Development of Worldwide Tsunami Hazard Map for Evacuation Planning and Rescue Operations

Jose, Jais; Yuvaraj, E.; Aswin, S.; Suresh, Anupama

doi:10.20944/preprints202004.0370.v1

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…As a result, deep networks with simple architecture and less MPJPE values are best suited for tasks like human pose estimation and motion retargeting to detect behavior. [33,34] Figure 3: Accuracy values observed in the methods as in Table 1 using confusion matrix.…”

Section: Evaluation Of Machine Learning and Deep Learning Approachmentioning

confidence: 99%

Study on Machine Learning and Deep Learning Methods for Human Action Recognition

Rajendran¹,

Lee²,

Gopi³

et al. 2020

Preprint

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With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

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Section: Evaluation Of Machine Learning and Deep Learning Approachmentioning

confidence: 99%

Study on Machine Learning and Deep Learning Methods for Human Action Recognition

Rajendran¹,

Lee²,

Gopi³

et al. 2020

Preprint

Self Cite

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“…There is thusly huge enthusiasm for characterizing a sound way to deal with de-acceleration. In any case, except if the occurrence of contaminations is diminished to an exceptionally low level in a given setting, the transmission will proceed until a populace assurance limit is reached [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning

Roy¹,

Jose²,

Sunil³

et al. 2020

Preprint

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The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22nd January 2020, till 26th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

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