Makale KoleksiyonuArticle Collectionhttps://hdl.handle.net/20.500.12511/44202024-03-28T19:18:25Z2024-03-28T19:18:25ZDiscovering the chemical factors behind regional royal jelly differences via machine learningÖzkök, AslıKeskin, MerveTanuğur Samancı, Aslı ElifYorulmaz Önder, ElifSilahtaroğlu, Gökhanhttps://hdl.handle.net/20.500.12511/115132023-10-02T12:03:29Z2023-01-01T00:00:00ZDiscovering the chemical factors behind regional royal jelly differences via machine learning
Özkök, Aslı; Keskin, Merve; Tanuğur Samancı, Aslı Elif; Yorulmaz Önder, Elif; Silahtaroğlu, Gökhan
This study aims to discover the characteristic chemical factors for determining the region of royal jelly using machine learning. 84 samples from 13 different regions of Turkey were used for the study, and the chemical parameters of moisture, pH, acidity, and 10-hydroxy-2-decanoic acid (10-HDA) were investigated. ANOVA test was conducted to determine whether there are differences between royal jelly from 13 locations concerning the four chemical values. In addition to the statistical tests, a machine learning model was used to find out what makes royal jelly different from each other. The descriptive statistics of the chemical analysis results of royal jelly showed the following values: moisture 63.05%±2.99, pH 3.67±0.08, acidity 45.32±3.55, and 10-HDA 2.40±0.24. Surprisingly, the machine learning model suggests that 10-HDA may be the most prominent parameter for determining the region of royal jelly. This information will help us identify royal jelly’s authenticity more easily.; Bu çalışmanın amacı, makine öğrenmesi yoluyla arı sütünün bölgesini belirlemek için ayırt edici
kimyasal faktörleri keşfetmektir. Çalışmada, Türkiye'nin 13 farklı bölgesinden 84 numune kullanılmış
ve nem, pH, asitlik ve 10-hidroksi-2-dekanoik asit (10-HDA) kimyasal parametreleri incelenmiştir. 13
yerden toplanan arı sütleri arasında dört kimyasal değer açısından farklılık olup olmadığı ANOVA testi
ile incelenmiştir. İstatistiksel testlere ek olarak, arı sütlerini birbirinden neyin ayırdığını keşfetmek için
bir makine öğrenimi modeli kullanılmıştır. Arı sütü, kimyasal analiz sonuçlarının tanımlayıcı
istatistikleri sırasıyla, nem %63,05±2,99, pH 3,67±0,08, asitlik 45,32±3,55 ve 10-HDA 2,40±0,24 olarak
bulunmuştur. Şaşırtıcı bir şekilde, makine öğrenimi modeli, 10-HDA'nın arı sütünün bölgesini
belirlemek için en belirgin parametre olabileceğini öne sürmektedir. Bu bilgi, arı sütünün
doğruluğunun tespitini daha kolay öğrenmemize yardımcı olacaktır.
2023-01-01T00:00:00ZEarly prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligenceİnce, Ali TüzünSilahtaroğlu, GökhanSeven, GülserenKoçhan, KorayYıldız, KemalŞentürk, Hakanhttps://hdl.handle.net/20.500.12511/111472023-07-05T12:35:13Z2023-01-01T00:00:00ZEarly prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence
İnce, Ali Tüzün; Silahtaroğlu, Gökhan; Seven, Gülseren; Koçhan, Koray; Yıldız, Kemal; Şentürk, Hakan
Objective: To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP).Methods: Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained.Results: Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively.Conclusions: The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.
2023-01-01T00:00:00ZDetecting Turkish fake news via text mining to protect brand integrityDoğuç, Özgehttps://hdl.handle.net/20.500.12511/111222023-06-22T07:45:17Z2022-01-01T00:00:00ZDetecting Turkish fake news via text mining to protect brand integrity
Doğuç, Özge
Fake news has been in our lives as part of the media for years. With the recent spread of digital news platforms, it affects not only traditional media but also online media as well. Therefore, while companies seek to increase their own brand awareness, they should also protect their brands against fake news spread on social networks and traditional media. This study discusses a solution that accurately classifies the Turkish news published online as real and fake. For this purpose, a machine learning model is trained with tagged news. Initially, the headlines were analyzed within the scope of this study that are collected from Turkish online sources. As a next step, in addition to the headlines of these news, news contexts are also used in the analysis. Analysis are done with unigrams and bigrams. The results show 95% success for the headlines and 80% for the texts for correctly classifying the fake Turkish news articles. This is the first study in the literature that introduces an ML model that can accurately identify fake news in Turkish language.
2022-01-01T00:00:00ZAn intelligent system for document-based banking processesDoğuç, Özgehttps://hdl.handle.net/20.500.12511/111122023-06-21T08:14:56Z2022-01-01T00:00:00ZAn intelligent system for document-based banking processes
Doğuç, Özge
Business process automation has been helping companies by eliminating mundane and repetitive tasks. Automation tools have been used in many sectors, providing high full-time employee (FTE) savings and low error rates to the companies. Banks have been utilizing the automation tools for core banking and branch operations. In addition, banks receive hundreds of notice documents from courts, municipalities and third parties; which usually contain private and time-sensitive information about their customers. Automated document processing requires special solutions such as optical character recognition (OCR), natural language processing (NLP) and taxonomy; and modern process automation tools can utilize these solutions to provide end-to-end automation. This paper discusses how the notice documents such as account closure, payment blockage in Turkey can be automated. It also shows how automation can efficiently and effectively process the documents by providing experimental results for each document type.; İş süreci otomasyonu, sıradan ve tekrarlayan görevleri ortadan kaldırarak şirketlere yardımcı olmaktadır. Otomasyon araçları birçok sektörde kullanılmakta olup, şirketlere yüksek tam zamanlı çalışan (FTE) tasarrufu ve düşük hata oranları sağlamaktadır. Bankalar, ana bankacılık ve şube operasyonları için otomasyon araçlarını kullanmaktadırlar. Ayrıca her gün bankalara mahkemelerden, belediyelerden ve üçüncü şahıslardan yüzlerce ihbar belgesi gelir; ve bu belgeler genellikle müşteriler hakkında özel ve zamana duyarlı bilgiler içerir. Bu belgelerin otomatik işlenmesi prosedürü, optik karakter tanıma (OCR), doğal dil işleme (NLP) ve sınıflandırma gibi özel çözümler gerektirir; ve modern süreç otomasyon araçları, uçtan uca otomasyon sağlamak için bu çözümleri kullanabilir. Bu makale, Türkiye'de hesap kapatma, ödeme blokajı gibi bildirim belgelerinin nasıl otomatikleştirilebileceğini tartışmaktadır.
2022-01-01T00:00:00Z