scholarly journals Evaluation of the Use ofAcacia niloticaLeaf as an Ecofriendly Adsorbent for Cr (VI) and Its Suitability in Real Waste Water: Study of Residual Errors

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ashly Leena Prasad ◽  
Santhi Thirumalisamy
Keyword(s):  
Waste Water ◽  
Absolute Error ◽  
Langmuir Model ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Mean Square ◽  
Chi Square ◽  
Average Percentage ◽  
Average Percentage Error ◽  
Residual Errors

The present paper aims to investigate the physical characteristics ofAcacia niloticaleaves (MVM) relative to their use as an adsorbent for removal of hazardous Cr (VI) from waste water. The adsorbent was characterized by FTIR and SEM studies. The applicability of the Langmuir model to MVM was proved by the high coefficient of determination. Eight error analysis methods, namely, residual root mean square error (RMSE), chi-square (χ2), sum of the square of the errors (ERRSQ), composite functional error (HYBRD), derivative of Marquardt's percent standard deviation (MPSD), average relative error (ARE), sum of absolute error (EABS), and average percentage error (APE) were used to evaluate the suitability of the adsorption isotherm. Desorption reveals that recovery of the metal from adsorbent was possible. The ecofriendly adsorbent MVM is expected to be an environmentally and economically feasible adsorbent for the removal of Cr (VI) from aqueous solution and real waste water.

scholarly journals Theoretical Prediction of Ultrasonic Velocity in Organic Liquid Mixtures

2009 ◽  
Vol 6 (s1) ◽  
pp. S235-S238 ◽  
Author(s):  
R. Uvarani ◽  
S. Punitha
Keyword(s):  
Ultrasonic Velocity ◽  
Goodness Of Fit ◽  
Organic Liquid ◽  
Liquid Mixtures ◽  
Percentage Error ◽  
Chi Square ◽  
Average Percentage ◽  
Chi Square Test ◽  
Experimental Values ◽  
Average Percentage Error

Theoretical values of ultrasonic velocity in the binary mixtures of cyclohexanone with 2-propanol and 2-methyl-2-propanol have been evaluated at 303 K using Nomoto’s relation, collision factor theory, free length theory, ideal mixture relation, Junjie’s method. Theoretical values are compared with the experimental values and the validity of the theories are checked by applying the chi-square test for goodness of fit and by calculating the average percentage error (APE).

scholarly journals COVID-19 in Iran: Forecasting Pandemic Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rahele Kafieh ◽  
Roya Arian ◽  
Narges Saeedizadeh ◽  
Zahra Amini ◽  
Nasim Dadashi Serej ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Percentage Error ◽  
Mean Square ◽  
Short Term ◽  
Average Percentage ◽  
The Usa ◽  
Long Short Term Memory ◽  
Average Percentage Error ◽  
Almost All

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2 . The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.

scholarly journals Experimental and Theoretical Studies of Ultrasonic Velocity in Binary Liquid Mixtures of Ethyl benzoate

2012 ◽  
Vol 9 (4) ◽  
pp. 2309-2314 ◽  
Author(s):  
Shaik Babu ◽  
S. V. Kumara Sastry ◽  
Ha Sie Tiong ◽  
S. Sreehari Sastry
Keyword(s):  
Ultrasonic Velocity ◽  
Goodness Of Fit ◽  
Liquid Mixtures ◽  
Ethyl Benzoate ◽  
Percentage Error ◽  
Chi Square ◽  
Average Percentage ◽  
Chi Square Test ◽  
Experimental Values ◽  
Average Percentage Error

Ultrasonic velocity is measured at 2MHz frequency in the binary mixtures of Ethyl Benzoate with 1-Propanol, 1-Butanol, 1-Pentanol and theoretical values of ultrasonic velocity have been evaluated at 303K using Nomoto's relation, Impedance relation, Ideal mixture relation, Junjie's method and free length theory. Theoretical values are compared with the experimental values and the validity of the theories is checked by applying the chi-square test for goodness of fit and by calculating the average percentage error (APE). A good agreement has been found between experimental and Nomoto’s ultrasonic velocity.

scholarly journals Theoretical Prediction of Ultrasonic Velocity in Binary Liquid Mixtures

2011 ◽  
Vol 8 (s1) ◽  
pp. S330-S334 ◽  
Author(s):  
Shaik. Babu
Keyword(s):  
Ultrasonic Velocity ◽  
Goodness Of Fit ◽  
Liquid Mixtures ◽  
Percentage Error ◽  
Chi Square ◽  
Average Percentage ◽  
Different Temperatures ◽  
Chi Square Test ◽  
Experimental Values ◽  
Average Percentage Error

Theoretical values of ultrasonic velocity in the binary mixtures of chlorobenzene with benzene have been evaluated at different temperatures from 303.15 K-323.15 K using Nomoto's relation, ideal mixture relation, Junjie's method, collision factor theory and free length theory. Theoretical values were compared with the experimental values and the validity of the theories was checked by applying the chi-square test for goodness of fit and by calculating the average percentage error (APE).

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.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

Forecasting Malaysian overnight islamic interbank rate using the Box-Jenkins model

2021 ◽  
Vol 2 (1) ◽  
pp. 38-51
Author(s):  
N.S.M. Radzi ◽  
S.R. Yaziz
Keyword(s):  
Monetary Policy ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Prediction Intervals ◽  
Percentage Error ◽  
Mean Square ◽  
Islamic Banks ◽  
Financial Instruments ◽  
Policy Makers ◽  
Forecasting Performance

Modelling the overnight Islamic interbank rate (IIR) is imperative to define the IIR performance as it would help the Islamic banks to adjust its costs of funding effectively and facilitate the policy makers to regulate a comprehensive monetary policy in Malaysia. The IIR framework which has been regulated by Bank Negara Malaysia under dual banking and financial system has always been overlooked in most previous studies in modelling the financial instruments rates. Therefore, it is vital to select the appropriate model as it resembles with the features of the IIR. The study assesses the forecasting performance of overnight IIR using the Box-Jenkins model. The suggested Box-Jenkins model has been applied to the Malaysian overnight IIR (in percentage) from 02/01/2001 to 31/12/2020. The empirical results determine that ARIMA (0,1,1) is the most appropriate model in forecasting overnight IIR as the model provides the smallest Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In multistep ahead forecasting, it can be summarised that ARIMA (0,1,1) model is able to trail the actual data trend of daily Malaysian overnight IIR up to 5-day ahead within 95% prediction intervals.

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 A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

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