Meslek Yüksek Okulları / Vocational Schools
Permanent URI for this communityhttps://hdl.handle.net/11727/1410
Browse
17 results
Search Results
Item Brain Tumor Prediction with Deep Learning and Tumor Volume Calculation(2021) Karayegen, Gokay; Aksahin, Mehmet FeyziItem Deep Learning Based Multi Modal Approach for Pathological Sounds Classification(2020) Ankishan, Haydar; Kocoglu, ArifAutomatic detection of voice disorders is very important because it makes the diagnosis process simpler, cheaper and less time consuming. In the literature, there are many studies available on the analysis of voice disorders based on the characteristics of the voice and subdividing the result of this analysis. In general, these studies have been carried out in order to subdivide the sound into pathological - normally sub - groups by means of certain classifiers as a result of subtraction of the features on frequency, time or hybrid axis. In contrast to existing approaches, in this study, a multiple- deep learning model using feature level fusion is proposed to distinguish pathological-normal sounds from each other. First, a feature vector (HOV) on the hybrid axis was obtained from the raw sound data. Then two CNN models were used. The first model has used raw audio data and the second model has used HOV as an input. Feature data in both model SoftMax layers were obtained as a matrix, and canonical correlation analysis (Canonical Correlation Analysis (CCA) was applied at feature level fusion. The new obtained feature vector was used as an input for multiple support vector machines (M-SVMs), Decision Tree (DTC) and naive bayes (NBC) classifiers. When the experimental results are examined, it is seen that the new multi-model based deep learning architecture provides superior success in classifying pathological sound data. With the results of the study, it will be possible to automatically detect and classify the pathology of these patients according to the proposed system.Item An Approach to the Classification of Environmental Sounds by LSTM Based Transfer Learning Method(2020) Ankishan, HaydarThis electronic Effective frequency extraction from acoustic environmental sounds in frequency and time axis increases the importance of voice recognition, sound detection, environmental classification in recently. For this purpose, there are many studies in the literature on the discrimination of acoustic environmental sounds. These studies generally perform these operations with the help of machine learning and deep learning algorithms. In this study, a new artificial intelligence architecture using two long short term memory networks (LSTM) is designed. The structure, which uses both raw data and the proposed feature vector at its inputs, is reinforced by the transfer learning approach. The obtained classification results were fused at the decision level. As a result of experimental studies, five different environmental acoustic sounds were subdivided into 97.15% test accuracy. In environmental studies conducted in pairs, it is seen that the environmental sounds have reached 100% accuracy. Experimental results have shown that the proposed artificial intelligence architecture with fusion support at decision level is capable of discriminating acoustic environmental sounds.Item A New Approach for Discriminating the Acoustic Signals: Largest Area Parameter (LAP)(2018) Ankishan, Haydar; Inam, S. CagdasFeature extraction of sound signals is essential for the performance of applications such as pattern and voice recognition etc. In this study, a method based on a novel feature is proposed to separate pathological human voice signals from healthy ones as well as to separate subgroups of pathological voices from each other. The voices are examined in time-frequency domain. Their differences obtained from the results of the proposed method are investigated and the mechanism of the method is demonstrated using experimental cases. It is concluded that the method succeeds to discriminate the voices marked "healthy" and "pathological".Item Status of the Albanian Muslims in Greece During the Population Exchange on the Basis of the Minutes of Lausanne Conference and the Reports of the Embassy of Albania in Athens(2017) Ozcan, HalilThe Subcommittee of Population Exchange at Lausanne Conference addressed the exchange topic in twelve sessions between January, 11, 1923 and January 30, 1923. The convention and protocol concerning the exchange between populations of two countries were signed on January 30, 1923 by the Turkish and Greek Governments. Accordingly, it was decided to conduct compulsory population exchange between the "Turkish nationals of Greek Orthodox religion" who had domiciled in Turkish territories and the "Greek nationals of Muslim religion" who had domiciled in Greek territories. Despite Greece provided all assurances at Lausanne Conference and afterwards, it took action to exchange the Albanian Muslims, who lived in Epirus-Chameria region where almost all of the population was Muslim with the immigrant Greeks who came from Anatolia, due to the fact that the Albanian Muslims were "Greek nationals with Muslim religion." During this period, Mid'hat Frasheri was assigned to the Embassy of Albania in Athens on January 1923. The Ambassador discussed the situation of the Albanian Muslims in Greece, with the authorities, the League of Nations, the Mixed Exchange Committee, the Turkish authorities and mainly the Greek authorities. Mid'hat Frashri sent approximately 200 reports, which included these discussions to the Albanian Ministry of Foreign Affairs.Item Home Application by Hemodialysis Patients for Hypertension Management(2017) Gokdogan, Feray; Kes, Duygu; Turgay, Gulay; Tuna, DonduOBJECTIVE: This study aimed to determine home application by hemodialysis patients for hypertension management. MATERIAL and METHODS: The population of the descriptive study included a total of 279 patients who were treated at the hemodialysis centers of two state hospitals, one private hospital and one training and research hospital situated within Karabuk province. A total of 120 patients who were over 18 years of age, had hypertension, could communicate, and whose clinic state were stable, who did not have any mental and psychiatric disorder and who accepted to participate in the research voluntarily were included in the sample. RESULTS: It was determined that 59.2% of the patients who participated in the study did not measure their blood pressures at home regularly; 44.6% did not take their medication regularly and did not know the name and dosages of their medication (60.7% and 64.3% respectively); 73.2% had stopped taking medication without the physician's knowledge; 85% used salt in meals; and 70.8% and 46.7% respectively did not comply with the liquid limitation. CONCLUSION: It is important to reveal the effects of a nursing care approach for supporting hypertension self-management at home of our patients based on their individual characteristics through studies focusing on regular training, monitoring and providing consultancy services.Item Max-Min Space Approach for Acoustic Signal Analysis(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020Acoustic signals having pathological problem are difficult to discriminate from each other. Despite the presence of many features, the difficulties arise from the chaotic and nonlinear nature of these voices. Unlike the existing features, a new feature and feature space are emphasized in this study. Considering the maximum and minimum values of acoustic signals at certain time intervals, the relation between them is revealed and Max-Min space is created. Experimental studies have shown that the space distribution between pathological and normal sounds is completely separated from each other and that the space-scattering field sizes are different from each other. As a result of the studies, a time-based feature is introduced which allows the separation of chaotic and nonlinear acoustic signals in the literature.Item A New Approach for Estimation of Heart Beat Rates from Speech Recordings(2017) Ankishan, Haydar; Baysal, Ugur; 0000-0002-6240-2545; AAH-4421-2019; AAJ-5711-2020Today, people are able to have information about their mental state, behavior, and health status in some issues from the features of the voices. The study involves calculating the heart rates of people using nonlinear equations with the help of the features of sound recordings. The model proposed for the study consists of the four inputs of the difference equation parameters which change with constant and variable sound features. When the experimental studies were examined, it was observed that the heart rate could be predicted with an accuracy of 89.76% by using 10s sound recordings. With the proposed equation, it is observed that the heart beat rate is related to the speech features, can be calculated these features with minimal error rate and also the nonlinear equation is presented in the literature.Item Elevator Parking Approach in Nearest Car Method(2018) Ciflikli, Cebrail; Tartan, Emre OnerA fundamental factor that determines the system efficiency and the quality of service in elevator group control systems is the used elevator dispatching algorithm. Along with the elevator dispatching algorithm, using an elevator parking algorithm can provide improvements in the performance of an elevator group control system. In this study considering a system that uses Nearest Car Method as the elevator dispatching algorithm, average passenger waiting time is investigated under different traffic conditions using three parking algorithms and when no parking algorithm is used. For a more efficient elevator control system an adaptive park algorithm which is changing according to varying traffic conditions is proposed.Item Cytomegalovirus (CMV) Infection Induces an Angiogenic Response through Hepatic Stellate Cells (HSCs) and Leads to Early Post-Transplant Liver Fibrosis (LF) and Poor Graft Survival.(2019) Ozdemir, B.H.; Ozgun, G.; Soy, E.H. Ayvazoglu; Haberal, N.; Moray, G.; Haberal, M.; 0000-0002-0993-9917; AAC-5566-2019