Yazar "Ari D." seçeneğine göre listele

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    A Genetic Programming Based Pollutant Concentration Predictor Design for Urban Pollution Monitoring Based on Multi-Sensor Electronic Nose
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ari D.; Alagoz B.B.
    An important part of air pollution control is the pollution monitoring. Since industrial spectrometers are expensive equipment, the number of observation points to monitor air pollution over an urban area can be limited. The low-cost multi-sensors network can spread over areas and form a wide-area electronic nose to estimate pollutant concentration distributions. However, the collected multisensor data should be analyzed to correctly estimate pollutant concentrations. This study demonstrates implementation of genetic programming (GP) to obtain prediction models that can estimate CO and NO2 concentrations from multisensor electronic nose data. For this purpose, to function as an electronic nose, a regression model from a training data set is obtained by using a tree-based GP algorithm. In order to improve performance of the GP based prediction models, data normalization is performed and prediction performance enhancements are demonstrated via statistical performance analyses on a test data set. © 2021 IEEE.
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    Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming
    (Institute of Electrical and Electronics Engineers Inc., 2021) Ari D.; Alagoz B.B.
    A behavioral modeling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modeling, ensemble average GP modeling performances are investigated and the results are reported for future research direction suggestions. © 2021 IEEE.