Yazar "Kirisoglu, Serdar" seçeneğine göre listele

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    Cyclical hybrid imputation technique for missing values in data sets
    (Nature Portfolio, 2025) Kotan, Kurban; Kirisoglu, Serdar
    The problem of missing data in data sets is the most important first step to be addressed in the preprocessing phase. Because incorrect imputation of missing data increases the error in the modeling phase and reduces the prediction performance of the model. When it comes to health, it is inevitable to choose models that show a higher success rate. In cases where there is missing data, the performance of machine learning models may differ depending on the amount of data contained in the data set. The presence of missing data and this high rate affects the accuracy and reliability of analysis and modeling studies because it will affect the complete amount of data in the data set. Estimating and filling in the missing data very precisely, close to its real value, will provide a significant visible performance increase in the modeling phase, which is the next stage. After imputing the missing data with an artificial intelligence model rather than a random method, it is obvious that the accuracy of the model trained with this data is higher than the model trained with data filled with classical filling methods such as mean and mode. In this study, we propose a new algorithm that has been tested on many datasets to address the problems caused by missing data imputation in the dataset. The algorithm aims to impute missing values more effectively by using row-based and column-based imputation techniques together and cyclically. The algorithm takes into account individual missing values using column-based imputation features and the overall data structure using row-based imputation features. The proposed algorithm achieved 100% accuracy with some row and column-based imputation techniques on 3 different datasets used in the study. Higher accuracy was achieved compared to other imputation techniques.
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    Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Kotan, Kurban; Kotan, Bayram; Kirisoglu, Serdar
    This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective smart, automatic crises detection and forecasting model selection application. The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made.
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    Determination of gold purity degrees using audio features with machine learning algorithms
    (Elsevier Sci Ltd, 2025) Devrim, Mehmet Osman; Kirisoglu, Serdar
    Gold purity determination is a critical part of quality control in jewellery and industrial production. Traditional methods (chemical analyses, X-ray fluorescence, etc.) can be costly, time consuming and destructive. This study aims to present an audio-based non-destructive alternative for gold purity classification. Mel-Frequency Cepstral Coefficients (MFCC) features delta-delta extracted from audio signals were analysed with 10 different machine learning algorithms (Support Vector Machines (SVM), Decision Trees, Logistic Regression, Random Forest, KNearest Neighbour (KNN), Naive Bayes, Gradient Boosting, AdaBoost, XGBoost, LightGBM). The dataset was divided into training, test and validation subsets; features were normalised with StandardScaler and the generalization performance of the models was optimised with 5-fold cross-validation. In the comparison of performance metrics (accuracy, precision, recall, F1-score), it was observed that SVM (94.58%) and Logistic Regression (93.75%) models were superior to other algorithms, especially in capturing subtle differences between classes. Confusion matrices detail the success of the model in discriminating 14, 22 and 24 carat classes. This study proves that the use of audio data in gold purity analysis has the potential for a fast, repeatable and non-destructive solution in industrial applications.
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    Sustainable Economic Development Through Crisis Detection Using AI Techniques
    (Mdpi, 2025) Kotan, Kurban; Kirisoglu, Serdar
    Economics is based on data and indicators. Although their interpretation can be complicated, their effects can be calculated in advance. In other words, economic crises are not as complicated and unpredictable as natural disasters. If economic news, news that reflects the thoughts of society, and especially the experiences and predictions of economic experts, is semantically processed from the news texts written by economic experts, economic crises can be predicted long in advance. In addition, the frequency of news about crises in society is also effective. Events that affect society are often mentioned. This can be an indication of some economic crises. In this research, we attempted to detect the economic crises and inflation increases in Turkey in December 2021 and in Germany in September 2022 several months in advance with natural language processing (NLP) models. In the study, the daily news retrieved via RSS from the leading news channels and newspapers was first preprocessed and then the similarities were checked with NLP models. Finally, similarities and changes were analyzed in comparison with inflation data. It was found that similar changes a few months ago had a high correlation with inflation data.