Zobrazeno 1 - 10
of 397 912
pro vyhledávání: '"A A, Abdullah"'
Adversarial attacks exploit the vulnerabilities of convolutional neural networks by introducing imperceptible perturbations that lead to misclassifications, exposing weaknesses in feature representations and decision boundaries. This paper presents a
Externí odkaz:
http://arxiv.org/abs/2412.19747
This paper explores the intricate relationship between interpretability and robustness in deep learning models. Despite their remarkable performance across various tasks, deep learning models often exhibit critical vulnerabilities, including suscepti
Externí odkaz:
http://arxiv.org/abs/2412.18952
Autor:
Abdullah, Dana A, Khidir, Hewir A., Maolood, Ismail Y., Ameen, Aso K., Hamad, Dana Rasul, Beitolahi, Hakem Saed, Abdullah, Abdulhady Abas, Rashid, Tarik Ahmed, Shakor, Mohammed Y.
In today's digital age, information systems (IS) are indispensable tools for organizations of all sizes. The quality of these systems, encompassing system, information, and service dimensions, significantly impacts organizational performance. This st
Externí odkaz:
http://arxiv.org/abs/2412.18512
Autor:
Khondoker, Abdullah, Taufik, Enam Ahmed, Tashik, Md Iftekhar Islam, mahmud, S M Ishtiak, Parsa, Antara Firoz
Evaluating text comprehension in educational settings is critical for understanding student performance and improving curricular effectiveness. This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and
Externí odkaz:
http://arxiv.org/abs/2412.18440
Autor:
Ke, Yu He, Jin, Liyuan, Elangovan, Kabilan, Ong, Bryan Wen Xi, Oh, Chin Yang, Sim, Jacqueline, Loh, Kenny Wei-Tsen, Soh, Chai Rick, Cheng, Jonathan Ming Hua, Lee, Aaron Kwang Yang, Ting, Daniel Shu Wei, Liu, Nan, Abdullah, Hairil Rizal
Large Language Models (LLMs) are emerging as powerful tools in healthcare, particularly for complex, domain-specific tasks. This study describes the development and evaluation of the PErioperative AI CHatbot (PEACH), a secure LLM-based system integra
Externí odkaz:
http://arxiv.org/abs/2412.18096
Autor:
Nafi, Abdullah al Nomaan, Hossain, Md. Alamgir, Rifat, Rakib Hossain, Zaman, Md Mahabub Uz, Ahsan, Md Manjurul, Raman, Shivakumar
Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about th
Externí odkaz:
http://arxiv.org/abs/2412.16860
Human behavior and interactions are profoundly influenced by visual stimuli present in their surroundings. This influence extends to various aspects of life, notably food consumption and selection. In our study, we employed various models to extract
Externí odkaz:
http://arxiv.org/abs/2412.16807
This study investigates the potential of Large Language Models (LLMs), particularly GPT-4o, for Optical Character Recognition (OCR) in low-resource scripts such as Urdu, Albanian, and Tajik, with English serving as a benchmark. Using a meticulously c
Externí odkaz:
http://arxiv.org/abs/2412.16119
Autor:
Wendlinger, Lorenz, Braun, Christian, Zubaer, Abdullah Al, Nonn, Simon Alexander, Großkopf, Sarah, Fellicious, Christofer, Granitzer, Michael
We show that current open-source foundational LLMs possess instruction capability and German legal background knowledge that is sufficient for some legal analysis in an educational context. However, model capability breaks down in very specific tasks
Externí odkaz:
http://arxiv.org/abs/2412.15902
This paper proposes a dataset augmentation method by fine-tuning pre-trained diffusion models. Generating images using a pre-trained diffusion model with textual conditioning often results in domain discrepancy between real data and generated images.
Externí odkaz:
http://arxiv.org/abs/2412.15358