Ahmetoğlu Taşdemir, Funda

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https://hdl.handle.net/20.500.14365/7292
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Email Address
funda.tasdemir@ieu.edu.tr
Main Affiliation
05.09. Industrial Engineering
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Former Staff
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ORCID ID
0000-0003-2952-3174
Scopus Author ID
57262199900
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WoS Researcher ID
GEQ-4966-2022

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  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Fuzzy Time Series and Grey Theory Forecasting for the Sales of Cleaning Products
    (Springer Science and Business Media Deutschland GmbH, 2022) Ahmetoğlu Taşdemir, Funda; Seker S.
    The Covid-19 pandemic has brought too many concerns in businesses whether they will survive or fail. This is what makes forecasting crucial since it guides businesses in order to make appropriate decisions. In a pandemic environment, forecasting is critical because there is little historical data available. Taking this into consideration, in this study, fuzzy time series (FTS) and grey model (GM), which do not require long past time series data are implemented for short term forecasting of a product sold on a dedicated social media account. After adopting monthly sales data of the related product, accuracy of fuzzy time series and grey model are improved with parameter optimization. According to the results of absolute percentage error (APE), both methods demonstrate superior forecasting accuracies when the product shows an increasing sales trend. This study can also be a reference for businesses that are positively affected by the pandemic due to increased sales and willing to act on the opportunities it has already emerged. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Conference Object
    Improving and Assessing the Prediction Capability of Machine Learning Algorithms for Breast Cancer Diagnosis
    (Springer International Publishing Ag, 2022) Ahmetoğlu Taşdemir, Funda
    Currently, one of the most common forms of cancer is breast cancer. In 2020, breast cancer caused 2.3 million cases and approximately 685,000 deaths worldwide. Since breast cancer is the second leading cause of death among women, it is very important to detect whether a biopsy cell is benign or malignant at an early stage so that it is not fatal. However, the breast cancer diagnosis process is quite complex as it consists of several stages, such as collecting and analyzing multivariate samples. These time demanding procedures delay diagnosis and pose a risk for people. On the other hand, the rapid development of Machine Learning (ML) and its applications in healthcare are bringing a new perspective to process and analyze medical big data. In addition, ML techniques help medical experts by analyzing the data in a short time and reduce time pressure on decision making procedures. Taking those into consideration in this study, different ML algorithms are employed for predicting if a cell nucleus is benign or malignant using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The ML algorithms utilized in this paper are: Support Vector Machines (SVM), Logistic Regression (LR) and Random Forest (RF). Dataset includes 32 attributes with 569 cases consisting of 357 benign and 212 malignant. To improve the accuracy of the results, hyperparameter tuning was done using Grid Search and results are compared. The simulation of algorithms is done by Python Programming language.
  • Thumbnail Image
    Article
    Citation - Scopus: 1
    Machine Learning Sales Forecasting for Food Supplements in Pandemic Era
    (Huaxi University Town, Editorial Department of JRACR, 2022) Ahmetoğlu Taşdemir, Funda
    The Covid-19 pandemic has brought a lot of concerns about the operational and financial situation of businesses. Forecasting is crucial as it guides businesses through these critical points. Forecasting has become even more critical in the pandemic environment and therefore the necessity of using an accurate forecasting method has increased. Taking this into consideration, in this study, intelligent machine learning methods, namely; Grey Model (GM), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to make a short-term prediction of a food supplement, a product whose demand increased with the pandemic situation. Eighty-five percent of the historical data is used for training purposes and fifteen percent of the data is used for measuring accuracy. The accuracy of the models employed is improved with parameter optimization The accuracy performance indicator Mean Absolute Percentage Error (MAPE) showed that all methods give superior results when the historical data has an increasing sales trend. This study presents an important consideration for businesses and has a potential to be generalized for a business whose sales have an increasing trend not only because of the pandemic but also for any reason. Copyright © 2022 by the authors.