• Blind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3D-DCT coefficients 

  Yılmaz, Gökçe Nur; Cemiloğlu, E. (Institution of Engineering and Technology, 2020-04-17)

  There is an urgent need for a robust video quality assessment (VQA) model that can efficiently evaluate the quality of a video content varying in terms of the distortion and content type in the absence of the reference ...
• Solving Assembly Line Workload Smoothing Problem via Answer Set Programming 

  Sabuncu, Orkunt; ÅžimÅek, M.C. (CEUR-WS, 2020)

  Assembly line balancing problems aim to assign production tasks to workstations or people while satisfying some constraints and optimizing various criteria such as production rate or number of workstations needed. One ...
• A scalable K-nearest neighbor algorithm for recommendation system problems 

  Sağdıç, Ahmet; Tekinbaş, C.; Arslan, E.; Küçükyılmaz, T. (Institute of Electrical and Electronics Engineers Inc., 2020-09-28)

  Memory-based classification techniques are commonly used for modeling recommendation problems. They rely on the intuition that similar users and/or items behave similarly, facilitating user-to-item, item-to-item, or ...
• Boosting gender identification using author preference 

  Küçükyılmaz, Tayfun; Deniz, Ayça; Kızılöz, Hakan Ezgi (ELSEVIER, 2020-12)

  Predicting the gender of a text document's author, also known as gender identification, is a well-studied authorship categorization task in the literature. A common theme in gender identification studies is that gender is ...
• Exploiting temporal changes in query submission behavior for improving the search engine result cache performance 

  Küçükyılmaz, Tayfun (ELSEVIER, 2021-05)

  A commonly used technique for improving query response time in retrieval systems is storing query-relevant information in a fast access memory storage such as a result cache. The effectiveness of the result cache heavily ...
• Analysis of multiobjective algorithms for the classification of multi-label video datasets 

  Dökeroğlu, Tansel; Karagöz, Gizem Nur; Yazıcı, Adnan; Coşar, Ahmet (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2020)

  It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key ...
• Deep into visual saliency for immersive VR environments rendered in real-time 

  Albayrak, Dilara; Çapın, Tolga K.; Çelikcan, Ufuk; Askın, Mehmet Bahadır (PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND, 2020-05)

  As virtual reality (VR) headsets with head-mounted-displays (HMDs) are becoming more and more prevalent, new research questions are arising. One of the emergent questions is how best to employ visual saliency prediction ...
• A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data 

  Dökeroğlu, Tansel; Karagöz, Gizem Nur; Yazıcı, Adnan; Coşan, Ahmet (SPRINGER HEIDELBERG, TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, 2020-06)

  There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is ...
• Context and content based scalable video adaptation for ubiquitous multimedia communication services 

  Nur Yılmaz, Gökçe (SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2020-10)

  A novel scalable video adaptation framework is developed to achieve the aim of ensuring enhanced Rate-Distortion (R-D) performances for the ubiquitous multimedia communication services in this study. This novel framework ...
• Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization 

  Dökeroǧlu, Tansel; Pehlivan, Selen; Avenoğlu, Bilgin (2020)

  This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO) for the multi-dimensional numerical problems. The best ...
• Human action recognition in first person videos using verb-object Pairs 

  Pehlivan, Selen; Gökçe, Zeynep (Institute of Electrical and Electronics Engineers Inc., 2019)

  Human action recognition problem is important for distinguishing the rich variety of human activities in first-person videos. While there has been an improvement in egocentric action recognition, the space of action ...
• A survey on new generation metaheuristic algorithms 

  Dökeroǧlu, Tansel; Sevinç, Ender; Küçükyılmaz, Tayfun; Coşar, Ahmet (Elsevier Ltd., 2019)

  Metaheuristics are an impressive area of research with extremely important improvements in the solution of intractable optimization problems. Major advances have been made since the first metaheuristic was proposed and ...
• Optimized Multiple Description Coding for Temporal Video Scalability 

  Choupani, R.; Wong, S.; Tolun, M. (2013)

  The vast application of video streaming over the Internet requires video adaptation to the fluctuations of the available bandwidth, and the rendering capabilities of the receiver device. On the other hand, the available ...
• Using context information for staged configuration of feature models 

  Karataş, A.S.; Doǧru, A.H.; Oǧuztüzün, H.; Tolun, M. (2011)

  Since their introduction feature models have been widely accepted and used in Software Product Lines. However, feature models may become too large easily, which complicates the management of the model. One of the reasons ...
• A novel parallel local search algorithm for the maximum vertex weight clique problem in large graphs 

  Sevinc, E.; Dokeroglu, T. (Springer Verlag, 2019)

  This study proposes a new parallel local search algorithm (Par-LS) for solving the maximum vertex weight clique problem (MVWCP) in large graphs. Solving the MVWCP in a large graph with millions of edges and vertices is an ...
• A machine learning framework for full-reference 3D shape quality assessment 

  Yildiz, Z.C.; Oztireli, A.C.; Capin, T. (Springer Verlag, 2018)

  To decide whether the perceived quality of a mesh is influenced by a certain modification such as compression or simplification, a metric for estimating the visual quality of 3D meshes is required. Today, machine learning ...
• A hybrid energy harvesting framework for energy efficiency in wireless sensor networks based smart grid applications 

  Yildiz, H.U.; Gungor, V.C.; Tavli, B. (Institute of Electrical and Electronics Engineers Inc., 2018)

  In smart grid applications, Wireless Sensor Net-works (WSNs) which consist of battery limited sensor nodes are used on critical equipments of power distribution grids for monitoring purposes. WSN nodes have tight energy ...
• A Fully Object-space Approach for Full-reference Visual Quality Assessment of Static and Animated 3D Meshes 

  Yildiz, Z.C.; Capin, Tolga (VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2019)

  3D mesh models are exposed to several geometric operations such as simplification and compression. Several metrics for evaluating the perceived quality of 3D meshes have already been developed. However, most of these metrics ...
• A comprehensive study of the affective and physiological responses induced by dynamic virtual reality environments 

  Cebeci, B.; Celikcan, U.; Çapın, Tolga (John Wiley and Sons Ltd, 2019)

  Previous studies showed that virtual reality (VR) environments can affect emotional state and cause significant changes in physiological responses. Aside from these effects, inadvertently induced cybersickness is a notorious ...
• A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines 

  Sevinc, Ender; Dokeroglu, Tansel (A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines, 2019)

  Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the ...

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