Browsing by Subject "Mobile applications"

Now showing 1 - 4 of 4

Open Access
Adaptive ensemble learning with confidence bounds for personalized diagnosis
(AAAI Press, 2016) Tekin, Cem; Yoon, J.; Van Der Schaar, M.
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 personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and high-dimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multimodal medical data. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift.
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Empowering the use of mobile-based vocabulary notebooks
(Informascope, 2018) Zengin, Özlem; Aksu, M.
The high demand on the mobile devices like smart phones and ipads in daily life has given a rise to mobile learning trends in second or foreign language learning. The main purpose of this study was to investigate the differences in vocabulary achievement level of students keeping mobile-based and paper-based vocabulary notebooks in English language learning. The study was designed through a mixed method where a pre-post test control group quasiexperimental study was conducted. Data were collected through a vocabulary achievement test used as pre and post tests. The results indicated mobile-based vocabulary notebooks have positive effects on students’ vocabulary achievement
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Location recommendations for new businesses using check-in data
(IEEE, 2016-12) Eravci, Bahaeddin; Bulut, Neslihan; Etemoğlu, C.; Ferhatosmanoğlu, Hakan
Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.
Open Access
Zero-free-parameter modeling approach to predict the voltage of batteries of different chemistries and supercapacitors under arbitrary load
(Electrochemical Society, Inc., 2017) Özdemir, E.; Uzundal, C. B.; Ulgut, B.
Performance modeling of electrochemical energy storage systems is gathering increasingly higher attention in recent years. With the ever increasing power demand of mobile applications, predicting voltage behavior under different load profiles is of utmost importance for communications, automotive and consumer electronics. The ideal modelling approach needs not only to accurately predict the response of the battery, but also be robust, easy to implement and have low computational complexity. We will present a new algorithm that is algebraically straightforward, that has no adjustable parameters and that can accurately predict the voltage response of batteries and supercapacitors. The approach works well in a variety of discharge profiles ranging from simple long DC discharge/charge profiles to pulse schemes based on drive schedules published by regulatory bodies. Our approach is based on Electrochemical Impedance Spectroscopy measurements done on the system to be predicted. The spectrum is used in the frequency domain without any further processing to predict the fast moving portion of the voltage in the frequency domain. DC response is added in through a straightforward lookup table. This widely applicable approach can predict the voltage of with less than 1% error, without any adjustable parameters to a large variety of discharge profiles.