PubMed İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8650
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Conference Object Citation - WoS: 2Citation - Scopus: 2Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set(Ios Press, 2019) Akcapinar Sezer, Ebru; Sever, Hayri; Par, Oznur EsraClinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the field of health care within the decision-making process. Decision support systems used for diagnosis are designed based on disease due to the complexity of diseases, symptoms, and disease-symptoms relationships. In the design and implementation of clinical decision support systems, mathematical modeling, pattern recognition and statistical analysis techniques of large databases and data mining techniques such as classification are also widely used. Classification of data is difficult in case of the small and / or imbalanced data set and this problem directly affects the classification performance. Small and/or imbalance dataset has become a major problem in data mining because classification algorithms are developed based on the assumption that the data sets are balanced and large enough. Most of the algorithms ignore or misclassify examples of the minority class, focus on the majority class. Most health data are small and imbalanced by nature. Learning from imbalanced and small data sets is an important and unsettled problem. Within the scope of the study, the publicly accessible data set, hepatitis was oversampled by distance-based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms. Considering the classification scores of four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree), optimal synthetic data generation rate is recommended.Conference Object Citation - WoS: 4Citation - Scopus: 4Ensembling Brain Regions for Brain Decoding(Ieee, 2015) Alkan, Sarper; Yarman-Vural, Fatos T.In this study, we propose a new method which ensembles the brain regions for brain decoding. The ensemble is generated by clustering the fMRI images recorded during an experimental set-up which measures the cognitive states associated to semantic categories. Initially, voxel clusters are formed by using hierarchical agglomerative clustering with correlation as the similarity metric. Then, for each voxel cluster, a support vector machine (SVM) classifier is trained to estimate the class-posteriori probabilities. Lastly, the class-posteriori probabilities are ensembled by concatenating them under the same feature space, which are then used to train a meta-layer SVM for the final classification of the cognitive states. By using the voxel clusters, we aim to utilize the distributed, but complementing nature of the semantic representations in the brain and improve the classification accuracy. Thus, we make an existential claim that the brain regions provide a natural basis for ensemble learning which should be superior to the random clusters formed over a selected set of voxels. Our approach yields to better classification accuracies in Mitchell [1] dataset on most of the subjects, when compared to state-of-the-art which emphasizes voxel selection and ensemble learning with random subspaces.Conference Object Citation - WoS: 59Citation - Scopus: 68Spectrophotometric Quantitative Determination of Cilazapril and Hydrochlorothiazide in Tablets by Chemometric Methods(Pergamon-elsevier Science Ltd, 2002) Baleanu, D; Dinç, EFour chemometric methods were applied to simultaneous determination of cilazapril and hydrochlorothiazide in tablets. Classical least-square (CLS), inverse least-square (ILS), principal component regression (PCR) and partial least-squares (PLS) methods do not need any priori graphical treatment of the overlapping spectra of two drugs in a mixture. For all chemometric calibrations a concentration set of the random mixture consisting of the two drugs in 0.1 M HCl and methanol (1:1) was prepared. The absorbance data in the UV-Vis spectra were measured for the 15 wavelength points (from 222 to 276 nm) in the spectral region 210-290 nm considering the intervals of Deltalambda = 4 nm. The calibration of the investigated methods involves only absorbance and concentration data matrices. The developed calibrations were tested for the synthetic mixtures consisting of two drugs and using the Maple V software the chemometric, calculations were performed. The results of the methods were compared each other as well as with HPLC method and a good agreement was found. (C) 2002 Elsevier Science B.V. All rights reserved.Conference Object Citation - WoS: 21Citation - Scopus: 25Effect of Electrical Discharge Machining on Dental Y-Tzp Ceramic-Resin Bonding(Elsevier Ireland Ltd, 2017) Kucukturk, Gokhan; Gurun, Hakan; Cogun, Can; Esen, Ziya; Rona, Nergiz; Yenisey, MuratPurpose: The study determined (i) the effects of electrical discharge machining (EDM) on the shear-bond strength (SBS) of the bond between luting resin and zirconia ceramic and (ii) zirconia ceramic's flexural strength with the three-point bending (TPB) test. Methods: Sixty 4.8 mm x 4.8 mm x 3.2 mm zirconia specimens were fabricated and divided into four groups (n = 15): SBG: sandblasted + silane, TSCG: tribochemical silica coated + silane, LTG: Er:YAG laser treated + silane, EDMG: EDM + silane. The specimens were then bonded to a composite block with a dual-cure resin cement and thermal cycled (6000 times) prior to SBS testing. The SBS tests were performed in a universal testing machine. The SBS values were statistically analyzed using ANOVA and Tukey's test. To determine flexural strength, sixty zirconia specimens were prepared and assigned to the same groups (n = 15) mentioned earlier. After surface treatment TPB tests were performed in a universal testing machine (ISO 6872). The flexural strength values were statistically analyzed using ANOVA and Tukey's test (a = 0.05). Results: The bond strengths for the four test groups (mean SD; MPa) were as follows: SBG (Control), 12.73 +/- 3.41, TSCG, 14.99 +/- 3.14, LTG, 7.93 +/- 2.07, EDMG, 17.05 +/- 2.71. The bond strength of the EDMG was significantly higher than those of the SBG and LTG (p < 0.01). The average flexural strength values for the groups SBG (Control), TSCG, LTG and EDMG were 809.47, 800.47, 679.19 and 695.71 MPa, respectively (p > 0.05). Conclusions: The EDM process improved the SBS. In addition, there was no significant adverse effect of EDM on the flexural strength of zirconia. (C) 2016 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.