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pro vyhledávání: '"Calyam P"'
Autor:
Yeddulapalli, Hemanth Sai, Alarcon, Mauro Lemus, Roy, Upasana, Neupane, Roshan Lal, Gafurov, Durbek, Mounesan, Motahare, Debroy, Saptarshi, Calyam, Prasad
Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogeneous and intermittent resources to support machine/deep learning (ML/DL
Externí odkaz:
http://arxiv.org/abs/2409.03057
The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact verification has sparked the interest in the development of automated claim verification systems. Several deep learning an
Externí odkaz:
http://arxiv.org/abs/2408.14317
In recent times, Volunteer Edge-Cloud (VEC) has gained traction as a cost-effective, community computing paradigm to support data-intensive scientific workflows. However, due to the highly distributed and heterogeneous nature of VEC resources, centra
Externí odkaz:
http://arxiv.org/abs/2407.01428
Autor:
Surya, Ramakrishna, Koerner, Gordon L., Hajilounezhad, Taher, Safavigerdin, Kaveh, Calyam, Prasad, Bunyak, Filiz, Palaniappan, Kannappan, Maschmann, Matthew R.
Understanding the dynamic self-assembly mechanisms of carbon nanotube (CNT) forests is necessary to advance their technological promise. Here, in-situ environmental scanning electron microscope (ESEM) chemical vapor deposition (CVD) synthesis observe
Externí odkaz:
http://arxiv.org/abs/2402.19336
Autor:
Neupane, Roshan Lal, Bonnah, Ernest, Bhusal, Bishnu, Neupane, Kiran, Hoque, Khaza Anuarul, Calyam, Prasad
Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Use of Blockchain technology-based platform can significantly improve scalability and response time for processing of c
Externí odkaz:
http://arxiv.org/abs/2402.13169
A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However, these detection methods are perceived as computationally intensive an
Externí odkaz:
http://arxiv.org/abs/2302.01985
Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification)
Externí odkaz:
http://arxiv.org/abs/2209.05257
Autor:
Valluripally, Samaikya, Akashe, Vaibhav, Fisher, Michael, Falana, David, Hoque, Khaza Anuarul, Calyam, Prasad
Social virtual reality learning environments (VRLEs) provide immersive experience to users with increased accessibility to remote learning. Lack of maintaining high-performance and secured data delivery in critical VRLE application domains (e.g., mil
Externí odkaz:
http://arxiv.org/abs/2108.12315
Social Virtual Reality Learning Environment (VRLE) is a novel edge computing platform for collaboration amongst distributed users. Given that VRLEs are used for critical applications (e.g., special education, public safety training), it is important
Externí odkaz:
http://arxiv.org/abs/1911.03563
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