S
Svetha Venkatesh
Researcher at Deakin University
Publications - 864
Citations - 20118
Svetha Venkatesh is an academic researcher from Deakin University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 60, co-authored 828 publications receiving 16441 citations. Previous affiliations of Svetha Venkatesh include Australian National University & National University of Singapore.
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Journal ArticleDOI
Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal.
Chandan Karmakar,Radhagayathri K. Udhayakumar,Peng Li,Svetha Venkatesh,Marimuthu Palaniswami +4 more
TL;DR: The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn, and DistEn showed the most consistent and the best performance in differentiating physiological and pathological conditions with various ofinput parameters among reported complexity measures.
Multiple camera coordination in a surveillance system
TL;DR: In this article, a distributed surveillance system that uses multiple cheap static cameras to track multiple people in indoor environments is presented, where a set of camera processing modules and a central module are used to coordinate the tracking tasks among the cameras.
Journal ArticleDOI
Mood sensing from social media texts and its applications
TL;DR: A large-scale mood analysis in social media texts is presented, addressing the problem of feature selection and classification of mood in blogosphere and extracting global mood patterns at different level of aggregation from a large- scale data set of approximately 18 millions documents.
Proceedings ArticleDOI
Eventscapes: visualizing events over time with emotive facets
TL;DR: A novel data-driven visualization that combines time, visual media, mood, and controversy is presented, which highlights the value of emotive facets for rapid evaluation of mixed news and social media topics, and a role for such visualizations as pre-cursors to deeper search.
Posted Content
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
TL;DR: This work proposes a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.