motora ait (devir, tork, sıcaklık, debi vb.) veriler algılayıcılar kullanılarak, veri alış-veriş kartı aracılığı ile bilgisayar ortamına aktarılmaktadır. BDOTS’a uzaktan erişim ve kontrol işlemleri .NET platformunda C# dili kullanılarak Server (Sunucu) ve Client (İstemci) uygulamaları ile gerçekleştirilmiştir. Sunucu tarafı yazılım ile BDOTS’a ait ölçüm değerleri ve ölçüm grafikleri örnekleme zamanına bağlı olarak görülebilmekte ve istenildiği anda istemci bilgisayarlar aracılığıyla bu ölçümler gönderilebilmektedir. İnternet tabanlı uzaktan eğitim için gerçek zamanlı görüntü ve ses paketlerinin de İstemci bilgisayarlara gönderilmesi sağlanmaktadır. Bu sayede tehlikeli deneylerin uzaktan kontrol edilerek yapılması sağlanmıştır. Birçok kullanıcının yapılan deneylerin İnternet üzerinden izlenebilmesi sayesinde deney tekrarı azaltılmaktadır. Bu da ekonomik katkı sağlamaktadır. Deney yapılan ortamda öğrencilerin bulunmaması zehirli gazlarında solunmasını önlemektedir. Geliştirilen bu yazılım, uzaktan eğitim sisteminde, tehlikeli ve maliyeti yüksek deney düzeneklerine de rahatlıkla uyarlanabilir.


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Bilgisayar Destekli Otomotiv Test Standının Internet Üzerinden Uzaktan Eğitim Amaçlı Kullanımı İçin Kullanıcı Arayüzlü Yazılım Tasarımı

  1. Topaloğlu, N., “İnternet Üzerinden Uzaktan Web Kamera Kontrolünün Tasarımı ve Uygulaması”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 27(2), pp. 229-235 (2013).

  2. Aktaş, A., Aydın, M., Sekmen, P., “Bir AC Jeneratörün Motor Dinamometresi Olarak Kullanılabilirliğinin Araştırılması”, El-Cezeri Journal of Science and Engineering, vol. 3(3), pp. 498-505 (2016).

Development and Implementation of Web Based Fuzzy Logic Content for Distance Education

In this study, the development and implementation of web-based content for distance education of fuzzy logic course which is being educated at Karabük University, Institute of Science and Technology, Electronic and Computer Education Department is described. For this, international studies which are related with educational design, human - computer interaction and ways to improve content topics were examined. To provide learning in electronic environment, presenting the content as understandable, supporting with convenience audio-visual materials, configuring by evaluating the specific expression formats and detection processes for people and being in structure that facilitating the learning of learning person’s interaction with computer are aimed.


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Development and Implementation of Web Based Fuzzy Logic Content for Distance Education

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