Filtreler

2007 - 200912020 - 20251
Filtreleri Sıfırla

Ayarlar

İndeks: Web of Science × Konu: Information systems ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Yayın
    The modified proactive feedback based flow control scheme for best-effort applications
    (International Institute of Informatics and Systemics (IIIS), 2007) Dağ, Tamer
    High speed networks that are characterized by large bandwidth propagation delay products are expected to support applications with diverse traffic characteristics and Quality of Service (QoS) requirements. Thus, flow control schemes are needed for an efficient usage of the network bandwidth. A proactive feedback (PF) based flow control scheme developed by the author attempts to eliminate the bandwidth mismatch problem seen in such networks by generating and transmitting early feedbacks based on the application characteristics. In this paper, an extension of this scheme to large scale networks is presented. Due to the bottlenecked network nodes, some best effort applications may not be able to use their assigned bandwidth. For such cases, a modified version of the proactive feedback based (MPF) flow control scheme is introduced. It is observed that without affecting the other applications the best effort traffic can be significantly increased.
  • Küçük Resim
    Yayın
    Comparing pre-trained and fine-tuned transformer-based models for sentiment analysis in Turkish comments in student surveys
    (Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Pourjalil, Kajal; Ekin, Emine; Recal, Füsun
    Student surveys are essential for evaluating teaching quality and course content, but analyzing open-ended responses is challenging due to their unstructured and multilingual nature. This study applies sentiment analysis to Turkish educational survey responses using three transformer-based models: SAVASY, DBMDZ BERT Base Turkish Cased, and XLM-RoBERTa Base. A labeled dataset of real-world student comments was used, with sentiment labels assigned using the Gemini AI tool to facilitate model fine-tuning. Evaluation metrics included accuracy, F1-score, precision, recall, and confidence scores. Results show that fine-tuning improves sentiment classification, effectively identifying positive, negative, and neutral sentiments. This highlights the value of transformer models in analyzing Turkish student feedback.