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Öğe Adaptive convolution kernel for artificial neural networks(Academic Press Inc., 2021-02) Tek, Faik Boray; Çam, İlker; Karlı, DenizMany deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 × 3) kernels. This paper describes a method for learning the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ‘‘Faces in the Wild’’ showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing ordinary convolution layers in a U-shaped network with 7 × 7 adaptive layers can improve its learning performance and ability to generalize.Öğe An adaptive locally connected neuron model: Focusing neuron(Elsevier B.V., 2021-01-02) Tek, Faik BorayThis paper presents a new artificial neuron model capable of learning its receptive field in the topological domain of inputs. The experiments include tests of focusing neuron networks of one or two hidden layers on synthetic and well-known image recognition data sets. The results demonstrated that the focusing neurons can move their receptive fields towards more informative inputs. In the simple two-hidden layer networks, the focusing layers outperformed the dense layers in the classification of the 2D spatial data sets. Moreover, the focusing networks performed better than the dense networks even when 70% of the weights were pruned. The tests on convolutional networks revealed that using focusing layers instead of dense layers for the classification of convolutional features may work better in some data sets.Öğe Adaptive visual obstacle detection for mobile robots using monocular camera and ultrasonic sensor(Springer-Verlag, 2012-10-07) İyidir, İbrahim Kamil; Tek, Faik Boray; Kırcalı, DoğanThis paper presents a novel vision based obstacle detection algorithm that is adapted from a powerful background subtraction algorithm: ViBe (VIsual Background Extractor). We describe an adaptive obstacle detection method using monocular color vision and an ultrasonic distance sensor. Our approach assumes an obstacle free region in front of the robot in the initial frame. However, the method dynamically adapts to its environment in the succeeding frames. The adaptation is performed using a model update rule based on using ultrasonic distance sensor reading. Our detailed experiments validate the proposed concept and ultrasonic sensor based model update.Öğe Animal sound classification using a convolutional neural network(IEEE, 2018-12-06) Şaşmaz, Emre; Tek, Faik BorayIn this paper, we investigate the problem of animal sound classification using deep learning and propose a system based on convolutional neural network architecture. As the input to the network, sound files were preprocessed to extract Mel Frequency Cepstral Coefficients (MFCC) using LibROSA library. To train and test the system we have collected 875 animal sound samples from an online sound source site for 10 different animal types. We report classification confusion matrices and the results obtained by different gradient descent optimizers. The best accuracy of 75% was obtained by Nesterov-accelerated Adaptive Moment Estimation (Nadam).Öğe Assessment of algorithms for mitosis detection in breast cancer histopathology images(Elsevier Science BV, 2015-02) Veta, Mitko; Van Diest, Paul J.; Willems, Stefan Martin; Wang, Haibo; Madabhushi, Anant; Cruz-Roa, Angel; Gonzalez, Fabio; Larsen, Anders Boesen Lindbo; Vestergaard, Jacob Schack Chack; Dahl, Anders Bjorholm; Cireşan, Dan Claudiu; Schmidhuber, Jürgen U.; Giusti, Alessandro; Gambardella, Luca M.; Tek, Faik Boray; Walter, Thomas C.; Wang, Chingwei; Kondo, Satoshi; Matuszewski, Bogdan J.; Precioso, Frédéric; Snell, Violet; Kittler, Josef; De Campos, Teofilo E.; Khan, Adnan M.; Rajpoot, Nasir Mahmood; Arkoumani, Evdokia; Lacle, Miangela M.; Viergever, Max A.; Pluim, Josien P WThe proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.Öğe 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.Öğe 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.Öğe 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.Öğe 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.Öğe Forecasting and analysis of energy consumption and waste generation in Antalya with SVR(IEEE, 2023-12-24) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalAntalya, a rapidly expanding coastal city in Türkiye, has experienced significant changes due to urbanization and increasing tourism activities. Comprehending tourism trends is crucial for the city's sustainable development and environmental management. Based on this perspective, this paper aims to present a comprehensive retrospective analysis of Antalya's energy consumption, domestic solid waste generation, wastewater generation, population growth, and tourist numbers over the years. Antalya faces significant challenges due to escalating trends in listed areas. Utilizing the Support Vector Regression, this study projects a need for an additional 1715 GWh of electricity production capacity, an expansion of wastewater capacity by 85639 thousand m3, and an increase in domestic solid waste disposal capacity by 597745 tons by 2028 to accommodate growing demands. We emphasize the importance of adopting effective policies and strategies to support energy efficiency, waste reduction, and wastewater management alongside sustainable urban planning and tourism management for Antalya's long-Term environmental sustainability and development. The findings presented in this study provide valuable insights for policymakers, urban planners, and stakeholders to make informed decisions, ensuring a balanced approach toward economic growth and environmental conservation.Öğe 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.Öğe 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.Öğe 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.Öğe 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.Öğe Malaria parasite detection with deep transfer learning(IEEE, 2018-12-06) Var, Esra; Tek, Faik BorayThis study aims to automatically detect malaria parasites (Plasmodium sp) on images taken from Giemsa stained blood smears. Deep learning methods provide limited performance when sample size is low. In transfer learning, visual features are learned from large general data sets, and problem-specific classification problem can be solved successfully in restricted problem specific data sets. In this study, we apply transfer learning method to detect and classify malaria parasites. We use a popular pre-trained CNN model VGG19. We trained the model for 20 epoch on 1428 P Vivax, 1425 P Ovule, 1446 E Falciparum, 1450 P Malariae and 1440 non-parasite samples. The transfer learning model achieves %80, %83, %86, %75 precision and 83%, 86%, 86%, 79% f-measure on 19 test images.Öğe Microsoft Kinect Sensörü kullanarak zemin düzlemi algılama(IEEE, 2013-06-13) Kırcalı, Doğan; Tek, Faik Boray; İyidir, İbrahim KamilGörüntü işleme tabanlı mobil robotların başarılı navigasyonu için zemin düzlemi algılama esastır. Bu bildiride Kinect derinlik sensöründen elde edilen derinlik bilgisine dayalı yeni ve gürbüz bir zemin düzlemi algılama algoritması önerilmektedir. Literatürdeki benzer yöntemlerin aksine, zemin düzleminin sahnedeki en büyük alan olduğunu varsayılmamaktadır. Yöntemimiz sensörün yeri görüş açısının sabit olduğu veya değişken olabileceği iki farklı durum için iki değişik algoritma halinde sunulmaktadır. Yaptığımız deneylerde her iki durum için önerdiğimiz algoritmaların oldukça başarılı olduğu gösterilmektedir.Öğe Mitosis detection using generic features and an ensemble of cascade adaboosts(Elsevier, 2013-05-30) Tek, Faik BorayContext: Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. Aims: We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object -level descriptions and thus require minimal segmentation. Materials and Methods: The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. Statistical Analysis Used: We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F -measure, and region overlap ratio measures. Results: We tested our features with two different classifier configurations: 1)An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non -cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F -measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape?based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. Conclusions: The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.Öğe Odaklanan nöron(IEEE, 2017-06-27) Çam, İlker; Tek, Faik BorayGeleneksel yapay sinir ağında topoloji eğitim sırasında değişebilecek esnekliğe sahip değildir. Ağda her bir nöron ve bağımsız bağlantı katsayıları çözüm işlevinin bir parçasıdır. Bu bildiride önerdiğimiz odaklanabilir nöron birbirine bağımlı katsayıların çekildiği bir odaklayıcı işlevden yararlanır. Nöron odak pozisyonu ve açıklığını değiştirerek aktivasyon topladığı nöronları değiştirebilir. Bu özelliği sayesinde esnek ve dinamik bir ağ topolojisi oluşturabilir ve standart geriye yayılım algoritmasıyla eğitilebilir. Yapılan deneylerde odaklanabilir nöronlarla kurulan bir ağ yapısının, tümüyle bağlı yapay sinir ağına göre daha yüksek başarı elde ettiği gözlenmiştir.Öğe Programlamaya giriş dersi öğrencilerinin öz yeterlilik algıları ve derse yönelik tutumlarının cinsiyet ve e?itim 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.Öğe Programlamaya giriş dersini alan öğrencilerin programlama öz yeterlilik algılarının ve programlamaya bakış açılarının incelenmesi(Düzce Üniversitesi, 2021-05-29) Benli, Kristin Surpuhi; Tek, Faik BorayBu çalışmada üniversite öğrencilerinin Java programlama öz yeterlilik algıları, programlama öğrenme istekleri ve çalışma alışkanlıkları çeşitli değişkenlere göre (cinsiyet, bölüm, eğitim dili, harf notu, ders tekrarları vb.) istatistiksel yöntemler kullanılarak (T-testi, Mann Whitney U-testi, Kruskal Wallis H testi, tek yönlü varyans analizi, Ki-Kare testi) incelenmiştir. Çalışma grubu, farklı bölümlerde zorunlu olarak programlamaya giriş dersini alan 191 lisans öğrencisinden oluşmaktadır. Elde edilen sonuçlara göre öğrencilerin Java programlama öz yeterlilik algıları bölümlerine ve programlama öğrenme isteklerine göre farklılaşmaktadır. Çalışmada ayrıca Apriori algoritması kullanılarak birliktelik kuralları çıkartılmıştır. En yüksek güven değeri elde edilen kurala göre, programlama öğrenmeyi çok fazla isteyen, programlama öğrenmenin iş hayatında kendisine fayda sağlayacağını düşünen ve programlama dersinden başarı ile geçen öğrencilerin programlama öz yeterlilikleri yüksektir.
