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Enhancing digital literacy skills among teachers for effective integration of computer science and design education: a case study at Astana International School, Kazakhstan
(Frontiers Media SA, 2024) Temirkhanova, Meruyert; Abildinova, Gulmira; Karaca, Celal
This study explores the development and impact of digital literacy skills among teachers at Astana International School, Kazakhstan, and examines how these skills influence the teaching of Computer Science and Design to middle school students. Employing a mixed-methods approach, the research combined quantitative assessments of students’ proficiency with qualitative evaluations of teacher and student experiences, involving 71 teachers and 382 students from grades 7 to 10. The findings indicate that students taught by digitally literate teachers demonstrated significant improvements in designing and utilizing virtual reality tools, mobile applications, and other digital resources, with teachers facilitating more interactive and engaging learning environments that enhanced students’ technical skills and creative capacities. This research contributes new insights into the dynamics of digital literacy in education, emphasizing the critical role of teacher training in digital tools for enhancing educational practices and uniquely demonstrating how systematic application of digital literacy can transform educational outcomes, supporting the integration of technology in teaching, aligned with the needs and competencies of Generation Z students.
Integrated application of digital technologies in interconnected energy sources in renewable energy education
(World Institute for Engineering and Technology Education, 2024) Karatayeva, Zhanerke; Abildinova, Gulmira; Karaca, Celal; Mukhtarkyzy, Kaussar
This study evaluates the impact of digital technology on renewable energy education among fourth-year IT students at a Kazakhstani university through a quasi-experimental design, comparing traditional teaching methods to a curriculum integrated with digital tools, including interactive simulations. Results demonstrated that students in the digital tools-enhanced group significantly outperformed their peers in traditional settings in mastering renewable energy concepts, aligning with literature that supports the effectiveness of technology-enhanced learning. However, satisfaction levels were similar across both groups, suggesting the importance of combining traditional and digital approaches. The research highlights the necessity of blending theoretical knowledge with practical skills, noting a skills confidence gap in the digital group. This study advocates for an educational strategy that merges conventional methods with digital innovations, providing insights for educators and curriculum developers to meet the evolving demands of sustainability and technology education.
Determining the most accurate machine learning algorithms for medical diagnosis using the monk’ problems database and statistical measurements
(Taylor and Francis Ltd., 2023) Avuçlu, Emre
Computer-aided diagnosis process in the field of health, especially cancer diagnosis, is of vital importance. Computer-aided diagnosis helps specialist physicians to make the most accurate diagnosis. According to research studies, it has been stated that the number of wrong or late diagnosis increases with each passing year and ultimately causes the death of people living in many parts of the world. For this reason, some calculations must be made to determine the most accurate one in the algorithm to be used to make the correct diagnosis. In this study, three different database Monk’ problems were used to determine the most accurate algorithm for medical diagnosis. Monk’ problems are used as one of the several classification problems used to create an important comparative study. Train and test operations were performed using five different Machine Learning Algorithms (MLAs) (k Nearest Neighbor (k-NN), Decision Tree Algorithm (DT), Random Forest Algorithm (RF), Naive Bayes algorithm (NB), Support Vector Cases (SVM)). These machine learning algorithms are compared statistically in terms of performance. Two different databases in the medical field were used to test the results (Breast Cancer Coimbra Data Set, Diabetic Retinopathy Debrecen Data Set). In the test processes in the experimental studies, the highest accuracy rate was obtained from the k-NN, DT, RF, NB, SVM algorithms, respectively; 0.9758, 1, 1, 0.9180, 0.9344. The best performance was obtained from RF MLA for 1. dataset, DT MLA for 2. dataset, highest accuracy rates from k-NN and RF MLAs in 3. dataset.
COVID-19 detection using X-ray images and statistical measurements
(Elsevier B.V., 2022) Avuçlu, Emre
The COVID-19 pandemic spread all over the world, starting in China in late 2019, and significantly affected life in all aspects. As seen in SARS, MERS, COVID-19 outbreaks, coronaviruses pose a great threat to world health. The COVID-19 epidemic, which caused pandemics all over the world, continues to seriously threaten people's lives. Due to the rapid spread of COVID-19, many countries' healthcare sectors were caught off guard. This situation put a burden on doctors and healthcare professionals that they could not handle. All of the studies on COVID-19 in the literature have been done to help experts to recognize COVID-19 more accurately, to use more accurate diagnosis and appropriate treatment methods. The alleviation of this workload will be possible by developing computer aided early and accurate diagnosis systems with machine learning. Diagnosis and evaluation of pneumonia on computed tomography images provide significant benefits in investigating possible complications and in case follow-up. Pneumonia and lesions occurring in the lungs should be carefully examined as it helps in the diagnostic process during the pandemic period. For this reason, the first diagnosis and medications are very important to prevent the disease from progressing. In this study, a dataset consisting of Pneumonia and Normal images was used by proposing a new image preprocessing process. These preprocessed images were reduced to 15x15 unit size and their features were extracted according to their RGB values. Experimental studies were carried out by performing both normal values and feature reduction among these features.
Galerkin methods for the numerical solution of the Schrödinger equation by using trigonometric B-splines
(University of Miskolc, 2022) Mersin, Mehmet Ali; Irk, D.; Görgülü, M. Zorşahin
SciVal Topics Funding details Abstract This paper includes four finite element methods which are based on quadratic, cubic, quartic and quintic trigonometric B-spline functions for space discretization and Crank-Nicolson method for time discretization, to be achieved the numerical solution of the Schrödinger equation (SE). The algorithms obtained by different degrees trigonometric B-spline Galerkin methods are new for getting numerical solution of the SE. To see the accuracy of the proposed methods, two numerical experiments are investigated and the comparison of the methods are given in the test problem section.
A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis
(PMC, 2022) Avuçlu, Emre
Pandemics and many other diseases threaten human life, health and quality of life by affecting many aspects. For this reason, the medical diagnosis to be applied for any disease is important in terms of the most accurate determination by the doctors and the appropriate treatment for the determined diagnosis. The COVID-19 pandemic that started in China in December 2019 spread all over the world in a short time. Researchers have begun to do different studies to make the most accurate diagnosis of COVID-19. Due to the rapid spread of COVID-19, doctors in the health sector of many countries were also caught off guard. Machine Learning Algorithms (MLAs) are of great importance in the development of computer-aided early and accurate diagnosis systems in today's medical field, as they greatly assist doctors in the medical diagnosis process. In this study, a method was proposed for the most accurate diagnosis of COVID-19 patients using the COVID-19 image data. Images were first standardized and features extracted using RGB values of 800x800 images, and these features were used in train and test processes for MLAs. 5 different MLAs were used in experimental studies using statistical measurements (k Nearest Neighbor (k-NN), Decision Tree (DT), Multinominal Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM)). A method was proposed that automatically finds the highest classification success that these algorithms can achieve. In experimental studies, the following accuracy rates were obtained in train operations for MLAs, respectively; 1, 1, 1, 0.69565, 0.92753. Accuracy results in test operations were obtained as follows; 0.85714, 0.79591, 0.91836, 0.61224, 0.89795. After the application of the proposed method, the test success rate for MLR increased from 0.91 to 0.98. As a result of applying the proposed algorithm, more accurate results were obtained. The results obtained were given in the experimental studies section in detail. The results obtained proved to be very promising. According to the results, it was seen that the proposed method could be used effectively in future studies.
A hybrid machine learning model for classifying time series
(Springer Science and Business Media Deutschland GmbH, 2022) Elen, Abdullah; Avuçlu, Emre
A time series is a sequence of numerical data points in equal time intervals and/or successive order. Time series are used in many fields to understand the behavior of systems, to make predictions, to create solutions to problems, etc. Electroencephalogram (EEG) and electrocardiogram (ECG) are frequently used in the diagnosis of diseases and research in this field. EEG signals examine the neural activity of the brain, while ECG examines the work of the heart muscle and neural conduction system. These signs contain a large amount of information about the functioning of the brain and heart functions. In order to use this information, experts in the field of signal processing must evaluate these signals. Due to the successful application methods of EEG and ECG signals in classification problems, various fields of artificial intelligence applications are frequently used by experts. In this study, a new hybrid model has been developed to classify EEG and ECG signals. These signals of five different classes have been used as the feature vector in the training of machine learning algorithms with 10 statistical parameters (8 normalized, 2 real signals). These algorithms are designed to give the best performance. In the proposed hybrid model, a machine learning model consisting of four stages is used. In the experimental studies, it has been observed that the proposed hybrid method gives better results than the normal classification process. The obtained results are given in the experimental studies section in detail.
A new dynamic feature extraction method for biometric images
(Gazi Üniversitesi, 2021) Avuçlu, Emre; Elen, Abdullah; Özçifçi, Ayhan
The image of biometric properties in humans is used in many fields today. Regardless of these features, it is necessary to first translate it into data that the computer understands. In this study, automatic and dynamic image segmentation was performed by using 300x300 fingerprint images. A fingerprint database with a total of 80 images and 10 different classes was used. The features of the images were subtracted from the sub-segments obtained from these images by the feature extraction algorithm that was originally developed. The 300x300 images were divided into 25x25 sub-images and the feature vector was obtained. 144x80 inputs obtained after image segmentation were kept in areas in separate tables. The developed segmentation and feature extraction algorithm can be applied to any image of equal size.
An interactive robot design to find missing people and inform their location by real-time face recognition system on moving images
(Wiley, 2022) Avuçlu, Emre; Başçiftçi, Fatih
In every country over the world, missing children and adults is a social problem. This problem affects both the relatives of the missing people and the community materially and morally. This article presents a new and original project developed to find a solution to this major social problem. There are three main parts of the project that are synchronized and interactive with each other. Each main part has its own sub-parts. In the first main part, there are units consisted of a robot working structure wandering around the outer world with a radio control (R/C) camera, solar power panel, and shock device unit. In the second main part, there is an interface program with implementations such as face recognition, short message service (SMS) sending, and warning programmed in the computer. In the third main part, there is a mobile phone and communication process on the robot transferring the information regarding the location. Finding the missing people is realized by synchronous communication of the robot and the interface implementation. As a result, an important study was carried out for the process of finding missing persons, which is a common and major social problem of the whole world.
A new data augmentation method to use in machine learning algorithms using statistical measurements
(Elsevier B.V., 2021) Avuçlu, Emre
Cancer disease is among the leading causes of death in the world today. It has been scientifically proven that if the disease is detected in the first stages, the success rate in treatment can be close to 100%. Accordingly, it can be said that the problems caused by breast cancer can be solved to a great extent thanks to early diagnosis. Data sets that can be processed in early diagnosis are required. Researchers use artificial intelligence techniques to develop systems to assist specialist doctors. It is of great importance to have a data set on which researchers can work. The more parameters of these data sets and the number of these parameters, the more artificial learning process takes place. In this study, both a new data augmentation method for two different classes in the database of breast cancer was presented and breast cancer was diagnosed with this method. First, the database was analyzed statistically with the proposed data augmentation method. Then, the new values obtained from the data augmentation process were added to the database with a 5-times and 10-times augmentation. Experimental studies were conducted between raw database and databases with augmentatied data. The proposed model with 5 different Machine Learning Algorithms (MLA) (k Nearest Neighbors (k-NN), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF)) and Support Vector Machine (SVM)) were tested. The accuracy rates in train and test operations were increased in the following order; for k-NN, same ? 15%, for DT, 0.1–13.01%, for RF, same ? 14.03%, for NB, 14.7%?19.7%, for SVM, 15.9%?14.06%. The rates obtained from the experimental results proved to be very promising. The proposed method can be used as an alternative data augmentation method for researchers to get more accurate results in their studies.
An application to control media player with voice commands
(Gazi Üniversitesi, 2020) Avuçlu, Emre; Özcifçi, Ayhan; Elen, Abdullah
Using technology today is of great importance in terms of making people's lives easier. It has become very easy to run some applications with technology. In this study, an application that provides media player control with voice commands was developed. This application was developed to address the needs of people who cannot listen to music on their own due to any disability. The application was implemented in C# programming language. In order to manage the media player with voice commands, voice recognition libraries were first used. In the developed application, operations with keyboard and mouse can be done with voice commands. Voice commands can be sent with the wireless headset from anywhere in the shooting area.
Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements
(Springer Science and Business Media Deutschland GmbH, 2020) Avuçlu, Emre; Elen, Abdullah
Parkinson’s disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson’s disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson’s patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson’s disease. Eleven different training and test data (45 × 55, 50 × 50, 55 × 45, 60 × 40, 65 × 35, 70 × 30, 75 × 25, 80 × 20, 85 × 15, 90 × 10, 95 × 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.
Determination age and gender with edge detection algorithms using dental X-ray images
(TUBITAK, 2020) Başçiftçi, Fatih; Avuçlu, Emre
Age determination in forensic medicine is a very important issue in terms of criminal and law. In case of any disaster or unlawfulness, identification may be required. In such cases, the competent authorities ask the forensic medicine institution for determining the age. Forensic medicines must make the most accurate process of determining the age. In this study, a database was created manually with a total of 1313 teeth images to determine age and gender. Images in this database are pre-processed with image processing techniques. Numerical data were obtained according to the tanimoto similarity rates of images. This numerical data is saved in the XML file. Comparing is done with the data on this XML file. In addition to the age determination process, gender determination process was also carried out. Age determination was performed with +-0 error. The application was developed in C # programming language.
A comparison of classification methods for diagnosis of Parkinson's
(İsmail Sarıtaş, 2020) Elen, Abdullah; Avuçlu, Emre
Parkinson's is a neurological health problem and one of the most common diseases affecting more than four million people worldwide. Recent studies have shown that deterioration of vocal cords, especially from Parkinson's, provides important information in the diagnosis and follow-up of the disease. In this study, a database of biomedical voice recordings from 32 people of different ages and genders was used to diagnose Parkinson's disease. With this database, the performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN) and Naïve Bayes (NB) classifiers were performed. Seven different distance measurement methods (Chebychev, Correlation, Cosine, Euclidean, Hamming, Mahalanobis, and Spearman) for the k-NN and five different distribution methods (Uniform kernel, Epanechnikov kernel, Gaussian kernel, Triangular kernel and Normal distribution) for the NB classifier were performed in the performance process and separate tests were performed. The data obtained from these tests were compared with statistical measurements. In experimental studies, we used 10-fold cross validation technique for Parkinson dataset. Better results were obtained from k-NN classification algorithm than Naive Bayes classification algorithm. While k-NN mean accuracy score was 82.34%, this ratio was obtained as 74.15% for NB. Mahalanobis distance measurement method was found to give better results.
Standardized Variable Distances: A distance-based machine learning method
(Elsevier, 2021) Elen, Abdullah; Avuçlu, Emre
Today, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naive Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accuracy (CACC), F-measure (FM) and area under the curve (AUC) on the WBCO dataset. On the led7digit dataset, approximately 74% CACC, 75.1% FM and 82.2% AUC scores were obtained. It has been observed that the classification scores obtained with the SVD are higher than other ML algorithms used in the experimental studies.
Making inferences about settlements from satellite images using glowworm swarm optimization
(Korean Institute of Electrical Engineers, 2020) Avuçlu, Emre; Elen, Abdullah; Kahramanlı Örnek, Humar
Optimization is the process of choosing the best one among existing possibilities under particular circumstances in a problem. There are various algorithms for optimization problems nowadays. Metaheuristic algorithms are the algorithms giving almost optimum solutions at an acceptable duration for the problems of large dimension. Heuristic optimization algorithms with general aim are evaluated in different groups. Swarm intelligence-based optimization algorithms were developed through examining the behaviors and movements of living flocks such as birds, fish, cats, and bees. With these algorithms, some estimating processes are carried out successfully in all areas. In this study a new approach was presented with a novel idea, by inspiring from the behavior type of Glowworm Swarm Optimization; and an application estimating the total population, square measurement and electricity quantity that was consumed by the chosen areas in a region was developed. The developed application works as a real-time and animated display. When all calculations are finished, the animation ends. Estimates also examined England as an example. The difference between the estimated value of the actual population of England is calculated as 1.7%. In the estimates for the values of the surface area of England with an error of 1.4%, the estimated values were very close to the actual values. Some other obtained estimation results are presented in the results section.
Automatic detection of petiole border in plant leaves
(SAGE Publications Ltd, 2021) Elen, Abdullah; Avuçlu, Emre
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.
The determination of age and gender by implementing new image processing methods and measurements to dental X-ray images
(Elsevier, 2020) Avuçlu, Emre; Başçiftçi, Fatih
All of the features used to identify and distinguish people from others constitute that person's identity. For any reason, a person's identity may need to be identified and distinguished from other people. Authorities provided the credentials of a living or dead person in such cases from the forensic institutions. The identification process must be done correctly. In this study, specific measurement calculations were made on dental x-ray images to determine age and gender. Age and gender information of the persons were systematically determined by working with panoramic dental x-ray images. Panoramic dental x-ray images were taken out of bounds, and a total of 1315 tooth images and 162 different tooth groups were used. These images have been subjected to 3 different preprocess operations. Each preprocessed image is recorded in different (M1, M2, M3) folders. Then, image processing techniques applied for the first time to the tooth images (Area, Perimeter, Center of gravity, Similarity ratio, Radius calculation) were applied. This information of the teeth is also kept in separate XML (XMLlist-1, 2, 3) files. The application was developed in C # programming language. The user loads the tooth image into the application. This image can be predicted by comparing it with the comparison group (area, etc.) after the desired preprocessing. The highest estimated age and gender estimates are 100% and 95%, respectively.
2014 Yılında eğitim teknolojileri alanındaki yayımlanan makalelerin incelenmesi
(Eğitim Teknolojisi Kuram ve Uygulama, 2016) Çakmak Kılıç, Ebru; Özüdoğru, Gül; Bozkurt, Şeyma Büşra; Ülker, Ülkü; Ünsal Özgül, Nimet; Boz, Kübranur; Bozkurt, Ömer Faruk; Karaca, Celal
Bu çalışmanın amacı, 2014 yılına ait Social Sciences Citation Index (SSCI) kapsamındaki eğitim teknolojileri alanında önde gelen uluslararası sekiz dergideki makalelerin içerik analizi yöntemiyle incelenmesidir. Araştırmada “Eğitim Teknolojileri Yayın Sınıflama Formu” adı ile Sözbilir ve Kutu (2008), Masood (2004), Reeves (1995) çalışmalarından yararlanılarak Göktaş vd. (2012) tarafından geliştirilip, ardından Kiliç-Çakmak vd. (2013)’ın üzerinde bazı değişiklikler yaparak “Makale İnceleme Formu (MİF) (Article Review Form)” olarak adlandırdıkları veri toplama aracı kullanılmıştır. Araştırma sonuçları incelendiğinde makalelerde; yöntem olarak en çok “nicel yöntem”, veri toplama aracı olarak en çok “anket”, veri toplama yöntemi olarak en çok “klasik”, örneklem seçimi olarak en çok “kolay ulaşılabilir örneklem”, örneklem sayısı olarak en çok “31-100 aralığı”, örneklem düzeyi olarak en çok eğitim fakültesi dışındaki fakültelerde “lisans (diğer)”, örneklem türü olarak en çok fen, matematik, sosyal dışındaki “diğer”, veri analiz yöntemi olarak en çok “kestirimsel analiz” yönteminin daha çok tercih edildiği ve “öğretim tasarımı” ve “eğitimde bilişim teknolojileri” konularının ise en çok incelenen konular olduğunu görülmüştür.
An expert system design to diagnose cancer by using a new method reduced rule base
(Elsevier, 2018) Basçiftçi, Fatih; Avuçlu, Emre
Background and objectives: A Medical Expert System (MES) was developed which uses Reduced Rule Base to diagnose cancer risk according to the symptoms in an individual. A total of 13 symptoms were used. With the new MES, the reduced rules are controlled instead of all possibilities (2(13) = 8192 different possibilities occur). By controlling reduced rules, results are found more quickly. The method of two-level simplification of Boolean functions was used to obtain Reduced Rule Base. Thanks to the developed application with the number of dynamic inputs and outputs on different platforms, anyone can easily test their own cancer easily. Methods: More accurate results were obtained considering all the possibilities related to cancer. Thirteen different risk factors were determined to determine the type of cancer. The truth table produced in our study has 13 inputs and 4 outputs. The Boolean Function Minimization method is used to obtain less situations by simplifying logical functions. Diagnosis of cancer quickly thanks to control of the simplified 4 output functions. Results: Diagnosis made with the 4 output values obtained using Reduced Rule Base was found to be quicker than diagnosis made by screening all 2(13) = 8192 possibilities. With the improved MES, more probabilities were added to the process and more accurate diagnostic results were obtained. As a result of the simplification process in breast and renal cancer diagnosis 100% diagnosis speed gain, in cervical cancer and lung cancer diagnosis rate gain of 99% was obtained. Conclusions: With Boolean function minimization, less number of rules is evaluated instead of evaluating a large number of rules. Reducing the number of rules allows the designed system to work more efficiently and to save time, and facilitates to transfer the rules to the designed Expert systems. Interfaces were developed in different software platforms to enable users to test the accuracy of the application. Any one is able to diagnose the cancer itself using determinative risk factors. Thereby likely to beat the cancer with early diagnosis. (C) 2018 Elsevier B.V. All rights reserved.

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