Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Machine Learning-Based Weather Prediction With Radiosonde Observations(JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2024-07-16) Gogen, Eralp; Guney, SeldaFrom the past to the present, weather forecasting holds significant importance for humanity. The precise execution of weather forecasting enables the implementation of precautions against natural disasters such as floods, tsunamis, etc., thereby minimizing the adverse effects that may arise. In this study, weather prediction is conducted using Radiosonde data. Within this prediction, estimations for both the highest and lowest temperatures are made employing machine learning algorithms. Unlike previous temperature prediction studies in the literature, a three-year dataset of Radiosonde observations is utilized. This dataset, measured at intervals of 1mbar up to an altitude of 40 km from the ground, allows for a more accurate modeling of the atmosphere compared to other studies in the literature. In this model, predictions for the highest and lowest temperatures for the next day are made. In this stage, the effects of normalization, feature extraction, or selection on the results are analyzed, and the most suitable model for prediction is determined. The software, implemented in the MATLAB environment, compares different regression methods. As a result of these analyses, utilizing the Gaussian Process Regression (GPR) method, the highest temperature prediction for the next day is achieved with the highest accuracy, with a mean square root deviation of 1.2. Using the same method, the lowest temperature prediction is made with a mean square root deviation ratio of 2.4. The results indicate more successful temperature predictions compared to studies in the literature.Item Study of Fish Species Discrimination Via Electronic Nose(2015) Guney, Selda; Atasoy, Ayten; 0000-0003-1188-2902; 0000-0002-0573-1326; HJH-3630-2023; AAC-7404-2020Fish freshness is a critical issue in determining fish quality. Since fish freshness changes according to the fish species, fish species has to be identified before examining the freshness. So far, fish species have been distinguished through different methods such as image processing. In this paper, an electronic nose has been used to distinguish between different species of fish. Thus, both freshness and species of fish will be determined just using a single, low cost device. The aim of this study is to distinguish between three different species of fish - horse mackerel, anchovy and whiting - by using an electronic nose composed of 8 different metal oxide gas sensors. In order to distinguish between the species of fish, a whole new method, which is not applied to this kind of data previously, is used and proposed for use in the pattern recognition unit of the electronic nose. It is examined in three parts such as signal pre-processing, feature extraction and classification. In the pre-processing stage, to reduce the negative effect of sensor drift, a new method is applied to the raw signal in addition to the well-known baseline manipulation method. In the feature extraction part, the sub-sampling method which is not frequently used is applied to the pre-processed signal. The extracted features are used in the classification part. The structure of the proposed classification algorithm is based on binary decision tree structure. The binary decision tree structure is composed of nodes. In every node of the decision tree structure, the feature spaces or classification algorithm can be changed according to the problem. Classification results demonstrate the effectiveness of the presented models. The overall accuracy of the identification of fish species achieved with the proposed methods is 96.18%. The performance of the proposed method is also compared to conventional methods such as Naive Bayes, k-Nearest Neighbor and Linear Discriminant Analysis. The successes of these classifiers are 84.73, 80 and 82.4, respectively. (C) 2015 Elsevier B.V. All rights reserved.Item Discrimination of Different Fish Species by E-nose(2015) Guney, Selda; Atasoy, Ayten; 0000-0003-1188-2902; 0000-0002-0573-1326; HJH-3630-2023; AAC-7404-2020The aim of this study is to distinguish three different types of fish which are anchovy, horse mackerel and whiting by an electronic nose. Generally, the electronic noses are composed of three units. These are sensor unit which has 8 metal oxide sensors in this study, an electronic unit and a pattern recognition unit. In the pattern recognition unit, signal preprocessing, feature extraction and classification stages are performed. For distinguishing different fish species, different feature extraction methods and classification methods are compared with each other. Then the best combination of feature extraction and classification method is selected and applied to the fish database.Item Fish Freshness Testing with Artificial Neural Networks(2015) Atasoy, Ayten; Ozsandikcioglu, Umit; Guney, Selda; 0000-0002-0573-1326; AAC-7404-2020In this work, with the use of an electronic nose which has 8 metal oxide gas sensors and was set up at Karadeniz Technical University, a fish freshness system was designed. There are 7 classes (1, 3, 5, 7, 9, 11, 13 day for fish storage) for classification and to perform classification process, Artificial Neural Networks was used in this work. To increase the classification success, Artificial Neural Network architecture, activation functions and input data obtained from different feature extraction method was changed, the storage condition is very important factor for fish freshness and fishes used in this study were stored at fish market conditions. In this study to determine the classification success, 5-Fold Cross Validation method was used and the maximum success rate was obtained as 98.94 %.Item Calibration Transfer Between E-Noses(2014) Guney, Selda; Fernandez, Luis; Marco, Santiago; https://orcid.org/0000-0002-0573-1326; AAC-7404-2020Electronic 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.Item Investigation of Harmonic Estimation Using Imperalist Competitive Algorithm Method(2015) Ertugrul, Emre; Guney, Selda; 0000-0002-0573-1326; AAC-7404-2020Recently, 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.Item 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-7404-2020In 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.Item Classification of Human Movements by Using Kinect Sensor(2023) Acis, Busra; Guney, Selda; https://orcid.org/0000-0001-6683-0005; https://orcid.org/0000-0002-0573-1326; HDM-2942-2022In recent years, studies have been carried out to classify human movements in many areas such as health and safety. To classify human movements, image processing methods have also started to be used in recent years. With the help of learning-based algorithms, human posture can be defined in the images obtained by various imaging methods. The predecessor methods of these classification algorithms are machine learning and deep learning. In addition, in recent years, the use of sensors that can detect human joints in perceiving human posture has also increased. The Kinect sensor, developed by Microsoft, is one of the most frequently used sensors because it is not wearable and can detect joints with infrared rays and transfer this information directly to the computer via USB connection. This study used a dataset called CAD60 that included real-time human posture information and images obtained using a Microsoft Kinect sensor, which is available in the literature. This dataset contains data that includes different movements/postures of different people. Within the scope of this study, the performances of these algorithms were obtained by using classification algorithms with the MATLAB program and these performances were compared. The classification algorithms have been used to try to improve the results by using different architectures. When raw data is used, classification accuracy is obtained as 72.60% with one of the machine learning methods, the Cosine K-Nearest Neighbor method. With the feature selection method, this success value has been increased to 74.18%. In addition, when classified by the Support Vector Machines method after the feature extraction process using the Long Short Term Memory method from the deep network architectures, which is the method proposed in this study, the accuracy rate was increased to 98.95%. The best method of classifying human posture was investigated by using different methods and a method was proposed by comparing it with the literature.Item Heart Disease Prediction by Using Machine Learning Algorithms(2020) Erdogan, Alperen; Guney, SeldaNowadays, 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.Item Comparison of Expectation Maximization and Naive Bayes Algorithms in Character Recognition(2016) Guney, Selda; Cakar, Ceyhun; 0000-0002-0573-1326; AAC-7404-2020Statistical 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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