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Uğuz, Sezer

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Profile URL
https://hdl.handle.net/20.500.12416/8868
Name Variants
Job Title
Arş. Gör.
Email Address
sezeruguz@cankaya.edu.tr
Main Affiliation
Mekatronik Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0001-6492-2846
Scopus Author ID
57374578900
Turkish CoHE Profile ID
387036
Google Scholar ID
wK0cE0kAAAAJ
WoS Researcher ID
HQZ-3529-2023
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02269.jpeg (611.74 KB)

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SUSTAINABLE CITIES AND COMMUNITIES
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GOOD HEALTH AND WELL-BEING
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INDUSTRY, INNOVATION AND INFRASTRUCTURE
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CLEAN WATER AND SANITATION
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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QUALITY EDUCATION
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GENDER EQUALITY
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REDUCED INEQUALITIES
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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LIFE ON LAND
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AFFORDABLE AND CLEAN ENERGY
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CLIMATE ACTION
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4

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JournalCount
2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings -- 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 -- 23 November 2022 through 25 November 2022 -- Karak -- 1860221
7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA -- MAY 23-24, 2025 -- Ankara, Türkiye1
Concurrency and Computation: Practice and Experience1
EMO Bilimsel Dergi1
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Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    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.
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    Article
    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.
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    Article
    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.
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    Conference Object
    Citation - Scopus: 1
    Research Trends in Agile Software Development
    (Institute of Electrical and Electronics Engineers Inc., 2022) Tokdemir, G.; Uguz, S.
    Agile software development (ASD) is a popular research area that attracts the attention of the software development industry as well. Many studies have been conducted to explore the issues related to the ASD domain. Research is still very vigorous in this domain as there is continuous interest from companies to adopt ASD in their software development processes. Moreover, with the remote work setting that the pandemic forces, software companies search for new methods and approaches to manage their projects effectively and successfully. Hence, this study explores the research conducted in the last two decades in the ASD domain through text mining to reveal the trending topics that get attention from scholars. According to the results of this study, eight topics are identified for the research in ASD domain. Among those, topics of Team Communication, Agile Development, and Software Development Process are the most popular, the most funded, and the most cited topics respectively. © 2022 IEEE.