scholarly journals Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA)

2021 ◽  
Vol 103 ◽  
pp. 107161
Author(s):  
K.E. ArunKumar ◽  
Dinesh V. Kalaga ◽  
Ch. Mohan Sai Kumar ◽  
Govinda Chilkoor ◽  
Masahiro Kawaji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Auto Regressive ◽  
Machine Learning Models

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

scholarly journals Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2205
Author(s):  
Luis Alfonso Menéndez García ◽  
Fernando Sánchez Lasheras ◽  
Paulino José García Nieto ◽  
Laura Álvarez de Prado ◽  
Antonio Bernardo Sánchez
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Moving Average ◽  
Environmental Pollutants ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Learning Models ◽  
Vector Autoregressive ◽  
Benzene Concentration ◽  
Machine Learning Models

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

scholarly journals Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives

2021 ◽  
Author(s):  
Hua-Liang Wei ◽  
Stephen A Billings
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Systems Thinking ◽  
Systems Engineering ◽  
Moving Average ◽  
A Priori ◽  
Learning Models ◽  
New Findings ◽  
Machine Learning Models

Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables pre-selected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective. The present study, for the first time, proposes using systems engineering and system identification approach to build transparent, interpretable, parsimonious and simulatable (TIPS) dynamic machine learning models, establishing links between the R number, the infection cases and deaths caused by COVID-19. The TIPS models are developed based on the well-known NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) model, which can help better understand the COVID-19 pandemic dynamics. A case study on the UK COVID-19 data is carried out, and new findings are detailed. The proposed method and the associated new findings are useful for better understanding the spread dynamics of the COVID-19 pandemic.

scholarly journals Spatial Statistical Machine Learning Models to Assess Health Vulnerabilities in Children

2021 ◽  
Author(s):  
Wala Draidi Areed ◽  
Aiden Price ◽  
Kathryn Arnett ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Physical Health ◽  
Spatial Variation ◽  
First Year ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Health And Development ◽  
Machine Learning Models

Abstract Background: The health and development of children during their first year of school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence health vulnerabilities among children. This article studies the relationships between health vulnerabilities and educational factors among children in Queensland, Australia. In Queensland, the percentage of children who are developmentally vulnerable in at least one domain in 2018 was around 26%, and the overall percentage of attendance at preschool was around 75.4% These are the lowest rates among all states and territories of Australia. There is also substantial geographic variation in rates across the state. Methods: Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between health vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches.Results: In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the health vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion: This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of health vulnerabilities among children in Queensland. At small-area population level (statistical area level 2 (SA2)), increased attendance at preschool was strongly associated with reduced physical and emotional health vulnerabilities among children in their first year of school.

scholarly journals Detecting the Exploitation of Hardware Vulnerabilities using Electromagnetic Emanations

2021 ◽  
Author(s):  
Giancarlo Canales Barreto ◽  
Nicholas Lamb
Keyword(s):  
Machine Learning ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Machine Learning Models

We present a cache attack monitoring methodology that leverages statistical machine learning models to detect n-day hardware attacks by analyzing the electromagnetic emanations of a device. Experimental results from a Raspberry Pi 4 hosting Linux and a Jetson TX2 development board running a Linux guest hosted by seL4 demonstrate that our approach can sense Spectre attacks with a concordance statistic of 97% and 95%.

Detecting the Exploitation of Hardware Vulnerabilities using Electromagnetic Emanations

2021 ◽  
Author(s):  
Giancarlo Canales Barreto ◽  
Nicholas Lamb
Keyword(s):  
Machine Learning ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Learning Models ◽  
Statistical Machine Learning ◽  
Machine Learning Models

We present a cache attack monitoring methodology that leverages statistical machine learning models to detect n-day hardware attacks by analyzing the electromagnetic emanations of a device. Experimental results from a Raspberry Pi 4 hosting Linux and a Jetson TX2 development board running a Linux guest hosted by seL4 demonstrate that our approach can sense Spectre attacks with a concordance statistic of 97% and 95%.

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