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YILDIZ, KAZIM

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Subject: Random Forest ×

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Comparison of the machine learning methods to predict wildfire areas
    (2022-09-01) YILDIZ, KAZIM; BAYAT G., YILDIZ K.
    In the last decades, global warming has changed the temperature. It caused an increasing the wildfire in everywhere. Wildfires affect people's social lives, animal lives, and countries' economies. Therefore, new prevention and control mechanisms are required for forest fires. Artificial intelligence and neural networks(NN) have been benefited from in the management of forest fires since the 1990s. Since that time, machine learning (ML) methods have been used in environmental science in various subjects. This study aims to present a performance comparison of ML algorithms applied to predict burned area size. In this paper, different ML algorithms were used to forecast fire size based on various characteristics such as temperature, wind, humidity and precipitation, using records of 512 wildfires that took place in a national park in Northern Portugal. These algorithms are Multilayer perceptron(MLP), Linear regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Stacking methods. All algorithms have been implemented on the WEKA environment. The results showed that the SVM method has the best predictive ability among all models according to the Mean Absolute Error (MAE) metric.
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    PublicationOpen Access
    Detection of autistic spectrum disorder using artificial neural network
    (2023-08-01) YILDIZ, KAZIM; ÖZDEMİR Ş. N., YILDIZ K.
    Autistic Spectrum Disorder (ASD) is a neuro-developmental disorder that is congenital or manifests with a delay in social relations and physiological development at an early age, and also causes problems in communication. It is possible to reduce the effect of the disease on individuals with early diagnosis. However, detecting ASD at an early age requires time and cost. In the studies conducted in recent years, it is seen that there is a serious increase in ASD cases. In order to prevent this increase, decision support systems should be established for early diagnosis. It is important to develop decision support models to diagnose ASD, especially for children aged 12-36 months. In this study, a model was developed that can help in detecting ASD with high accuracy for 12-36 months old children. The data set used in the created model was collected from the mobile application named ASDTests developed by Thabtah. In the estimation phase, four different machine learning algorithms which are support vector machine, Naive Bayes,Random Forest and Artificial Neural Network were used. In the classification process, high success rate was obtained with artificial neural network, random forest classifier.
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    PublicationOpen Access
    Housing price estimation with deep learning: A case study of Sakarya Turkey
    (2022-06-01) YILDIZ, KAZIM; BÜYÜKTANIR, BÜŞRA; ÖZDEMİR M., YILDIZ K., BÜYÜKTANIR B.
    Shelter is one of the most basic human needs. Besides housing needs, the housing market is also very important for investment. It is also a market where many people, such as engineers, architects, real estate agents make economic gain. When a house is bought for living in it, it is not desired to be changed for many years, and when it is bought for investment, it is a tool that requires good income. Therefore, the best decision should be made when buying a house, and it should be scrutinized. Correct estimation of house prices is very important for both buyers to make the right decision and for sellers to sell without a loss. There are many parameters for estimating house prices. In addition to variables such as the number of floors, location, and several bathrooms used in previous studies, economic factors (such as the price of bread, foreign currency price, new car price) and the housing loan interest rate of the banks were taken as inputs in this study. Sakarya province, where all parameters can be tested to make a more accurate determination, was chosen as the research area. A comparison of polynomial regression, random forest, and deep learning methods was made and it was concluded that the most accurate method was deep learning. At the same time, it was determined which parameters are more effective in house price estimation.