scholarly journals SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

Forecasting ◽  
2022 ◽  
Vol 4 (1) ◽  
pp. 72-94
Author(s):  
Roberto Vega ◽  
Leonardo Flores ◽  
Russell Greiner
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Sir Model ◽  
Percentage Error ◽  
Infected People ◽  
Average Percentage ◽  
Interpretable Model ◽  
The Government ◽  
Average Percentage Error ◽  
Government Level

Accurate forecasts of the number of newly infected people during an epidemic are critical for making effective timely decisions. This paper addresses this challenge using the SIMLR model, which incorporates machine learning (ML) into the epidemiological SIR model. For each region, SIMLR tracks the changes in the policies implemented at the government level, which it uses to estimate the time-varying parameters of an SIR model for forecasting the number of new infections one to four weeks in advance. It also forecasts the probability of changes in those government policies at each of these future times, which is essential for the longer-range forecasts. We applied SIMLR to data from in Canada and the United States, and show that its mean average percentage error is as good as state-of-the-art forecasting models, with the added advantage of being an interpretable model. We expect that this approach will be useful not only for forecasting COVID-19 infections, but also in predicting the evolution of other infectious diseases.

Actuation and dynamic mechanical characteristics of a core free flat dielectric electro-active polymer soft actuator

2020 ◽  
Vol 14 (4) ◽  
pp. 7396-7404
Author(s):  
Abdul Malek Abdul Wahab ◽  
Emiliano Rustighi ◽  
Zainudin A.
Keyword(s):  
Theoretical Model ◽  
Resonance Frequency ◽  
Dynamic Testing ◽  
Experimental Results ◽  
Percentage Error ◽  
Initial Tension ◽  
Average Percentage ◽  
Soft Actuator ◽  
Average Percentage Error ◽  
Mechanical Dynamics

Various complex shapes of dielectric electro-active polymer (DEAP) actuator have been promoted for several types of applications. In this study, the actuation and mechanical dynamics characteristics of a new core free flat DEAP soft actuator were investigated. This actuator was developed by Danfoss PolyPower. DC voltage of up to 2000 V was supplied for identifying the actuation characteristics of the actuator and compare with the existing formula. The operational frequency of the actuator was determined by dynamic testing. Then, the soft actuator has been modelled as a uniform bar rigidly fixed at one end and attached to mass at another end. Results from the theoretical model were compared with the experimental results. It was found that the deformation of the current actuator was quadratic proportional to the voltage supplied. It was found that experimental results and theory were not in good agreement for low and high voltage with average percentage error are 104% and 20.7%, respectively. The resonance frequency of the actuator was near 14 Hz. Mass of load added, inhomogeneity and initial tension significantly affected the resonance frequency of the soft actuator. The experimental results were consistent with the theoretical model at zero load. However, due to inhomogeneity, the frequency response function’s plot underlines a poor prediction where the theoretical calculation was far from experimental results as values of load increasing with the average percentage error 15.7%. Hence, it shows the proposed analytical procedure not suitable to provide accurate natural frequency for the DEAP soft actuator.

PP15 Predicting variations of calls to an ambulance service in the UK caused by circulating infections using-deep learning methods

2019 ◽  
Vol 36 (10) ◽  
pp. e7.2-e7
Author(s):  
Thilo Reich ◽  
Marcin Budka
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Prehospital Care ◽  
Machine Learning Algorithms ◽  
Ambulance Service ◽  
Percentage Error ◽  
Learning Methods ◽  
Average Percentage ◽  
Average Percentage Error ◽  
The Uk

BackgroundDigital patient records in the ambulance service have opened up new opportunities for prehospital care. Previously it was demonstrated that prehospital pyrexia numbers are linked to an increase in overall calls to the ambulance service. This study aims to predict the future number of calls using deep-learning methods.MethodsTemperature readings for 280,447 patients were generously provided by the South Western Ambulance Service Trust. The data covered the time between 05/01/2016 and 30/04/2017 with overall 44,472 patients being pyretic. A rolling window of 10 days was applied to daily sums for both pyretic and apyretic patients. These windows were used as input features to train machine-learning algorithms predicting the number of calls 10 days ahead. Algorithms tested include Linear Regression (LR), basic Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. A genetic approach was used to optimise the architecture, in which parameters were randomly modified and over several generations the best performing algorithm will be selected to be further manipulated. To assess performance the Mean Average Percentage Error (MAPE) was used.ResultsThe initial analysis showed that the total patient number and pyretic patient numbers are correlated. The best performing algorithms with varying numbers of hidden units had the following MAPE in comparison to simple LR: LR=19.4%, LSTM (104 units) = 6.1%, RNN (79 units)=6.01%, GRU (80 units)=5.97%.ConclusionsThese preliminary results suggest that deep-learning methods allow to predict the variations in total number of calls caused by circulating infections. Further investigations will aim to confirm these findings. Once fully verified these algorithms could play a major role in operational planning of any ambulance service by predicting increases in demand.

scholarly journals Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types

2020 ◽  
Vol 17 (24) ◽  
pp. 9314
Author(s):  
Verena Hartung ◽  
Mustafa Sarshar ◽  
Viktoria Karle ◽  
Layal Shammas ◽  
Asarnusch Rashid ◽  
...  
Keyword(s):  
Physical Activity ◽  
Arm Movements ◽  
Percentage Error ◽  
Activity Measurement ◽  
Overground Walking ◽  
Activity Monitors ◽  
Average Percentage ◽  
Study Results ◽  
Promotion Of Physical Activity ◽  
Average Percentage Error

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

scholarly journals Comparison of Otolith Readability and Reproducibility of Counts of Translucent Zones Using Different Otolith Preparation Methods for Four Endemic Labeobarbus Species in Lake Tana, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1336 ◽  
Author(s):  
Gebremedhin ◽  
Bekaert ◽  
Getahun ◽  
Bruneel ◽  
Anteneh ◽  
...  
Keyword(s):  
Management Strategies ◽  
Tropical Fish ◽  
Percentage Error ◽  
Systematic Bias ◽  
Preparation Technique ◽  
Lake Tana ◽  
Preparation Methods ◽  
Average Percentage ◽  
Average Percentage Error ◽  
Preparation Techniques

The analysis of fish age data is vital for the successful conservation of fish. Attempts to develop optimal management strategies for effective conservation of the endemic Labeobarbus species are strongly affected by the lack of accurate age estimates. Although methodological studies are key to acquiring a good insight into the age of fishes, up to now, there have not been any studies comparing different methods for these species. Thus, this study aimed at determining the best method for the endemic Labeobarbus species. Samples were collected from May 2016 to April 2017. Asteriscus otoliths from 150 specimens each of L. intermedius, L. tsanensis, L. platydorsus, and L. megastoma were examined. Six methods were evaluated; however, only three methods resulted in readable images. The procedure in which whole otoliths were first submerged in water, and subsequently placed in glycerol to take the image (MO1), was generally best. Except for L. megastoma, this method produced the clearest image as both the coefficient of variation and average percentage error between readers were lowest. Furthermore, except for L. megastoma, MO1 had high otolith readability and no systematic bias. Therefore, we suggest that MO1 should be used as the standard otolith preparation technique for the first three species, while for L. megastoma, other preparation techniques should be evaluated. This study provides a reference for researchers from Africa, particularly Ethiopia, to develop a suitable otolith preparation method for the different tropical fish species.

Stock-specific assessment of precise age and growth in the long-whiskered catfish Sperata aor from the Ganges River

2020 ◽  
Vol 71 (12) ◽  
pp. 1693
Author(s):  
Aafaq Nazir ◽  
M. Afzal Khan
Keyword(s):  
Age And Growth ◽  
Percentage Error ◽  
Specific Information ◽  
Ganges River ◽  
Average Percentage ◽  
Marginal Increment ◽  
Hard Structures ◽  
Average Percentage Error ◽  
Anthropogenic Threats ◽  
The Ganges

Sustainable management of the long-whiskered catfish Sperata aor (Hamilton, 1822) in the Ganges River justifies precise stock-specific information on age and growth. The aim of the present study was to estimate the age and growth of three stocks, namely Narora–Kanpur, Varanasi and Bhagalpur, of S. aor from the Ganges River. Among the hard structures chosen for analysis, vertebrae provided precise age estimates up to 9 years of age in all the three stocks of S. aor based on average percentage error. Edge analysis of vertebrae and marginal increment ratio analysis of sectioned otoliths showed annulus formation once per year during April–June. The von Bertalanffy growth rates showed significant differences between the sexes and among the stocks. The growth coefficient k (0.24–0.30 year–1) showed rapid growth relative to asymptotic length (L∞) in all three stocks. The growth performance index was nearly the same for all three stocks. The results of the present study can be used in formulating scientifically sound management policies in view of anthropogenic threats to the populations of S. aor from the Ganges River.

A COMPARISON OF FORECASTING THE RESULTS OF ROAD TRANSPORTATION NEEDS

Transport ◽  
2012 ◽  
Vol 27 (1) ◽  
pp. 73-78 ◽  
Author(s):  
Bogna Mrówczyńska ◽  
Karolina Łachacz ◽  
Tomasz Haniszewski ◽  
Aleksander Sładkowski
Keyword(s):  
Harmonic Analysis ◽  
Percentage Error ◽  
Road Transportation ◽  
Artificial Immune ◽  
Mathematical Methods ◽  
Average Percentage ◽  
The Past ◽  
Forecasting Methods ◽  
Average Percentage Error

Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winter's method for seasonal problems – a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error). The results of calculations were compared and the best example was presented.

scholarly journals The Design of Portable Battery Charging Devices Using Motorcycle Wheel Round

2019 ◽  
Vol 2 (2) ◽  
pp. 16-19
Author(s):  
Arlenny
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Electrical Power ◽  
Electrical Energy ◽  
Threshold Value ◽  
Electric Power Consumption ◽  
Percentage Error ◽  
Average Percentage ◽  
Power Load ◽  
Average Percentage Error

This research aims to the development of reader equipment as well as control the load limitation of electric power using Atmega 8535 microcontroller. In the development of equipment of reading and controlling electrical energy consumptions, the modified KWH (Kilo Watt Hour) meter was used by placing the optocoupler sensor as the enumerator indicator the electric power consumption on the disc. Atmega 8535 microcontroller was used to control and limitation of the electric power consumption. In this research, the measuring and control system was developed to record the amount of electrical power load used, and it can be used as an alternative to the current divider for the achievement of the efficiency of practical electrical energy consumption. The results of the measurement comparison between the measured load and the output load tended to be stable with an average percentage error of 6.3%, and it was still below the optimum threshold value of the error factor, which around 10%. Therefore, results of testing developed equipment KWH digital meter using Atmega 8535 microcontroller that was produced a good performance.

scholarly journals An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

2021 ◽  
Vol 22 (3) ◽  
pp. 1245
Author(s):  
Adi Kurniawan ◽  
Anisa Harumwidiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Average Percentage ◽  
Daily Average ◽  
Artificial Neural ◽  
Average Percentage Error ◽  
The City

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

scholarly journals Day-Ahead Forecasting of Losses in the Distribution Network

AI Magazine ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 38-49
Author(s):  
Nisha Dalal ◽  
Martin Mølna ◽  
Mette Herrem ◽  
Magne Røen ◽  
Odd Erik Gundersen
Keyword(s):  
Learning System ◽  
Percentage Error ◽  
Small Data ◽  
Data Set ◽  
Average Percentage ◽  
Utility Companies ◽  
Minimal Work ◽  
Average Percentage Error ◽  
Grid Loss ◽  
Incorrect Data

Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.

scholarly journals Pemodelan Dan Simulasi Produksi Biogas Dari Substrat Glukosa Menggunakan Anaerobic Digestion Model No. 1 (ADM1)

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Isman Kurniawan ◽  
Annisa Aditsania
Keyword(s):  
Anaerobic Digestion ◽  
Biogas Production ◽  
Final Concentration ◽  
Percentage Error ◽  
Research Focus ◽  
Average Percentage ◽  
Standard Parameter ◽  
Simulation Parameter ◽  
Average Percentage Error ◽  
In The Beginning

This research focus in modeling of biogas production using Anaerobic Digestion Model No. 1 (ADM1). Initial simulation was performed using recommended parameter and its result will be used to determine the accuracy. Simulation result shows similar trend compare to experimental data even it is less accurate. The accuracy of calculation is improved by optimize the simulation parameter. The number of parameter is reduced by calculate the sensivity indices of each parameter. Optimization process using genetic algorithm result new optimized parameter value. The value of mean average percentage error (MAPE) of simulation using standard parameter and optimized parameter are 22,54% and 0,08%, respectively. It shows that simulation using optimized parameter give better accuracy. Simulation results shows the glucose concentration decrease significantly in the beginning of process and methane concentration increase simultaneously. The final concentration of methan after 500 mgCOD/L of glucose decomposed is 354,79 mgCOD/L.

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