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Publication Metadata only Coupled Shape Priors for Dynamic Segmentation of Dendritic Spines(IEEE, 2017) Atabakilachini, Naeimeh; Erdil, Ertunc; Argunsah, A. Ozgur; Rada, Lavdie; Unay, Devrim; Cetin, Mujdat; Sabanci University; University of Zurich; Bahcesehir University; Izmir Ekonomi UniversitesiSegmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results.Publication Metadata only Face Recognition under Ageing Effect: A Comparative Analysis(SPRINGER-VERLAG BERLIN, 2013) Akhtar, Zahid; Rattani, Ajita; Hadid, Abdenour; Tistarelli, Massimo; Petrosino, A; Bahcesehir University; Michigan State University; University of Oulu; University of SassariPrevious studies indicate that performance of the face recognition system severely degrades under the ageing effect. Despite the rising attention to facial ageing, there exist no comparative evaluation of the existing systems under the impact of ageing. Moreover, the compound effect of ageing and other variate such as glasses, gender etc, that are known to influence the performance, remain overlooked till date. To this aim, the contribution of this work are as follows: 1) evaluation of six baseline facial representations, based on local features, under the ageing effect, and 2) analysis of the compound effect of ageing with other variates, i.e., race, gender, glasses, facial hair etc.Publication Metadata only Logistics tool selection with two-phase fuzzy multi criteria decision making: A case study for personal digital assistant selection(PERGAMON-ELSEVIER SCIENCE LTD, 2012) Buyukozkan, Gulcin; Arsenyan, Jbid; Ruan, Da; Galatasaray University; Bahcesehir University; Belgian Nuclear Research Centre (SCK CEN)Efficient logistics and supply chain management are enabled through the use of efficient information technologies (IT). The mobile logistics tools represent the IT interface in the supply chain. This paper aims to aid decision makers to identify the most appropriate mobile logistics tools and to achieve this aim, several evaluation criteria are identified to evaluate logistics tools, and a fuzzy axiomatic design (FAD) based group decision-making method is adopted to perform the evaluation in two phases. In the first phase of pre-assessment, alternatives that cannot meet basic requirements and the defined threshold are eliminated. In the second phase of selection, the remaining alternatives are more meticulously evaluated. Criteria weights are determined using fuzzy analytic hierarchy process (AHP) and another fuzzy multicriteria decision-making (MCDM) technique, namely fuzzy technique for order preference by similarity to ideal solution (TOPSIS), is applied in the second phase to compare the outcome of FAD. A case study is provided in order to demonstrate the potential of the proposed methodology. Personal digital assistants (PDAs) with integrated barcode scanner that are available in the Turkish market are evaluated. (C) 2011 Elsevier Ltd. All rights reserved.Publication Metadata only RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks(IEEE, 2018) Bircanoglu, Cenk; Atay, Meltem; Beser, Fuat; Genc, Ozgun; Kizrak, Merve Ayyuce; Yildirim, T; Manolopoulos, Y; Angelov, P; Iliadis, L; Bahcesehir University; Middle East Technical University; Yildiz Technical University; Koc UniversityWaste management and recycling is the fundamental part a sustainable economy. For more efficient and safe recycling, it is necessary to use intelligent systems instead of employing humans as workers in the dump-yards. This is one of the early works demonstrating the efficiency of latest intelligent approaches. In order to provide the most efficient approach, we experimented on well-known deep convolutional neural network architectures. For training without any pre-trained weights, Inception-Resnet, Inception-v4 outperformed all others with 90% test accuracy. For transfer learning and fine-tuning of weight parameters using ImageNet, DenseNet121 gave the best result with 95% test accuracy. One disadvantage of these networks, however, is that they are slightly slower in prediction time. To enhance the prediction performance of the models we altered the connection patterns of the skip connections inside dense blocks. Our model RecycleNet is carefully optimized deep convolutional neural network architecture for classification of selected recyclable object classes. This novel model reduced the number of parameters in a 121 layered network from 7 million to about 3 million.Publication Metadata only Voting-Based Multiple Classification Approach for Turkish News Texts(IEEE, 2019) Buluz, Basak; Komecoglu, Yavuz; Kizrak, Merve Ayyuce; Gebze Technical University; Bahcesehir UniversityNowadays, there are numerous sources on the internet that produce news on a daily basis. Through this growing knowledge base, it makes it difficult for users to access the information and news they are looking for. It is important to classify the information for fast and efficient search and access. In this study, a dataset consisting of Turkish news content Kemik prepared by Yildiz Technical University, Natural Language Processing Group, used. A hierarchical approach based on a voting structure is adopted by using machine learning based approaches. In order to solve the problem, firstly Tf-Idf method is applied for word 1-3-ngrams and character 2-6-ngrams. Thus, the 2000 dimensional feature vector is pre-trained. By using FastText, 300-dimensional feature vectors and 2 feature vectors are combined to produce 2300-dimensional feature vectors.. In order to determine the one that will increase the classification accuracy among these vectors, Support Vector Machines method is applied and Tf-Idf method which has the robust accuracy is determined as the main feature extraction method. Next, Support Vector Machines, K-Nearest Neighborhood Method, Random Forest, Logistic Regression, XGBoost methods are used for the classification of news texts. Estimated label values from all classifiers are voted for each sample and the label with the highest voting rate is considered as the final estimate. In this study, it is aimed to open the way to reach the right information quickly by classifying news topics. Finally, the feature vector size has been reduced using Principal Component Analysis and it is possible to gain processing speed without reducing performance. In both approaches, it is seen that the performance achieved by voting is higher than the individual performance rates of the classifiers.Publication Metadata only Augmented Reality Tool for Markerless Virtual Try-on around Human Arm(IEEE, 2015) Gunes, San; Sanli, Okan; Ergun, Ovgu Ozturk; Stadon, J; Gwilt, I; Smith, CH; Bahcesehir UniversityWe present a Markerless 3D Augmented Reality Application for virtual accessory try-on applications around human arm. The system is based on a Kinect sensor and a multi-layer rendering framework to render RGB, depth data and 3D model of accessories simultaneously. The aim is to support realistic visualization of virtual objects around human arm, by detecting wrist pose and handling occlusion for various interactive marketing and retail applications, such as virtual watch try-on.Publication Metadata only Exploration Strategy Related Design Considerations of WSN-Aided Mobile Robot Exploration Teams(SPRINGER-VERLAG BERLIN, 2012) Tuna, Gurkan; Gulez, Kayhan; Gungor, Vehbi Cagri; Mumcu, Tarik Veli; Huang, D; Gan, Y; Gupta, P; Gromiha, MM; Trakya University; Yildiz Technical University; Bahcesehir UniversityThis paper presents a novel approach to mobile robot exploration. In this approach, mobile robots send their local maps to the central controller and coordinate with each other using a wireless sensor network (WSN). Different from existing rendezvous point-based exploration strategies, the use of a WSN as the communication media allows quick and cost-effective exploration and mapping of an unknown environment. Overall, this paper introduces WSN-aided mobile robot exploration strategy and shows comparative performance evaluations using the Player/Stage simulation platform. Here, our main goal is to present potential advantages of WSN-aided mobile robot exploration for Simultaneous Localization and Mapping (SLAM).Publication Metadata only Accurate Prediction of Advertisement Clicks based on Impression and Click-Through Rate using Extreme Gradient Boosting(SCITEPRESS, 2019) Cakmak, Tulin; Tekin, Ahmet T.; Senel, Cagla; Coban, Tugba; Uran, Zeynep Eda; Sakar, C. Okan; DeMarsico, M; DiBaja, GS; Fred, A; Bahcesehir UniversityOnline travel agencies (OTAs) aim to use digital media advertisements in the most efficient way to increase their market share. One of the most commonly used digital media environments by OTAs are the metasearch bidding engines. In metasearch bidding engines, many OTAs offer daily bids per click for each hotel to get reservations. Therefore, management of bidding strategies is crucial to minimize the cost and maximize the revenue for OTAs. In this paper, we aim to predict both the impression count and Click-Through-Rate (CTR) metrics of hotel advertisements for an OTA and then use these values to obtain the number of clicks the OTA will take for each hotel. The initial version of the dataset was obtained from the dashboard of an OTA which contains features for each hotel's last day performance values in the search engine. We enriched the initial dataset by creating features using window-sliding approach and integrating some domain-specific features that are considered to be important in hotel click prediction. The final set of features are used to predict next day's CTR and impression count values. We have used state-of-the-art prediction algorithms including decision tree-based ensemble methods, boosting algorithms and support vector regression. An important contribution of this study is the use of Extreme Gradient Boosting (XGBoost) algorithm for hotel click prediction, which overwhelmed state-of-the-art algorithms on various tasks. The results showed that XGBoost gives the highest R-Squared values in the prediction of all metrics used in our study. We have also applied a mutual information filter feature ranking method called minimum redundancy-maximum relevance (mRMR) to evaluate the importance of the features used for prediction. The bid value offered by OTA at time t - 1 is found to be the most informative feature both for impression count and CTR prediction. We have also observed that a subset of features selected by mRMR achieves comparable performance with using all of the features in the machine learning model.Publication Metadata only RANSAC-based Training Data Selection for Speaker State Recognition(ISCA-INT SPEECH COMMUNICATION ASSOC, 2011) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, A. Tanju; Koc University; Bahcesehir University; Ozyegin UniversityWe present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Sub-challenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxication Sub-challenge performance.Publication Metadata only Forecasting Hotel Room Sales within Online Travel Agencies by Combining Multiple Feature Sets(SCITEPRESS, 2019) Aras, Gizem; Ayhan, Gulsah; Sarikaya, Mehmet Ali; Tokuc, A. Aylin; Sakar, C. Okan; DeMarsico, M; DiBaja, GS; Fred, A; Bahcesehir UniversityHotel Room Sales prediction using previous booking data is a prominent research topic for the online travel agency (OTA) sector. Various approaches have been proposed to predict hotel room sales for different prediction horizons, such as yearly demand or daily number of reservations. An OTA website includes offers of many companies for the same hotel, and the position of the company's offer in OTA website depends on the bid amount given for each click by the company. Therefore, the accurate prediction of the sales amount for a given bid is a crucial need in revenue and cost management for the companies in the sector. In this paper, we forecast the next day's sales amount in order to provide an estimate of daily revenue generated per hotel. An important contribution of our study is to use an enriched dataset constructed by combining the most informative features proposed in various related studies for hotel sales prediction. Moreover, we enrich this dataset with a set of OTA specific features that possess information about the relative position of the company's offers to that of its competitors in a travel metasearch engine website. We provide a real application on the hotel room sales data of a large OTA in Turkey. The comparative results show that enrichment of the input representation with the OTA-specific additional features increases the generalization ability of the prediction models, and tree-based boosting algorithms perform the best results on this task.