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Nirmalya Roy
Researcher at University of Maryland, Baltimore County
Publications - 154
Citations - 2220
Nirmalya Roy is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 21, co-authored 121 publications receiving 1727 citations. Previous affiliations of Nirmalya Roy include Washington State University & University of Maryland, College Park.
Papers
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Journal ArticleDOI
Recent trends in machine learning for human activity recognition—A survey
TL;DR: This article presents a comprehensive overview of recent machine learning and data mining techniques generally employed for AR and the underpinning problems and challenges associated with the existing systems.
Journal ArticleDOI
Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments
TL;DR: A hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors, with a focus on multi-inhabitant environments.
Proceedings ArticleDOI
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation
TL;DR: A transductive transfer learning model that is specifically tuned to the properties of convolutional neural networks (CNNs) is proposed, called HDCNN, which assumes that the relative distribution of weights in the different CNN layers will remain invariant, as long as the set of activities being monitored does not change.
Proceedings ArticleDOI
Context-aware resource management in multi-inhabitant smart homes a Nash H-learning based approach
TL;DR: This paper proves that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem and develops a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty.
Proceedings ArticleDOI
A game theory based pricing strategy for job allocation in mobile grids
TL;DR: This article realizes the vision of mobile grid computing by proposing a fair pricing strategy and an optimal, static job allocation scheme and shows that by drawing upon the Nash bargaining solution (NBS), it can obtain an unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing.