Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Adhikari, Bijaya"'
Implicit graph neural networks have gained popularity in recent years as they capture long-range dependencies while improving predictive performance in static graphs. Despite the tussle between performance degradation due to the oversmoothing of lear
Externí odkaz:
http://arxiv.org/abs/2406.17894
Autor:
Adhikari, Bijaya
Broadly this thesis looks into network and time-series mining problems pertaining to dynamics over networks in various domains. Which locations and staff should we monitor in order to detect C. Difficile outbreaks in hospitals? How do we predict the
Externí odkaz:
http://hdl.handle.net/10919/106727
Autor:
Jang, Hankyu, Lee, Sulyun, Hasan, D. M. Hasibul, Polgreen, Philip M., Pemmaraju, Sriram V., Adhikari, Bijaya
As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare
Externí odkaz:
http://arxiv.org/abs/2303.11563
Autor:
Adhikari, Bijaya1 bijayakusam@gmail.com, Maharjan, Niroj
Publikováno v:
Asian Journal of Medical Sciences. Aug2024, Vol. 15 Issue 8, p151-155. 5p.
Autor:
Rodríguez, Alexander, Cui, Jiaming, Ramakrishnan, Naren, Adhikari, Bijaya, Prakash, B. Aditya
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous i
Externí odkaz:
http://arxiv.org/abs/2202.10446
Autor:
Hasan, D. M. Hasibul, Rohwer, Alex, Jang, Hankyu, Herman, Ted, Polgreen, Philip M., Sewell, Daniel K., Adhikari, Bijaya, Pemmaraju, Sriram V.
COVID-19 has caused an enormous burden on healthcare facilities around the world. Cohorting patients and healthcare professionals (HCPs) into "bubbles" has been proposed as an infection-control mechanism. In this paper, we present a novel and flexibl
Externí odkaz:
http://arxiv.org/abs/2105.06278
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods. While these methods have achieved qualified success, their applicability is limited due to their inabil
Externí odkaz:
http://arxiv.org/abs/2101.10247
Autor:
Amiri, Sorour E., Adhikari, Bijaya, Wenskovitch, John, Rodriguez, Alexander, Dowling, Michelle, North, Chris, Prakash, B. Aditya
Generating useful network summaries is a challenging and important problem with several applications like sensemaking, visualization, and compression. However, most of the current work in this space do not take human feedback into account while gener
Externí odkaz:
http://arxiv.org/abs/2012.11821
Autor:
Rodríguez, Alexander, Adhikari, Bijaya, González, Andrés D., Nicholson, Charles, Vullikanti, Anil, Prakash, B. Aditya
Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states
Externí odkaz:
http://arxiv.org/abs/2012.03413
Autor:
Rodríguez, Alexander, Muralidhar, Nikhil, Adhikari, Bijaya, Tabassum, Anika, Ramakrishnan, Naren, Prakash, B. Aditya
Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still remains a challenge despite increasing research interest. It is even more
Externí odkaz:
http://arxiv.org/abs/2009.11407