Zobrazeno 1 - 10
of 614
pro vyhledávání: '"Monadjemi, A."'
Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns
Externí odkaz:
http://arxiv.org/abs/2407.21041
The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We pre
Externí odkaz:
http://arxiv.org/abs/2404.14521
The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by an
Externí odkaz:
http://arxiv.org/abs/2304.09415
The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while
Externí odkaz:
http://arxiv.org/abs/2208.05021
Publikováno v:
IET Image Processing, Vol 18, Iss 4, Pp 996-1013 (2024)
Abstract Melanoma, a widespread and hazardous form of cancer, has prompted researchers to prioritize dermoscopic image‐based algorithms for classifying skin lesions. Recently, there has been a growing trend in using pre‐trained convolutional neur
Externí odkaz:
https://doaj.org/article/cc246c6338ba4d9aa7cc03af73a6ccb4
Researchers collect large amounts of user interaction data with the goal of mapping user's workflows and behaviors to their higher-level motivations, intuitions, and goals. Although the visual analytics community has proposed numerous taxonomies to f
Externí odkaz:
http://arxiv.org/abs/2201.03740
Publikováno v:
In Computer Vision and Image Understanding February 2024 239
Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical
Externí odkaz:
http://arxiv.org/abs/2010.08155
Analyzing interaction data provides an opportunity to learn about users, uncover their underlying goals, and create intelligent visualization systems. The first step for intelligent response in visualizations is to enable computers to infer user goal
Externí odkaz:
http://arxiv.org/abs/2009.06042
Autor:
Abbasi, Ashkan, Monadjemi, Amirhassan, Fang, Leyuan, Rabbani, Hossein, Noormohammadi, Neda, Zhang, Yi
The data-driven sparse methods such as synthesis dictionary learning (e.g., K-SVD) and sparsifying transform learning have been proven effective in image denoising. However, they are intrinsically single-scale which can lead to suboptimal results. We
Externí odkaz:
http://arxiv.org/abs/2003.11265