Yazar "Saritas I." seçeneğine göre listele
Listeleniyor 1 - 4 / 4
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Classification of Parkinson disease data with artificial neural networks(Institute of Physics Publishing, 2019) Yasar A.; Saritas I.; Sahman M.A.; Cinar A.C.An artificial neural network system has been developed to detect Parkinson's Disease (PD). Three samples were taken from each patient and included in the system. The importance of the study is based on the development and use of a new subject-based ANN approach that takes into account the dependent nature of the data in a replicated measure-based design. In order to evaluate the performance of the proposed system, an audio replication-based experiment was performed to differentiate healthy people from PD patients. The UCI Experiment consisted of 80 subjects, half of whom were affected by PD. Although the proposed system has a reduced number of subjects, the system is able to distinguish people with PD from an acceptable degree of healthy people with an accuracy rate of 94.93% in an artificial neural network. © Published under licence by IOP Publishing Ltd.Öğe A comparative study of ANN and FES for predicting of cutting forces and tool tip temperature in turning(2010) Ozkan I.A.; Saritas I.; Yaldiz S.In this study, fuzzy expert system (FES) and artificial neural network (ANN) models are designed for the estimation of cutting forces in turning operations. On designed models, cutting forces and experimental temperature data obtained from different cutting conditions were used in process of turning. Cutting forces at different cutting conditions and temperature values can be estimated with the help of developed models. The results obtained with these models, compared with the experimental data. The regression values were found as 0.99505 between the Experiment-FES and, 0.9888 between Experiment- ANN in the analysis. As a result, the both artificial intelligence (AI) methods have made successful modeling, but it's seen that, realized FES model has more successful results than the ANN model in the process of estimation of cutting forces. Copyright © 2010 ACM.Öğe The design of fuzzy expert system for emission parameters(2008) Saritas I.; Ciniviz M.The most important problem, which the world is face to face, is the environment pollution. The greatest reason of the pollution is the use of energy, especially the fossil fuel. Therefore the pollution of air, the soot emission which forms danger for human life and natural life and Nitrogen oxide emission that causes acid rain. Consequently decreasing emission is one of the important problems of our daily life. In this study; the effects of atomizing pressure and the alteration of CO2 portion in the air, the emission parameters, of a turbo diesel engine on nitrogen oxide and soot emission. It was researched and was completed with a fuzzy expert system method by using Mamdani type mechanism. Experiments were done for the same motor, were evaluated by comparing experimental outputs and the results of fuzzy expert system. The main object of the study is laying the groundwork for the real studies. In the correlation, which was applied between the outputs of the experiments and the outputs of the designed fuzzy expert system, the ratio of 99.87% was taken. It was also observed that the designed fuzzy expert system represented the outputs of experiments in the ratio of 98% according to the repeated two sided variance analyze.Öğe Tree-seed algorithm in solving real-life optimization problems(Institute of Physics Publishing, 2019) Sahman M.A.; Cinar A.C.; Saritas I.; Yasar A.Tree-seed algorithm (TSA) is a nature-inspired and population-based algorithm for solving continuous optimization problems. The tree-seed relationship is the main motivation of this algorithm. TSA has only two peculiar parameters which are the total number of trees in the stand (pop) and the controller of the seed production (search tendency). Although many problems have been solved in the literature by TSA which is the successful optimizer for low dimensional unconstrained continuous problems, real-life problems have not been addressed yet. In this study, six continuous unconstrained real-life optimization problems (gas transmission compressor design, optimal capacity of gas production facility, gear train design, frequency modulation sounds parameter identification, the spread spectrum radar polyphase code design with 10 decision variables, the spread spectrum radar polyphase code design with 20 decision variables) have been solved. It is seen that choosing the number of the population as 50 and the value of search tendency as 0.1 is appropriate according to experimental results for these problems. © Published under licence by IOP Publishing Ltd.
