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

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.

scholarly journals Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2910
Author(s):  
Andreas Andreou ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla ◽  
Evangelos Pallis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Study ◽  
Nonlinear Least Squares ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Marquardt Algorithm ◽  
Learning Technique ◽  
Iot Devices ◽  
Algorithmic Techniques

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

scholarly journals Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cédric Beaulac ◽  
Fabrice Larribe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Real Time Estimation ◽  
Mobile Target

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.

Interactive Learning of Timbral Rhythms for Percussion Robots

2018 ◽  
Vol 42 (2) ◽  
pp. 35-51 ◽  
Author(s):  
Michael Krzyzaniak
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Interactive Learning ◽  
Machine Learning Technique ◽  
Human Partner ◽  
Learning Technique ◽  
Improvised Music ◽  
Time Observation ◽  
Real Time Observation

This article presents a machine-learning technique to analyze and produce statistical patterns in rhythm through real-time observation of human musicians. Here, timbre is considered an integral part of rhythm, as might be exemplified by hand-drum music. Moreover, this article considers challenges (such as mechanical timing delays, that are negligible in digitally synthesized music) that arise when the algorithm is executed on percussion robots. The algorithm's performance is analyzed in a variety of contexts, such as learning specific rhythms, learning a corpus of rhythms, responding to signal rhythms that signal musical transitions, improvising in different ways with a human partner, and matching the meter and the “syncopicity” of improvised music.

scholarly journals Modeling of Surfactant-Enhanced Drying of Poly(styrene)-p-xylene Polymeric Coatings Using Machine Learning Technique

Coatings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1529
Author(s):  
Raj Kumar Arya ◽  
Jyoti Sharma ◽  
Rahul Shrivastava ◽  
Devyani Thapliyal ◽  
George D. Verros
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Regression Tree ◽  
Maximum Error ◽  
Polymeric Coatings ◽  
Tree Model ◽  
Machine Learning Technique ◽  
Squared Error ◽  
Learning Technique ◽  
Predicted Values

In this work, a machine learning technique based on a regression tree model was used to model the surfactant enhanced drying of poly(styrene)-p-xylene coatings. The predictions of the developed model based on regression trees are in excellent agreement with the experimental data. A total of 16,258 samples were obtained through experimentation. These samples were separated into two parts: 12,960 samples were used for the training of the regression tree, and the remaining 3298 samples were used to test the tree’s prediction accuracy. MATLAB software was used to grow the regression tree. The mean squared error between the model-predicted values and actual outputs was calculated to be 8.8415 × 10−6. This model has good generalizing ability; predicts weight loss for given values of time, thickness, and triphenyl phosphate; and has a maximum error of 1%. It is robust and for this system, can be used for any composition and thickness for this system, which will drastically reduce the need for further experimentations to explain diffusion and drying.

scholarly journals SELECTING KDD FEATURES AND USING RANDOM CLASSIFICATION TREE FOR PREDICTING ATTACKS

2014 ◽  
pp. 115-123
Author(s):  
Rachid Beghdad
Keyword(s):  
Machine Learning ◽  
Classification Tree ◽  
Regression Tree ◽  
Classification Trees ◽  
Classification And Regression Tree ◽  
Data Sets ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Classification And Regression ◽  
Anova Technique

The purpose of this study is to identify some higher-level KDD features, and to train the resulting set with an appropriate machine learning technique, in order to classify and predict attacks. To achieve that, a two-steps approach is proposed. Firstly, the Fisher’s ANOVA technique was used to deduce the important features. Secondly, 4 types of classification trees: ID3, C4.5, classification and regression tree (CART), and random tree (RnDT), were tested to classify and detect attacks. According to our tests, the RndT leads to the better results. That is why we will present here the classification and prediction results of this technique in details. Some of the remaining results will be used later to make comparisons. We used the KDD’99 data sets to evaluate the considered algorithms. For these evaluations, only the four attack categories’ case was considered. Our simulations show the efficiency of our approach, and show also that it is very competitive with some similar previous works.

scholarly journals VIIRS Nighttime Light Data for Income Estimation at Local Level

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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