Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Guney, Seldasearch.filters.applied.f.language: search.filters.language.tur

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    Calibration Transfer Between E-Noses
    (2014) Guney, Selda; Fernandez, Luis; Marco, Santiago; https://orcid.org/0000-0002-0573-1326; AAC-7404-2020
    Electronic nose is an instrument which is composed of gas sensor array and pattern recognition unit. It is generally used for classifying, identifying or quantifying the odors or volatile organic components for these commonly used devices, calibration transfer is an important issue because of differences in each instrument, sensor drift, changes in environmental conditions or background changes. Calibration transfer is a transfer of model between different instruments which have different conditions. In this study, calibration transfer is applied to the e-noses which have different temperature conditions. Also the results of the direct standardization, piecewise direct standardization and orthogonal signal correction which are different calibration methods were compared. The results of the piecewise direct standardization method are more successful than the other methods for the dataset which is used in this study.
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    Investigation of Harmonic Estimation Using Imperalist Competitive Algorithm Method
    (2015) Ertugrul, Emre; Guney, Selda; 0000-0002-0573-1326; AAC-7404-2020
    Recently, some new methods and algorithms have been started to use as an alternative for harmonic estimation instead of Fourier Transform based traditional algorithms. Because, it is inevitable to look for alternative solutions for harmonic estimation problems due to the existing limitations of Fourier Transform based algorithms. The Imperialist Competitive Algorithm (ICA) investigated in this study is a social based intuitive optimization algorithm. The advantages of the evolutionary approximations selected by the nature have been suggested in genetic algorithms and its derivations in order to be useful in optimization area. The animal behaviors have been concluded as partial swarm and ant colony optimization algorithms. Recently, the animal behaviors simulate the human social behaviors and guide us to solve some engineering problems.
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    Classification of Different Objects with Artificial Neural Networks Using Electronic Nose
    (2015) Ozsandikcioglu, Umit; Atasoy, Ayten; Guney, Selda; 0000-0002-5397-6301; 0000-0003-1188-2902; 0000-0002-0573-1326; AAR-4368-2020; HJH-3630-2023; AAC-7404-2020
    In this paper; an e-nose with low cost which consisting of 8 different gas sensors was used and with this e-nose 9 different odors ((mint, lemon, egg, rotten egg, angelica root, nail polish, naphthalene, rose water, and acetone) was classified. This 9 different odor are classified with Artificial Neural Networks and by using different activation functions, and then the successes of the classification were compared with each other. The maximum success of the testing data is obtained with 100% accuracy rate by using logsig activation function in hidden layer and tansig activation function in output layer. In conclusion; using the chemical database containing the odor of the different objects, distinct odors were shown to be classified correctly.
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    Heart Disease Prediction by Using Machine Learning Algorithms
    (2020) Erdogan, Alperen; Guney, Selda
    Nowadays, one of the most important illness is heart disease which cause of mostly patients dead. Medical diagnosis of heart diseases is very difficult. While heart diseases are diagnosed medically, they can be confused with other diseases that show same symptoms such as chest pain, shortness of breath, palpitations and nausea. This makes it difficult to diagnose heart diseases medically. In this study, the presence of heart diseases was determined by using machine learning algorithms. In this study, the data obtained from the patients were weighted according to their effects on the success rate. In this study, a method is proposed for determine weight coefficient. According to proposed method's results, 86,90% success was achieved with 13 different features obtained from the patients.
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    Comparison of Expectation Maximization and Naive Bayes Algorithms in Character Recognition
    (2016) Guney, Selda; Cakar, Ceyhun; 0000-0002-0573-1326; AAC-7404-2020
    Statistical character recognition methods are used very common nowadays in the character recognition. A certain number of features are extracted from characters recognized for the recognition of the character. The classification is performed with the recognition of these features. Extracted features can be considered as input signal of a prediction system. Thus proposed methods for estimation can be used for the recognition. In this study, digit characters are classified with expectation maximization and Naive Bayes methods over Gaussian mixture models. These two methods are compared with each other.
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    The Comparison of Estimation Algorithms for Mobile Robot Navigation
    (2016) Guney, Selda; Bilen, Murat; 0000-0002-0573-1326; AAC-7404-2020
    In this study, a robot with different maneuvras is followed with different estimation algorithms. The mobile robot has acted first linear, then maneuver and finally linear again. It's speed is constant through the way. Standard Kalman Filter, Adaptive Kalman Filter, Extended Kalman Filter and Interacting Multiple Model consist of multiple model Kalman Filter combined of linear and non-linear model are used to follow the act of the robot. The results of these estimations are compared with each other. Multiple model Kalman Filter is the best estimation algorithm among them for this motion model.