Browsing by Keywords "Machine learning"
Now showing items 1-20 of 45
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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 ... -
Anomaly detection in diverse sensor networks using machine learning
(Bilkent University, 2022-01)Earthquake precursor detection is one of the oldest research areas that has the potential of saving human lives. Recent studies have enlightened the fact that strong seismic activities and earthquakes affect the electron ... -
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 ... -
An approach based on sound classification to predict soundscape perception through machine learning
(Bilkent University, 2021-06)A growing amount of literature and a series of ISO standards focus on concept, data collection, and data analysis methods of soundscapes. Yet, this field of research still lacks predictive models. We hypothesize that machine ... -
Assessment and correction of errors in DNA sequencing technologies
(Bilkent University, 2017-12)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, 2019-09)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 ... -
Detection of cardiac arrhythmia using autonomic nervous system, Gaussian mixture model and artificial neural network
(Institute of Electrical and Electronics Engineers, 2020)In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) by using state-of-the-art signal processing and machine learning methods is developed to perform the robust detection of ... -
Detection of myocardial infarction using autonomic nervous system, Gaussian mixture model and artificial neural network
(Institute of Electrical and Electronics Engineers, 2020)In this study, a new technique which detects anomalies in skin sympathetic nerve activity (SKNA) and ECG by using state-of- the-art signal processing and machine learning methods is developed to perform the robust detection ... -
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, 2017-08)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. ... -
Forecasting high-frequency excess stock returns via data analytics and machine learning
(Wiley-Blackwell Publishing Ltd., 2021-11-23)Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. ... -
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 ... -
Genetic circuits combined with machine learning provides fast responding living sensors
(Elsevier BV, 2021-04-15)Whole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and ...
