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Yayın A haar classifier based call number detection and counting method for library books(IEEE, 2018-12-06) Kanburoğlu, Ali Buğra; Tek, Faik BorayCounting and organization of books in libraries is a routine and time-consuming task The task gets more complicated by misplaced books in shelves. In order to solve these problems, we propose an automated visual call number (book-id) detection and counting system in this paper. The method employs a Haar feature-based classifier from OpenCV library and cloud-based OCR system to decode characters from images. To develop and test the method, we have acquired and organized a dataset of 1000 book call numbers. The proposed method has been tested on 20 bookshelves images that contain 233 call numbers, which resulted in a true detection rate of 96% and false detection rate of 1.75 per image. For OCR step, the number of false recognized characters per call number was 0.76.Yayın Automated cell nucleus detection for large-volume electron microscopy of neural tissue(IEEE, 2014-04-29) Tek, Faik Boray; Kroeger, Thorben; Hamprecht, Fred A.; Mikula, ShawnVolumetric electron microscopy techniques, such as serial block-face electron microscopy (SBEM), generate massive amounts of image data that are used for reconstructing neural circuits. Typically, this requires time-intensive manual annotation of cells and their connections. To facilitate this analysis, we study the problem of automated detection of cell nuclei in a new SBEM dataset that contains cerebral cortex, white matter, and striatum from an adult mouse brain. The dataset was manually annotated to identify the locations of all 3309 cell nuclei in the volume. We make both dataset and annotations available here. Using a hybrid approach that combines interactive learning, morphological processing, and object level feature classification, we demonstrate automated detection of cell nuclei at 92.4% recall and 95.1% precision. These algorithms are not RAM-limited and can scale to arbitrarily large datasets.Yayın Implicit theories and self-efficacy in an introductory programming course(Institute of Electrical and Electronics Engineers Inc, 2018-08) Tek, Faik Boray; Benli, Kristin Surpuhi; Deveci, EzgiContribution: This paper examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy help in understanding academic success, which must be considered when developing effective learning strategies for programming.Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy, related to each other and to student success in programming? Is it possible to predict student performance in a course using these constructs? Methodology: Two consecutive surveys ({N}=100 and {N}=81) were administered to non-CS engineering students in Işik University, Turkey. Findings: Implicit theories of programming-aptitude and programming-efficacy are interrelated and positively correlated with effort, performance, and previous failures in the course. Although it was not possible to predict student course grade the data confirms that students who believe in improvable programming aptitude have significantly higher programming efficacy, report more effort, and get higher course grades. In addition, failed students tend to associate the failure with fixed programming aptitude; repeating students favor fixed programming aptitude theory and have lower programming-efficacy, which increases the possibility of further failure.Yayın Hotel sales forecasting with LSTM and N-BEATS(IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalTime series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, water level), and hotel sales forecasting. This paper focuses on hotel sales forecasting with machine learning and deep learning solutions. A simple forecast solution is to repeat the last observation (Naive method) or the average of the past observations (Average method). More sophisticated solutions have been developed over the years, such as machine learning methods that have linear (Linear Regression, ARIMA) and nonlinear (Polynomial Regression and Support Vector Regression) methods. Different kinds of neural networks are developed and used in time series forecasting problems, and two of the successful ones are Recurrent Neural Networks and N-BEATS. This paper presents a forecasting analysis of hotel sales from Türkiye and Cyprus. We showed that N-BEATS is a solid choice against LSTM, especially in long sequences. Moreover, N-BEATS has slightly better inference time results in long sequences, but LSTM is faster in short sequences.Yayın Etkileşimli öğrenme ile akciğer tomografi hacim taramalarında nodül tespiti(Institute of Electrical and Electronics Engineers Inc., 2016-06-20) Çam, İlker; Tek, Faik BorayBu bildiride akciğer BT taramalarında otomatik nodül tespiti yapmak üzere geliştirdigimiz yeni ve özgün bir yöntem sunulmaktadır. Önerdiğimiz yöntem, akciğer organına ve belirli bir nodül tipine bağlı kalmaksızın genelleştirilmiş bir yaklaşım sunmaktadır. Böylelikle akciğer bölütlemesine ihtiyaç duymamaktadır. Düşük doz radyasyonlu ve çeşitli tipte (katı ve kırık cam görünümlü, yüzeye ve damara ilişik) 10 mm’den küçük nodüllerden oluşan zorlu bir tarama kümesinde (Anode09) sınamalar yapılmıştır. Tarama başına ortalama 8 yanlış tespit için nodül tespit duyarlılığı %52’dir. Yarışmada ilk altıya giren algoritmalarla karşılaştırılabilir düzeydedir.Yayın Texture recognition for frog identification(ACM SIGMM, 2012-11-02) Cannavo, Flavio; Nunnari, Giuseppe; Kale, İzzet; Tek, Faik BorayThis paper describes a visual processing technique for automatic frog (Xenopus Laevis sp.) localization and identification. The problem of frog identification is to process and classify an unknown frog image to determine the identity which is recorded previously on an image database. The frog skin pattern (i.e. texture) provides a unique feature for identification. Hence, the study investigates three different kind of features (i.e. Gabor filters, granulometry, threshold set compactness) to extract texture information. The classifier is built on nearest neighbor principle; it assigns the query feature to the database feature which has the minimum distance. Hence, the study investigates different distance measures and compares their performance. The detailed results show that the most successful feature and distance measure is granulometry and weighted L1 norm for the frog identification using skin texture features.Yayın Ground plane detection using an RGB-D sensor(Springer, 2014-10-27) Kırcalı, Doğan; Tek, Faik BorayGround plane detection is essential for successful navigation of vision based mobile robots. We introduce a very simple but robust ground plane detection method based on depth information obtained using anRGB-Depth sensor. We present two different variations of the method: the simplest one is robust in setups where the sensor pitch angle is fixed and has no roll, whereas the second one can handle changes in pitch and roll angles. Our comparisons show that our approach performs better than the vertical disparity approach. It produces accurate ground plane-obstacle segmentation for difficult scenes, which include many obstacles, different floor surfaces, stairs, and narrow corridors.Yayın Programlamaya giriş dersi öğrencilerinin öz yeterlilik algıları ve derse yönelik tutumlarının cinsiyet ve eğitim diline göre incelenmesi(IEEE, 2017-10-31) Deveci, Ezgi; Aydın, Damla; Benli, Kristin Surpuhi; Tek, Faik BorayBu araştırmanın amacı F.M.V. Işık Üniversitesi Mühendislik Fakültesinde öğrenim gören öğrencilerin genel öz-yeterlilik algılarının ve Programlamaya Giriş(CSE101) dersine yönelik tutumlarının; cinsiyet ve eğitim aldıkları programın diline (Türkçe-İngilizce) göre incelenmesidir. Araştırmaya 40 kadın ve 74 erkek olmak üzere toplam 114 üniversite öğrencisi katılmıştır. Öğrencilerin öz yeterlilik algılarını ölçmek için Genel Öz Yeterlilik ölçeği kullanılmış, ders sonucunu (başarı ve başarısızlık) değerlendirmeleri için açık uçlu sorular sorulmuş ve yaş, cinsiyet gibi temel demografik bilgileri alınmıştır. Açık uçlu sorular niteliksel (kalitatif) analiz yöntemi ile incelenmiştir. Yapılan niceliksel analiz sonucunda öğrencilerin genel öz-yeterlilik puanları ile genel not ortalaması arasında anlamlı, CSE101 dersi dönem sonu not ortalaması arasında ise anlamsız bir ili ki olduğu bulgulanmıştır. Ayrıca öğrencilerin öz-yeterlilik puanlarının cinsiyete ve eğitim aldıkları dile göre (Türkçe-İngilizce) değişmediği görülmüştür. Öğrencilerin motivasyon puanları da eğitim aldıkları dile göre farklılaşmamaktadır. Niteliksel analiz bulgularına göre ise öğrencilerin verdiği cevapların yüzde sıklık değerlerinin cinsiyetleri açısından değiştiği görülmüştür. Bu çalışmanın sonuçları özellikle öğrencilerin derse yönelik tutumlarında cinsiyet açısından bir farklılık olduğunu göstermesi ile mühendislik programlama eğitiminde öğrenci başarısını yordayan değişkenlerin tespit edilmesi sürecine katkı sağlaması beklenmektedir.Yayın Convolutional attention network for MRI-based Alzheimer's disease classification and its interpretability analysis(IEEE, 2021-09-17) Türkan, Yasemin; Tek, Faik BorayNeuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), help to identify Alzheimer's disease (AD). These techniques generate large-scale, high-dimensional, multimodal neuroimaging data, which is time-consuming and difficult to interpret and classify. Therefore, interest in deep learning approaches for the classification of 3D structural MRI brain scans has grown rapidly. In this research study, we improved the 3D VGG model proposed by Korolev et al. [2]. We increased the filters in the 3D convolutional layers and then added an attention mechanism for better classification. We compared the performance of the proposed approaches for the classification of Alzheimer's disease versus mild cognitive impairments and normal cohorts on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We observed that both the accuracy and area under curve results improved with the proposed models. However, deep neural networks are black boxes that produce predictions that require further explanation for medical usage. We compared the 3D-data interpretation capabilities of the proposed models using four different interpretability methods: Occlusion, 3D Ultrametric Contour Map, 3D Gradient-Weighted Class Activation Mapping, and SHapley Additive explanations (SHAP). We observed that explanation results differed in different network models and data classes.Yayın Forecasting and analysis of domestic solid waste generation in districts of istanbul with support vector regression(Institute of Electrical and Electronics Engineers Inc., 2020-10-12) Özçelik, Şuayb Talha; Tek, Faik BorayWaste planning is essential for large and developing cities such as Istanbul. In this report, we perform data analysis on "Waste Amount Based on District, Year and Waste Type"dataset shared by Istanbul Metropolitan Municipality. After analyzing the waste of the districts, we used support vector regression (SVR) to forecast the waste amounts for the coming years. The analysis has shown an overall increasing trend in the waste generation, although it dropped in 2019. The SVR predicts that the most waste generating district will be Küçükçekmece in the coming years.
