Browsing by Subject "Statistical tests"
Now showing 1 - 11 of 11
Results Per Page
Sort Options
Item Open Access Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation(Springer, 2015) 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.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 is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.Item Open Access Category-selective top-down modulation in the fusiform face area of the human brain during visual search(IEEE, 2017) Dar, Salman Ul Hassan; Çukur, TolgaSeveral regions in the ventral-temporal cortex of the human brain are thought to have representations of specific categories of objects. Furthermore, a distributed network of frontal and parietal brain regions is implicated in attentional control. It is assumed that during visual search, attention-control regions send top-down signals to the target category-selective areas to bias the processing in favour of the attended object category. However, little is known about such causal interactions during naturalistic visual search. Here we assess the influence of attention-control brain regions on a well-known face selective area fusiform face area (FFA) during natural visual search using Granger causality analysis. Our results indicate that attending to humans enhances the influence of attention-control regions on the fusiform face area.Item Open Access Data imputation through the identification of local anomalies(Institute of Electrical and Electronics Engineers Inc., 2015) Ozkan, H.; Pelvan, O. S.; Kozat, S. S.We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose: 1) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and 2) a maximum a posteriori estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions. Our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted (clean) reference data set. Once each part is labeled as anomalous versus normal, the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed. Under a certain conditional independency structure assumed for the binary patterns, we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning. The proposed framework is tested over several well-known machine learning data sets with synthetically generated corruptions and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities. Our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions. © 2015 IEEE.Item Open Access Fizik tedavi egzersizlerinin giyilebilir hareket algılayıcıları işaretlerinden dinamik zaman bükmesiyle sezimi ve değerlendirilmesi(IEEE, 2014-04) Yurtman, Aras; Barshan, BillurGiyilebilir hareket algılayıcılarından kaydedilen sinyalleri işleyerek fizik tedavi egzersizlerini algılamak ve değerlendirmek için özerk bir sistem geliştirilmiştir. Bir fizik tedavi seansındaki bir ya da birden fazla egzersiz tipini algılamak için, temeli dinamik zaman bükmesi (DZB) benzeşmezlik ölçütüne dayanan bir algoritma geliştirilmiştir. Algoritma, egzersizlerin doğru ya da yanlış yapıldığını değerlendirmekte ve varsa hata türünü saptamaktadır. Algoritmanın başarımını degerlendirmek için, beş katılımcı tarafından yapılan sekiz egzersiz hareketinin üç yürütüm türü için birer şablon ve 10’ar sınama yürütümünden oluşan bir veri kümesi kaydedilmiştir. Dolayısıyla, eğitim ve sınama kümelerinde sırasıyla 120 ve 1,200 egzersiz yürütümü bulunmaktadır. Sınama kümesi, boş zaman dilimleri de içermektedir. Öne sürülen algoritma, sınama kümesindeki 1,200 yürütümün % 8.58’ini kaçırmakta ve boş zaman dilimlerinin % 4.91’ini yanlış sezim olarak değerlendirerek toplam 1,125 yürütüm algılamaktadır. Doğruluk, sadece egzersiz sınıflandırması ele alındığında ˘ % 93.46, hem egzersiz hem de yürütüm türü sınıflandırması içinse % 88.65’tir. Sistemin bilinmeyen egzersizlere karşı davranışını sınamak için, algoritma, her egzersiz için, o egzersizin şablonları dışarıda bırakılarak çalıştırılmış ve 1,200 egzersizin sadece 10’u yanlış sezilmiştir. Bu sonuç, sistemin bilinmeyen hareketlere karşı gürbüz olduğunu göstermektedir. Öne sürülen sistem, hem bir fizik tedavi seansının yoğunluğunu kestirmek, hem de hastaya ve fizik tedavi uzmanına geribildirim vermek amacıyla egzersiz hareketlerini değerlendirmek için kullanılabilir.Item Open Access GCap: Graph-based automatic image captioning(IEEE, 2004) Pan J.-Y.; Yang H.-J.; Faloutsos C.; Duygulu, PınarGiven an image, how do we automatically assign keywords to it? In this paper, we propose a novel, graph-based approach (GCap) which outperforms previously reported methods for automatic image captioning. Moreover, it is fast and scales well, with its training and testing time linear to the data set size. We report auto-captioning experiments on the "standard" Corel image database of 680 MBytes, where GCap outperforms recent, successful auto-captioning methods by up to 10 percentage points in captioning accuracy (50% relative improvement). © 2004 IEEE.Item Open Access Histogram of oriented rectangles: a new pose descriptor for human action recognition(Elsevier BV, 2009-09-02) İkizler, N.; Duygulu, P.Most of the approaches to human action recognition tend to form complex models which require lots of parameter estimation and computation time. In this study, we show that, human actions can be simply represented by pose without dealing with the complex representation of dynamics. Based on this idea, we propose a novel pose descriptor which we name as Histogram-of-Oriented-Rectangles (HOR) for representing and recognizing human actions in videos. We represent each human pose in an action sequence by oriented rectangular patches extracted over the human silhouette. We then form spatial oriented histograms to represent the distribution of these rectangular patches. We make use of several matching strategies to carry the information from the spatial domain described by the HOR descriptor to temporal domain. These are (i) nearest neighbor classification, which recognizes the actions by matching the descriptors of each frame, (ii) global histogramming, which extends the idea of Motion Energy Image proposed by Bobick and Davis to rectangular patches, (iii) a classifier-based approach using Support Vector Machines, and (iv) adaptation of Dynamic Time Warping on the temporal representation of the HOR descriptor. For the cases when pose descriptor is not sufficiently strong alone, such as to differentiate actions "jogging" and "running", we also incorporate a simple velocity descriptor as a prior to the pose based classification step. We test our system with different configurations and experiment on two commonly used action datasets: the Weizmann dataset and the KTH dataset. Results show that our method is superior to other methods on Weizmann dataset with a perfect accuracy rate of 100%, and is comparable to the other methods on KTH dataset with a very high success rate close to 90%. These results prove that with a simple and compact representation, we can achieve robust recognition of human actions, compared to complex representations. © 2009 Elsevier B.V. All rights reserved.Item Open Access On recognizing actions in still images via multiple features(Springer, Berlin, Heidelberg, 2012) Şener, Fadime; Bas, C.; Ikizler-Cinbis, N.We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features. © 2012 Springer-Verlag.Item Open Access Proof-of-concept energy-efficient and real-time hemodynamic feature extraction from bioimpedance signals using a mixed-signal field programmable analog array(IEEE, 2017) Töreyin, Hakan; Shah, S.; Hersek, S.; İnan, O. T.; Hasler, J.We present a mixed-signal system for extracting hemodynamic parameters in real-time from noisy electrical bioimpedance (EBI) measurements in an energy-efficient manner. The proof-of-concept system consists of floating-gate-based analog signal processing (ASP) electronics implemented on a field programmable analog array (FPAA) chip interfaced with an on-chip low-power microcontroller. Physiological features important for calculating hemodynamic parameters (e.g., heart rate, blood volume, and blood flow) are extracted using the custom signal processing circuitry, which consumes a total power of 209 nW. Testing of the signal processing circuitry has been performed using ∼580 sec of an impedance plethysmography dataset collected from the knee of a subject using a custom analog EBI front-end. Results show the similarities of variations in heart rate, blood volume, and blood flow calculated using features extracted by the ASP circuitry implemented on an FPAA and a MATLAB digital signal processing algorithm.Item Open Access Query Processing in Context - Oriented Retrieval of Information(1998) Saygin, A.P.; Yavuz, T.This paper proposes a context-oriented approach towards document retrieval and presents the query processing component of the retrieval process in detail. Our approach differs from the existing ones by its use of word sense disambiguation in the first stages and by its employment of query structuring. The proposed system, called CORE, attempts to match contexts of documents and queries rather than regarding these as sets of words. Clarifying the indicated meaning of the words in the query before the search is conducted will eliminate a large number of irrelevant documents and will enable the construction of a context for the query. Query structuring allows the preparation of a query execution plan for an efficient search and thus yields response time improvement. In addition, it positively affects Recall and Precision by considering the relative importance of the words in the query.Item Open Access A simulation-based support tool for data-driven decision making: operational testing for dependence modeling(IEEE, 2014) Biller, B.; Akçay, Alp; Çorlu, C.; Tayur, S.Dependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling.Item Open Access Statistical analysis by wavelet leaders reveals differences in multi-fractal characteristics of stock price and return series in Turkish high frequency data(World Scientific Publishing Co. Pte. Ltd., 2023-12-08) Lahmiri, Salim; Şensoy, Ahmet; Akyıldırım, ErdinçThe price and return time series are two distinct features of any financial asset. Hence, exam-ining the evolution of multiscale characteristics of price and returns sequential data in timedomain would be helpful in gaining a better understanding of the dynamical evolution mecha-nism of the financial asset as a complex system. In fact, this is important to understand theirrespective dynamics and to design their appropriate predictive models. The main purpose ofthe current work is to investigate the multiscale fractals of price and return high frequency datain Turkish stock market. In this regard, the wavelet leaders computational method is appliedto each high frequency data to reveal its multi-fractal behavior. In particular, the method isapplied to a large set of Turkish stocks and statistical results are performed to check for (i)presence of multi-fractals in price and returnseries and (ii) differences between prices andreturns in terms of multi-fractals. Our statistical results show strong evidence that high fre-quency price and return data exhibit multi-fractal dynamics. In addition, they show evidenceof distinct fractal characteristics on different scales between price and return series. Further-more, our statistical results show evidence of differences in local fluctuation characteristics ofprice and return time series. Therefore, differences in local characteristics are useful to buildspecific predictive models for each type of data for better modeling and prediction to generateprofits. Besides, we found evidence that both long-range correlations and fat-tail distributionscontribute to the multifractality in Turkish stocks. This finding can be attributed to the majorrole played by international investors in increasing the volatility of Turkish stocks.