Browsing by Keywords "Machine learning"

Now showing items 1-20 of 25

    • Activity recognition invariant to sensor orientation with wearable motion sensors 

      Yurtman, A.; Barshan, B. (MDPI AG, 2017)
      Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is ...

    • Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation 

      Ravens, U.; Katircioglu-Öztürk, D.; Wettwer, E.; Christ, T.; Dobrev, D.; Voigt, N.; Poulet, C.; Loose, S.; Simon, J.; Stein, A.; Matschke, K.; Knaut, M.; Oto, E.; Oto, A.; Güvenir, H. A. (Springer, 2015)
      Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability ...

    • Assessment and correction of errors in DNA sequencing technologies 

      Fırtına, Can (Bilkent University, 2018-01)
      Next Generation Sequencing technologies differ by several parameters where the choice to use whether short or long read sequencing platforms often leads to trade-offs between accuracy and read length. In this thesis, I ...

    • Chat mining: predicting user and message attributes in computer-mediated communication 

      Kucukyilmaz T.; Cambazoglu, B. B.; Aykanat, C.; Can, F. (Elsevier Ltd, 2008-07)
      The focus of this paper is to investigate the possibility of predicting several user and message attributes in text-based, real-time, online messaging services. For this purpose, a large collection of chat messages is ...

    • Data mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’ 

      Okutucu, S.; Katircioglu-Öztürk, D.; Oto, E.; Güvenir, H. A.; Karaagaoglu, E.; Oto, A.; Meinertz, T.; Goette, A. (Oxford University Press, 2016)
      Aims: The aims of this study include (i) pursuing data-mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial dataset containing atrial fibrillation (AF) burden scores ...

    • Deepside: predicting drug side effects with deep learning 

      Üner, Onur Can (Bilkent University, 2019-10)
      Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and cause substantial financial losses. Side effect prediction algorithms, on the other hand, have the potential ...

    • Diagnosis of gastric carcinoma by classification on feature projections 

      Güvenir, H. A.; Emeksiz, N.; İkizler, N.; Örmeci, N. (Elsevier, 2004)
      A new classification algorithm, called benefit maximizing classifier on feature projections (BCFP), is developed and applied to the problem of diagnosis of gastric carcinoma. The domain contains records of patients with ...

    • An energy efficient additive neural network 

      Afrasiyabi, A.; Nasir, B.; Yıldız, O.; Yarman-Vural, F. T.; Çetin, Ahmet Enis (IEEE, 2017)
      In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called additive neural network, is very suitable for ...

    • Fall detection and classification using wearable motion sensors 

      Turan, Mustafa Şahin (Bilkent University, 2017-09)
      Effective fall-detection systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is wearable sensor based fall-detection systems. ...

    • Generalizing predicates with string arguments 

      Cicekli, I.; Cicekli, N. K. (Springer New York LLC, 2006-06)
      The least general generalization (LGG) of strings may cause an over-generalization in the generalization process of the clauses of predicates with string arguments. We propose a specific generalization (SG) for strings to ...

    • Inducing translation templates with type constraints 

      Çiçekli, İlyas (Springer, 2005)
      This paper presents a generalization technique that induces translation templates from a given set of translation examples by replacing differing parts in the examples with typed variables. Since the type of each variable ...

    • Induction of logical relations based on specific generalization of strings 

      Uzun, Yasin (Bilkent University, 2007)
      Learning logical relations from examples expressed as first order facts has been studied extensively by the Inductive Logic Programming research. Learning with positive-only data may cause overgeneralization of examples ...

    • Investigation of Sensor Placement for Accurate Fall Detection 

      Ntanasis, P.; Pippa, E.; ÖZdemir, A. T.; Barshan, B.; Megalooikonomou, V. (Springer Verlag, 2017)
      Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the ...

    • Learning with feature partitions 

      Şirin, İzzet (Bilkent University, 1993)
      This thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, ...

    • A machine learning approach for result caching in web search engines 

      Kucukyilmaz T.; Cambazoglu, B. B.; Aykanat, C.; Baeza-Yates R. (Elsevier, 2017)
      A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access ...

    • Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity 

      Öztoprak, H.; Toycan, M.; Alp, Y. K.; Arıkan, O.; Doğutepe, E.; Karakaş S. (Elsevier Ireland Ltd, 2017)
      Objective Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its ...

    • Machine-based learning system: classification of ADHD and non-ADHD participants 

      Öztoprak, H.; Toycan, M.; Alp, Y. K.; Arıkan, Orhan; Doğutepe, E.; Karakaş, S. (IEEE, 2017)
      Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is ...

    • Mathematical model of causal inference in social networks 

      Şimsek, Mustafa; Delibalta, İ.; Baruh, L.; Kozat, Süleyman Serdar (IEEE, 2016)
      In this article, we model the effects of machine learning algorithms on different Social Network users by using a causal inference framework, making estimation about the underlying system and design systems to control ...

    • Predicting risk of mortality in patients undergoing cardiovascular surgery 

      Tunca, Ayşen (Bilkent University, 2008)
      It is very important to inform the patients and their relatives about the risk of mortality before a cardiovascular operation. For this respect, a model called EuroSCORE (The European System for Cardiac Operative Risk ...

    • Problem representation for refinement 

      Guvenir, H. A.; Akman, V. (Springer Netherlands, 1992)
      In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An ...