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Copyright of Afyon Kocatepe University Journal of Social Sciences / Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi is the property of Afyon Kocatepe University (AKU) Sosyal Bilimler Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anatolian Journal of Educational Leadership & Instruction / Anadolu Eğitim Liderliği ve Öğretim Dergisi is the property of Anatolian Journal of Educational Leadership & Instruction and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of its development, intelligent manufacturing often focuses on production flexibility, client customization, and quality control, all of which are required for the manufacture of powder-based metallurgy. In particular, the identification and categorization of defects are crucial steps in the production processes involved in powder-based metallurgy. Intelligent strategies to detect faults in metal parts continue to be a challenge in automated industrial production lines. These techniques have been a particular concern for microscopic metal component producers for a long time. Due to its precision and speed, the YOLOv4 approach has been widely used for object detection. On the other hand, the identification of tiny targets, particularly imperfections on the surface of metal parts, continues to present a number of obstacles and difficulties. To increase the overall performance of detection, this research provided a technique for the detection of tiny objects based on YOLOv4 for such objects. To increase the effectiveness of the detection process, this involves expanding the size of the k detector while simultaneously eliminating unnecessary branches of the YOLO head network. Experiments have shown that the KD-YOLO model performs better than its predecessors, YOLOv4, YOLOv5, and PP-YOLO, in terms of the total number of parameters, classification accuracy and detection precision. [ABSTRACT FROM AUTHOR]

This study investigates customer satisfaction with mobile banking applications in Turkey through a comprehensive text mining analysis of user-generated reviews. Drawing from a large corpus of data across ten leading Turkish banks, including Ziraat Bank, İş Bank, Garanti BBVA, Akbank, Yapı Kredi Bank, Halkbank, Vakıfbank, DenizBank, QNB Finansbank, and Turkey Şekerbank, the alignment between user ratings and sentiments is explored to uncover the nuances of customer feedback. The dataset undergoes rigorous preprocessing, sentiment analysis, trend analysis, and Latent Dirichlet Allocation (LDA) topic modeling to identify prevailing themes and factors affecting user satisfaction. The methodology involves the classification of reviews into positive, negative, and neutral sentiments and the examination of trends over time to pinpoint periods of heightened dissatisfaction. The analysis is further augmented by the application of advanced machine learning algorithms, including Random Forest, Gradient Boosting Machine, and BERT, showcasing an accuracy range between 92% and 95% in sentiment classification. The results of the topic modeling are visualized through word clouds, providing a clear depiction of the dominant themes in user feedback. Trend analysis over time identifies critical periods where negative reviews surpass positive ones, often coinciding with app updates or changes in service features. The findings highlight the necessity for continuous improvement and testing of mobile banking applications to meet customer expectations effectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Abant Social Sciences / Abant Sosyal Bilimler Dergisi is the property of Journal of Abant Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of International Management Research & Applications is the property of Journal of International Management Research & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In today's globalizing economy, the importance of timely and accurate financial reporting has become more crucial than ever. These reports provide a detailed insight into a company's financial health and performance, offering critical information to investors, creditors, and other relevant parties to make informed decisions. While determining the publication timing of financial reports, strategies are set based on the weight of good and bad news. Companies tend to announce positive news quickly, especially during open market periods, to support their corporate reputation and boost the confidence of shareholders and potential investors. On the other hand, companies might be inclined to withhold or announce bad news during closed market periods to minimize or delay negative market reactions. Through these strategies, firms aim to create either a positive or negative impact on shareholders and investors and control the effect of news most efficiently. The purpose of this study is to examine whether the timing of financial reporting varies depending on firm characteristics. Logistic regression analysis was conducted using data from 2345 observation obtained from companies traded on Borsa Istanbul between 2009 and 2019. The results of the study indicate that firm characteristics such as firm size and financial leverage have a significant effect on whether financial reports are announced on weekdays or weekends. These findings reveal that the timing strategies in financial reporting can vary depending on certain characteristics of the firm. Notably, they highlight that when deciding how and when to announce positive or negative news, companies consider both their own characteristics and market conditions. [ABSTRACT FROM AUTHOR]