Browsing by Author "Erkantarci, Betul"

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Citation - Scopus: 1
Semantic-Forward Relaying for 6G: Performance Boosts With ResNet-18 and GoogleNet Plus
(Institute of Electrical and Electronics Engineers Inc., 2024) Erkantarci, Betul; Çoban, Mert Korkut; Bozoǧlu, Abdulkadir; Köse, Abdulkadir
This paper investigates the integration of advanced deep learning architectures, namely ResNet-18, GoogleNet and enhanced GoogleNet (GoogleNet Plus), into the Semantic-Forward (SF) relaying framework for cooperative communications in 6G networks. The SF relaying framework enhances transmission efficiency and robustness by leveraging semantic information at relay nodes. We analyze and compare the performance of these deep learning models in terms of validation accuracy, semantic accuracy, and Euclidean distance (ED) metrics on the CIFAR-10 dataset. Results indicate that ResNet-18 achieves the highest performance due to its residual learning architecture. GoogleNet Plus, incorporating Automatic Mixed Precision (AMP) training and the Adam optimizer, demonstrates improved stability and efficiency compared to the original GoogleNet. The results highlights the potential of deep learning models to enhance semantic processing capabilities in SF relaying, contributing to the development of more efficient, resilient, and adaptive cooperative communication systems in 6G networks. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 13
Citation - Scopus: 9
An Empirical Study of Sentiment Analysis Utilizing Machine Learning and Deep Learning Algorithms
(Springernature, 2024) Erkantarci, Betul; Bakal, Gokhan
Among text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73-76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.
A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
(IEEE, 2025) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, Abdulkadir
The steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.