401 research outputs found
The Impact of Text Representation and Preprocessing on Author Identification
No full textAuthor identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification.Author identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification
Healty Lifestyle Behaviours of the Cardiovascular Heart Disease Patients
No full textPersil Özkan, Özlem (Arel Author), Büyükünal, Serkan Kemal (Arel Author), Şakar, Şule (Arel Author) --- Conference : 13th International Congress of Update in Cardiology and Cardiovascular Surgery March 23-26, 2017 Çeşme, Izmir-Turkey.
Application of a fuzzy expert system for failure load estimation of two serial pinned or bolted sandwich composite plates
No full textBALLI, Serkan/0000-0002-4825-139XWOS: 000373529200009The present paper reports about the application of fuzzy expert system for estimating failure loads of two serial pinned/bolted sandwich composite plates. Considered composite material for present application was produced by a glass fiber reinforced layer and aluminum sheets to create sandwich structures. Briefly outlines, the experimental data of a previous study were related to different geometrical boundary conditions and also numerous preload moments as 0 (pinned), 2, 3, 4 and 5 (bolted) Nm. Anyway, both a fuzzy expert system and a regression analysis were applied depending on mentioned geometrical parameters and pinned/bolted joint arrangements in this work. Obtained results point out that the fuzzy expert system was more suitable than regression analysis method for modeling and estimation of analyzed sandwich composite. Achievements of the fuzzy expert system and the regression analysis methods were considered in terms of error ratios and mean absolute deviations
Kardiyovasküler hastalık tanısı olan hastalarda sağlıklı yaşam biçimi davranışlarının değerlendirilmesi
No full text#nofulltext# --- Persil Özkan, Özlem (Arel Author), Büyükünal, Serkan Kemal (Arel Author), Şakar, Şule (Arel Author) --- Conference : 6. Ulusal Sağlıklı Yaşam Sempozyumu 1. Yaşam İçin Beslenme ve Spor Kongresi. İstanbul, 24-27 Mayıs 2017.
Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System
No full textBALLI, Serkan/0000-0002-4825-139XWOS: 000350946000005The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations.Mugla Sitki Kocman University, TurkeyMugla Sitki Kocman University [BAP 2013/59]The research is supported by Mugla Sitki Kocman University, Turkey, BAP 2013/59
Computing reliability indices of repairable systems via signature
No full textEryilmaz, Serkan/0000-0002-2108-1781The purpose of this paper is to show the usefulness of system signature for computing some important reliability indices of repairable systems. In particular, we obtain signature-based expressions for stationary availability, rate of occurrence of failure, and mean time to the first failure of repairable systems. Using these expressions we compute corresponding reliability indices of all systems with three and four components. Computational results are also presented for consecutive-k-within-m-out-of-n:F and m-consecutive-k-out-of-n:F systems. (C) 2013 Elsevier B.V. All rights reserved.Scientific and Technological Research Council of Turkey, TUBITAK [110T559]The author thanks the referee for his/her helpful comments and suggestions, which were useful in improving the paper. This work is partially supported by the Scientific and Technological Research Council of Turkey, TUBITAK Project No. 110T559
Performance evaluation of artificial neural networks for identification of failure modes in composite plates
No full textThe aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratio
Cosmology of hidden sector with Higgs portal
No full textThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 67-75).In this thesis, we are investigating cosmological implications of hidden sector models which involve scalar fields that do not interact with the Standard Model gauge interactions, but couple directly to the Higgs field. We particularly focus on their relic particle density as a candidate for dark matter. For the case of hidden sector without a gauge field we have improved the accuracy of the bounds on the coupling constant and give bounds on the Lagrangian parameters. Models with Abelian and non-Abelian gauge fields are also studied with relic density bounds, BBN and galactic dynamics constraints. Several discussions on phase transitions and alternative dark matter candidates are included.by Serkan Cabi.Ph.D
Analytical assessment of optimal number and placement of sensors for modal response estimation of frame buildings
No full textContinuous monitoring of the structural health of buildings can be achieved by placing acceleration sensors at various locations on the structure. Considerable variations in the dynamic response captured through these measurements can be used to identify damage due to aging or extreme load effects. Accurate estimation of modal dynamic behavior using acceleration measurements is critical for efficient structural health monitoring (SHM) of buildings under limited economic resources. In this study, an analytical approach is presented to determine theoretically the optimal number and placement of sensors in a typical five-story reinforced concrete frame building, ensuring that accuracy in modal response is not compromised. In this regard, acceleration responses at each floor level are recorded from time-history analyses of the building’s finite element model under several earthquake ground motions, simulating a complete set of SHM system measurements during seismic events. Then, considering exhaustive search optimization for sensor placement, the operational modal analysis method of Enhanced Frequency Domain Decomposition (EFDD) is applied to extract modal parameters such as the first-mode natural frequency, damping ratio, and mode shape from the simulated monitoring data. The accuracy of the estimated modal parameters from different sensor design alternatives is compared with those from the benchmark sensor configuration to determine the optimal number and locations of sensors. The findings of this research also highlight the importance of using different modal identification methods in SHM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
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