Browsing by Author "Uguz, Sezer"

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Citation - WoS: 2
The Effect of Population and Tourism Factors on Covid-19 Cases in Italy: Visual Data Analysis and Forecasting Approach
(Wiley, 2022) Ozyer, Baris; Ozyer, Gulsah Tumuklu; Tokdemir, Gul; Uguz, Sezer; Yaganoglu, Mete
At the beginning of 2020, the new coronavirus disease (Covid-19), a deadly viral illness, is declared as a public health emergency situation by WHO. Consequently, it is accepted as pandemic that affected millions of people worldwide. Italy is one of the most affected countries by Covid-19 disease among the world. In this article, our main goal is to investigate the effect of intensity of Covid-19 cases based on the population size and tourism factors in certain regions of Italy by visual data analysis. The regions of Lombardia, Veneto, Campania, Emilia-Romagna, Piemonte are the top five regions covering 58.50% of the total Covid-19 cases diagnosed in Italy. It has been shown by visual data analysis that population and tourism factors play an important role in the spread of Covid-19 cases in these five regions. In addition, a prediction model was created using Bi-LSTM and ARIMA algorithms to forecast the number of Covid-19 cases occurring in these five regions in order to take early action. We can conclude that these northern regions have been affected mostly by Covid-19 and the distribution of the resident population and tourist flow factors affected the number of Covid-19 cases in Italy.
Rezes Nesnelerin İnterneti Tabanlı Geri Dönüşüm Uygulama Sistemleri
(2021) Uguz, Sezer; Tokdemir, Gul
Nüfus artışı ve plansız sanayileşmenin sonucunda oluşan çevre kirliliği, insanoğlunun neden olduğu en büyük sorunlardan birisidir ve her geçen gün canlı ve cansız varlıklara olan olumsuz etkisi artarak devam etmektedir. Çevre kirliliğinin oldukça büyük bir kısmını oluşturan plastik, cam ve teneke kutu gibi geri dönüştürülmesi mümkün olan katı atıkların doğaya bırakılması sonucunda toprak ve su kirliliği meydana gelmektedir. Bu çalışmada sunulan sistem ile çevre kirliliği problemine yenilikçi bir çözüm getirilerek, geri dönüşümün akıllı bir şekilde yapılması hem ekonomik katma değer sağlayıp hem de çevre kirliliğinin önlenmesi amaçlanmaktadır. REZES (Yenilenebilir Enerji Sıfır Enerji İsrafı) sistemi, Nesnelerin İnterneti, Görüntü İşleme, Büyük Veri Analizi ve Oyunlaştırma gibi en yeni teknoloji ve metotların kullanılmasıyla akıllı bir geri dönüşüm sistemi sunmaktadır. Böylelikle plastik, cam ve teneke kutu gibi katı atıkların geri dönüştürülmesi konusuna yenilikçi bir çözüm getirilmektedir.
Topic-Aware Multi-Class Classification for Financial Complaints: Comparing BERTopic With Classical Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2025) Uguz, Sezer; Kumbasar, Mert; Tokdemir, Gul
In today's digital world, customers can utilize a variety of communication channels, such as business emails, consumer forms, feedback platforms, and dedicated complaint websites, to communicate their complaints. This study compares the performance of the supervised Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic) with traditional machine learning algorithms, including Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), K-nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost), for multi-class classification of financial customer complaints. The dataset consists of 16,715 balanced training data and 3,808 test data across five different categories, with the financial complaint data. Experimental results demonstrate that traditional machine learning models, particularly XGBoost, SVM, and LR, achieved the highest classification performance with accuracy rates close to 88%. BERTopic showed a competitive performance with an accuracy of 82.48%. The results suggest that while BERTopic offers interpretability advantages through topic modeling techniques, traditional algorithms provide higher accuracy. This study highlights the promising potential for future financial text analysis and customer complaint classification using hybrid methods, which could lead to more detailed, topic-aware classification approaches. © 2025 IEEE.