Zobrazeno 1 - 10
of 10 835
pro vyhledávání: '"Ozmen A"'
Autor:
Yazdani-Jahromi, Mehdi, Yalabadi, Ali Khodabandeh, Rajabi, AmirArsalan, Tayebi, Aida, Garibay, Ivan, Garibay, Ozlem Ozmen
The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in informatio
Externí odkaz:
http://arxiv.org/abs/2410.16432
Autor:
Kowsher, Md, Sobuj, Md. Shohanur Islam, Prottasha, Nusrat Jahan, Alanis, E. Alejandro, Garibay, Ozlem Ozmen, Yousefi, Niloofar
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series d
Externí odkaz:
http://arxiv.org/abs/2410.11674
Autor:
Prottasha, Nusrat Jahan, Mahmud, Asif, Sobuj, Md. Shohanur Islam, Bhat, Prakash, Kowsher, Md, Yousefi, Niloofar, Garibay, Ozlem Ozmen
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning
Externí odkaz:
http://arxiv.org/abs/2410.08598
There is a growing interest in integrating Large Language Models (LLMs) with autonomous driving (AD) systems. However, AD systems are vulnerable to attacks against their object detection and tracking (ODT) functions. Unfortunately, our evaluation of
Externí odkaz:
http://arxiv.org/abs/2409.14488
Autor:
Abdidizaji, Sina, Baekey, Alexander, Jayalath, Chathura, Mantzaris, Alexander, Garibay, Ozlem Ozmen, Garibay, Ivan
The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identi
Externí odkaz:
http://arxiv.org/abs/2407.09657
Autor:
Abdidizaji, Sina, Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Garibay, Ozlem Ozmen, Garibay, Ivan
Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of inf
Externí odkaz:
http://arxiv.org/abs/2401.11656
Autor:
Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Abdidizaji, Sina, Garibay, Ivan, Garibay, Ozlem Ozmen
The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A
Externí odkaz:
http://arxiv.org/abs/2401.11524
Autor:
Mahmut Muslumanoglu, Baran Mollavelioglu, Neslihan Cabioglu, Selman Emiroglu, Mustafa Tukenmez, Hasan Karanlık, Tolga Ozmen, Ravza Yılmaz, Rana Gunoz Comert, Semen Onder, Aysel Bayram, Duygu Has Simsek, Melis Oflas, Kamuran Ibis, Adnan Aydıner, Vahit Ozmen, Abdullah Igci
Publikováno v:
World Journal of Surgical Oncology, Vol 22, Iss 1, Pp 1-10 (2024)
Abstract Background Sentinel lymph node biopsy (SLNB) is widely used in patients who receive neoadjuvant chemotherapy (NAC). Still, axillary lymph node dissection (ALND) is recommended for patients with any axillary residual disease after NAC. The ne
Externí odkaz:
https://doaj.org/article/cedf3b5f7ef947de9876b0f84578dfb9
Autor:
Yalabadi, Ali Khodabandeh, Yazdani-Jahromi, Mehdi, Yousefi, Niloofar, Tayebi, Aida, Abdidizaji, Sina, Garibay, Ozlem Ozmen
Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challeng
Externí odkaz:
http://arxiv.org/abs/2311.02326
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains. Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of
Externí odkaz:
http://arxiv.org/abs/2309.13543