WOS - Web of Science
Permanent URI for this collectionhttps://acikarsiv.thk.edu.tr/handle/123456789/2552
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Publication The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis(Hindawi Limited, 2020-04-25) Omer F. Akmese; Gul Dogan; Hakan Kor; Hasan Erbay; Emre DemirAcute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.Publication Context-dependent model for spam detection on social networks(Springer Science and Business Media LLC, 2020-08-29) Razan Ghanem; Hasan ErbayPublication Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks(Springer Science and Business Media LLC, 2020) Ahmet Haşim Yurttakal; Hasan Erbay; Gökalp Çinarer; Hatice BaşPublication 3-State Protein Secondary Structure Prediction based on SCOPe Classes(FapUNIFESP (SciELO), 2021) Sema Atasever; Nuh Azgınoglu; Hasan Erbay; Zafer AydınPublication End-To-End Computerized Diagnosis of Spondylolisthesis Using Only Lumbar X-rays(Springer Science and Business Media LLC, 2021-01-11) Fatih Varçın; Hasan Erbay; Eyüp Çetin; İhsan Çetin; Turgut KültürPublication Correction to: Novel authorship verification model for social media accounts compromised by a human(Springer Science and Business Media LLC, 2021-02-16) Suleyman Alterkavı; Hasan ErbayPublication Solar irradiation forecastby deep learning architectures(National Library of Serbia, 2022) Omer Dagistanli; Hasan Erbay; Hasim Yurttakal; Hakan KorGlobal solar irradiation data is a crucial component to measure solar energy potential when we plan, size, and design solar photovoltaic fields. Often, due to the absence of measuring equipment at meteorological stations, data for the place of interest are not available. However, solar irradiation can be estimated by ordinary meteorological data such as humidity, and air temperature. Herein we propose two different deep learning methods, one based on a deep neural network regression and the other based on multivariate long short term memory unit networks, to estimate solar irradiation at given locations. Validation criteria include mean absolute error, mean squared error, and coefficient of determination (R2 value). According to the simulation results, multivariate long short term memory unit networks performs slightly better than deep neural network. Even though both have very close R2 values, multivariate long short term memory?s R2 values are more consistent. The same is true for mean squared error and mean absolute error.Publication Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning(MDPI AG, 2022-08-26) Emre Deniz; Hasan Erbay; Mustafa CoşarThe multi-label customer reviews classification task aims to identify the different thoughts of customers about the product they are purchasing. Due to the impact of the COVID-19 pandemic, customers have become more prone to shopping online. As a consequence, the amount of text data on e-commerce is continuously increasing, which enables new studies to be carried out and important findings to be obtained with more detailed analysis. Nowadays, e-commerce customer reviews are analyzed by both researchers and sector experts, and are subject to many sentiment analysis studies. Herein, an analysis of customer reviews is carried out in order to obtain more in-depth thoughts about the product, rather than engaging in emotion-based analysis. Initially, we form a new customer reviews dataset made up of reviews by Turkish consumers in order to perform the proposed analysis. The created dataset contains more than 50,000 reviews in three different categories, and each review has multiple labels according to the comments made by the customers. Later, we applied machine learning methods employed for multi-label classification to the dataset. Finally, we compared and analyzed the results we obtained using a diverse set of statistical metrics. As a result of our experimental studies, we found the Micro Precision 0.9157, Micro Recall 0.8837, Micro F1 Score 0.8925, and Hamming Loss 0.0278 to be the most successful approaches.Publication Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images(Springer Science and Business Media LLC, 2022-04-27) Hakan Kör; Hasan Erbay; Ahmet Haşim YurttakalPublication The classification of wheat yellow rust disease based on a combination of textural and deep features(Springer Science and Business Media LLC, 2023-05-11) Tolga Hayıt; Hasan Erbay; Fatih Varçın; Fatma Hayıt; Nilüfer Akci