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Mariofanna Milanova

As seen in: MDPI
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Articles

Multi-Agent RAG Framework for Entity Resolution: Advancing Beyond Single-LLM Approaches with Specialized Agent Coordination

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By Muzakkiruddin Ahmed Mohammed, Mariofanna Milanova, John Talburt, Aatif Muhammad
| preprints.org
This version is not peer-reviewed. Aatif Muhammad,Muzakkiruddin Ahmed Mohammed,Mariofanna Milanova  *,John R. Talburt,Mert Can Cakmak Aatif Muhammad,Muzakkiruddin Ahmed Mohammed,Mariofanna Milanova  *,John R. Talburt,Mert Can Cakmak Submitted: 30 October 2025 You are already at the latest version Entity resolution in real-world datasets remains a persistent challenge, particularly in identifying households and detecting co-residence patterns within inconsistent and incomplete data.
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A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing

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By Mariofanna Milanova, Walid Abdallah, Walid Abdallah Shobaki
| MDPI
1. Introduction Eye-gaze writing is a novel interaction modality with the potential to revolutionize communication for individuals with limited mobility [1]. However, this technology faces several limitations. In low-light conditions or when users experience eye fatigue or irritation, excessive head movement or blinking can hinder eye-tracking accuracy, making it difficult to control the cursor or select items.
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LSTM-Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting

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By R. Kabir, Mariofanna Milanova, Dipayan Bhadra, Moinul Ridoy
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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