Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Improving Salary Offer Processes With Classification Based Machine Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024-09-21) Kaya, Rukiye; Saatci, Mehtap; Bakal, Gokhan; Bakal, Mehmet GokhanIn job applications, salary is major motivational factor for employees and making accurate salary prediction is crucial for both employers and employees. Utilizing advanced technologies can significantly enhance the accuracy and efficiency of salary prediction process. In this study, we explore Machine Learning (ML) methods to enhance salary prediction process. We evaluated seven classification models for predicting salary categories, with the Artificial Neural Network (ANN) model achieving the highest accuracy at 58.2% on the test dataset, followed by the K-Nearest Neighbors (KNN) model with an accuracy of 56.8%. Additionally, we employed ensemble models to further enhance prediction accuracy. Among these, the Majority Voting Classifier using Hard Voting achieved the highest accuracy at 59.3%, demonstrating the potential of ensemble techniques in refining salary predictions. The developed salary prediction tool estimates the most appropriate salary category for each candidate and help mitigate potential biases in manual salary assessments, hence enables a more objective and consistent compensation system. ∗CRITICAL: Do Not Use Symbols, Special Characters, or Math in Paper Title or Abstract, and do not cite other papers in the abstract. © 2024 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models(IEEE, 2025-05-23) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, AbdulkadirThe 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.