Browsing by Keywords "Machine learning"
Now showing items 1-20 of 28
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Activity recognition invariant to sensor orientation with wearable motion sensors
(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 ... -
Adaptive ensemble learning with confidence bounds for personalized diagnosis
(AAAI Press, 2016)With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in ... -
Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation
(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
(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 ... -
Bilişsel algılamaya doğru: çok görevli öğrenme ile radar fonksiyon sınıflandırma
(IEEE, 2019-04)Elektronik Harp (EH) sistemleri için ortamda yayın yapan bir radarın tespiti ve ilgili radarın fonksiyonunun belirlenmesi, sistemin en önemli görevlerinden biridir. Bu çalışmada, EH sistemleri tarafından ölçülen radar ... -
Chat mining: predicting user and message attributes in computer-mediated communication
(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 ... -
A comparative study on prediction of the indoor soundscape in museums via machine learning
(Institute of Noise Control Engineering(INCE), 2019-06)This paper presents the preliminary findings of a soundscape research, which uses machine learning to make a prediction about human perception for indoor auditory environments. Museums of Çengelhan Rahmi Koc and Erim Tan ... -
Data mining experiments on the Angiotensin II-Antagonist in Paroxysmal Atrial Fibrillation (ANTIPAF-AFNET 2) trial: ‘exposing the invisible’
(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 ... -
Deep learning in electronic warfare systems: automatic pulse detection and intra-pulse modulation recognition
(Bilkent University, 2020-12)Detection and classification of radar systems based on modulation analysis on pulses they transmit is an important application in electronic warfare systems. Many of the present works focus on classifying modulations ... -
Deepside: predicting drug side effects with deep learning
(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
(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
(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
(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
(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
(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
(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
(Springer, 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
(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
(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
(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 ...
