Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

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Learn, Assign, and Search: Real-Time Estimation of Dynamic Origin-Destination Flows Using Machine Learning Algorithms

IEEE Access ◽

10.1109/access.2019.2901289 ◽

2019 ◽

Vol 7 ◽

pp. 26967-26983 ◽

Cited By ~ 6

Author(s):

Jishun Ou ◽

Jiawei Lu ◽

Jingxin Xia ◽

Chengchuan An ◽

Zhenbo Lu

Keyword(s):

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Time Estimation ◽

Machine Learning Algorithms ◽

Real Time Estimation

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VIIRS Nighttime Light Data for Income Estimation at Local Level

Remote Sensing ◽

10.3390/rs12182950 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2950

Author(s):

Kinga Ivan ◽

Iulian-Horia Holobâcă ◽

József Benedek ◽

Ibolya Török

Keyword(s):

Machine Learning ◽

Real Time ◽

Local Level ◽

Time Estimation ◽

Learning Model ◽

Earth Observation ◽

The Real ◽

Machine Learning Model ◽

Income Data ◽

Real Time Estimation

The aim of the paper is to develop a model for the real-time estimation of local level income data by combining machine learning, Earth Observation, and Geographic Information System. More exactly, we estimated the income per capita by help of a machine learning model for 46 cities with more than 50,000 inhabitants, based on the National Polar-orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime satellite images from 2012–2018. For the automation of calculation, a new ModelBuilder type tool was developed within the ArcGIS software called EO-Incity (Earth Observation–Income city). The sum of light (SOL) data extracted by means of the EO-Incity tool and the observed income data were integrated in an algorithm within the MATLAB software in order to calculate a transfer equation and the average error. The results achieved were subsequently reintegrated in EO-Incity and used for the estimation of the income value at local level. The regression analyses highlighted a stable and strong relationship between SOL and income for the analyzed cities. The EO-Incity tool and the machine learning model proved to be efficient in the real-time estimation of the income at local level. When integrated in the information systems specific for smart cities, they can serve as a support for decision-making in order to fight poverty and reduce social inequalities.

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Real-time Estimation of Global CFR Ascribed to COVID-19 Confirmed Cases Applying Machine Learning Technique

10.1101/2021.12.27.21268463 ◽

2021 ◽

Author(s):

MONALISHA PATTNAIK ◽

ARYAN PATTNAIK

Keyword(s):

Machine Learning ◽

Real Time ◽

Regression Tree ◽

Time Estimation ◽

World Health Organisation ◽

World Health ◽

Global Risk ◽

Machine Learning Technique ◽

Learning Technique ◽

Real Time Estimation

The COVID-19 is declared as a public health emergency of global concern by World Health Organisation (WHO) affecting a total of 201 countries across the globe during the period December 2019 to January 2021. As of January 25, 2021, it has caused a pandemic outbreak with more than 99 million confirmed cases and more than 2 million deaths worldwide. The crisp of this paper is to estimate the global risk in terms of CFR of the COVID-19 pandemic for seventy deeply affected countries. An optimal regression tree algorithm under machine learning technique is applied which identified four significant features like diabetes prevalence, total number of deaths in thousands, total number of confirmed cases in thousands, and hospital beds per 1000 out of fifteen input features. This real-time estimation will provide deep insights into the early detection of CFR for the countries under study.

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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