Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
Browse
3 results
Search Results
Publication Metadata only Combining spatial proximity and temporal continuity for learning invariant representations(2012) Kursun, Olcay; Aytekin, Tevfik; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyLocation and time are two critical aspects of most security-related events, and thus, spatiotemporal data analysis plays a central role in many security-related applications. The human brain has great capabilities of developing invariant representations of objects by taking advantage of both spatial similarity of features of objects/events and their relative timings (temporal information). Trace learning rule is one well-known solution for this problem of combining temporal relations with spatial proximity in clustering tasks such as the one performed by self organizing maps. In this work, we investigate a two stage mechanism: i) finding local clusters using spatial proximity, ii) grouping these clusters as suggested by temporal continuity patterns. We show our experimental results on a movie created from face images. © 2012 IEEE. © 2013 Elsevier B.V., All rights reserved.Publication Metadata only Improving the performance of active voxel selection in the analysis of fMRI data using genetic algorithms(2013) Ülker, Ceyhun Can; Aytekin, Tevfik; Ülker, Ceyhun Can, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyRecent research has shown that it is possible to classify cognitive states of human subjects based on fMRI (functional magnetic resonance imaging) data. One of the obstacles in classifying fMRI data is the problem of high dimensionality. A single fMRI snapshot consists of thousands of voxels and since a single experiment contains many fMRI snapshots, the dimensionality of an fMRI data instance easily surpasses the order of tens of thousands. So, feature selection methods become a must from both classification and running time performance points of view. To this end several feature selection methods are studied, either general or specific to fMRI data. So far, one of the best such methods, which is specific to fMRI data, is called the active method [9]. In this work we combine genetic algorithms with the active method in order to improve the performance of feature selection. Specifically, we first reduce the feature dimension using the active method and search for informative features in that reduced space using genetic algorithms. We achieve better or similar levels of classification performance using a much smaller number of voxels than the active method offers. Copyright 2013 ACM. © 2014 Elsevier B.V., All rights reserved.Publication Metadata only Short term water demand forecasting using regional data, Bölgesel veriler üzerinde yapilan kisa dönem su talep tahmini(Institute of Electrical and Electronics Engineers Inc., 2019) Zeynep Yildiz, Tugba; Aytekin, Tevfik; Zeynep Yildiz, Tugba,; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyLimited water resources and changing climatic conditions make water one of the critical natural resources. In order to manage this limited resource in the most effective way, real-time monitoring and automatic control systems are becoming increasingly popular. Water demand forecasting is one of the important subjects in these studies. Accurate water demand forecasting increases efficiency in the management of water networks and also allows for leak/fraud detection. In this work, we carry out short term water demand forecasting using water consumption data collected from water meters in a regional area. For forecasting, we first clean water consumption data, extract various features and apply machine learning methods for forecasting. After giving the experimental results we discuss future improvements. © 2020 Elsevier B.V., All rights reserved.