Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

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Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽

10.11113/matematika.v35.n4.1263 ◽

2019 ◽

Vol 35 (4) ◽

pp. 53-64

Author(s):

Siti Nabilah Syuhada Abdullah ◽

Ani Shabri ◽

Ruhaidah Samsudin

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Forecast Errors ◽

Ann Model ◽

Mode Decomposition ◽

The Neural Network ◽

Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

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Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

Hydrology Research ◽

10.2166/nh.2020.141 ◽

2020 ◽

Vol 51 (3) ◽

pp. 423-442

Author(s):

Naser Dehghanian ◽

S. Saeid Mousavi Nadoushani ◽

Bahram Saghafian ◽

Morteza Rayati Damavandi

Keyword(s):

Flood Control ◽

Mean Squared Error ◽

Absolute Error ◽

Ann Model ◽

Runoff Generation ◽

Source Areas ◽

Walnut Gulch ◽

Artificial Neural Network Ann ◽

Semi Arid ◽

Flood Source

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

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Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

Sensors ◽

10.3390/s19143054 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3054 ◽

Cited By ~ 5

Author(s):

Sigfredo Fuentes ◽

Gabriela Chacon ◽

Damir D. Torrico ◽

Andrea Zarate ◽

Claudia Gonzalez Viejo

Keyword(s):

Management Practices ◽

Mean Squared Error ◽

Computer Application ◽

Aerial Image ◽

Canopy Architecture ◽

Ann Model ◽

Aroma Profile ◽

Squared Error ◽

Artificial Neural Network Ann ◽

The Tropics

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality.

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COVID-19 Infection Forecasting based on Deep Learning in Iran

10.1101/2020.05.16.20104182 ◽

2020 ◽

Cited By ~ 3

Author(s):

Mehdi Azarafza ◽

Mohammad Azarafza ◽

Jafar Tanha

Keyword(s):

Short Term Memory ◽

Mean Squared Error ◽

Moving Average ◽

National Level ◽

Absolute Error ◽

Percentage Error ◽

Autoregressive Integrated Moving Average ◽

Error Metrics ◽

Squared Error ◽

Better Than

Since December 2019 coronavirus disease (COVID-19) is outbreak from China and infected more than 4,666,000 people and caused thousands of deaths. Unfortunately, the infection numbers and deaths are still increasing rapidly which has put the world on the catastrophic abyss edge. Application of artificial intelligence and spatiotemporal distribution techniques can play a key role to infection forecasting in national and province levels in many countries. As methodology, the presented study employs long short-term memory-based deep for time series forecasting, the confirmed cases in both national and province levels, in Iran. The data were collected from February 19, to March 22, 2020 in provincial level and from February 19, to May 13, 2020 in national level by nationally recognised sources. For justification, we use the recurrent neural network, seasonal autoregressive integrated moving average, Holt winter's exponential smoothing, and moving averages approaches. Furthermore, the mean absolute error, mean squared error, and mean absolute percentage error metrics are used as evaluation factors with associate the trend analysis. The results of our experiments show that the LSTM model is performed better than the other methods on the collected COVID-19 dataset in Iran

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Prediction of rubber vulcanization using an artificial neural network

Hemijska industrija ◽

10.2298/hemind210511026l ◽

2021 ◽

Vol 75 (5) ◽

pp. 277-283

Author(s):

Jelena Lubura ◽

Predrag Kojic ◽

Jelena Pavlicevic ◽

Bojana Ikonic ◽

Radovan Omorjan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Large Scale ◽

Mean Squared Error ◽

Percentage Error ◽

Dense Layer ◽

Ann Model ◽

Different Temperatures ◽

Artificial Neural ◽

Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

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A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/8159506 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yaoqin Lu ◽

Huan Yan ◽

Lijiang Zhang ◽

Jiwen Liu

Keyword(s):

Occupational Disease ◽

Hybrid Algorithm ◽

Prevention And Control ◽

Occupational Diseases ◽

Mean Squared Error ◽

Percentage Error ◽

Ann Model ◽

Squared Error ◽

Precise Prediction ◽

And Control

Occupational disease is a huge problem in China, and many workers are under risk. Accurate forecasting of occupational disease incidence can provide critical information for prevention and control. Therefore, in this study, five hybrid algorithm combing models were assessed on their effectiveness and applicability to predict the incidence of occupational diseases in China. The five hybrid algorithm combing models are the combination of five grey models (EGM, ODGM, EDGM, DGM, and Verhulst) and five state-of-art machine learning models (KNN, SVM, RF, GBM, and ANN). The quality of the models were assessed based on the accuracy of model prediction as well as minimizing mean absolute percentage error (MAPE) and root-mean-squared error (RMSE). Our results showed that the GM-ANN model provided the most precise prediction among all the models with lowest mean absolute percentage error (MAPE) of 3.49% and root-mean-squared error (RMSE) of 1076.60. Therefore, the GM-ANN model can be used for precise prediction of occupational diseases in China, which may provide valuable information for the prevention and control of occupational diseases in the future.

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Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Applied Sciences ◽

10.3390/app9214554 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4554 ◽

Cited By ~ 10

Author(s):

Hoang Nguyen ◽

Xuan-Nam Bui ◽

Trung Nguyen-Thoi ◽

Prashanth Ragam ◽

Hossein Moayedi

Keyword(s):

State Of The Art ◽

Promising Result ◽

Absolute Error ◽

Original Data ◽

Training Data ◽

Open Pit ◽

Training Dataset ◽

Percentage Error ◽

Ann Model ◽

Ann Models

Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

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Analysis of canopy phenology in man-made forests using near-earth remote sensing

Plant Methods ◽

10.1186/s13007-021-00803-9 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Peng Guan ◽

Yili Zheng ◽

Guannan Lei

Keyword(s):

Remote Sensing ◽

Color Index ◽

Short Term Memory ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Earth Remote Sensing ◽

Squared Error ◽

Highly Sensitive ◽

Different Color

Abstract Background Forest canopies are highly sensitive to their growth, health, and climate change. The study aims to obtain time sequence images in mix foresters using a near-earth remote sensing method to track the seasonal variation in the color index and select the optimal color index. Three different regions of interest (RIOs) were defined and six color indexes (GRVI, HUE, GGR, RCC, GCC, and GEI) were calculated to analyze the microenvironment difference. The key phenological phase was identified using the double logistic model and the derivative method, and the phenology forecast of color indexes was performed based on the long short-term memory (LSTM) model. Results The results showed that the same color index in different RIOs and different color indexes in the same RIO present a slight difference in the days of growth and the days corresponding to the peak value, exhibiting different phenological phases; the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the LSTM model was 0.0016, 0.0405, 0.0334, and 12.55%, respectively, indicating that this model has a good forecast effect. Conclusions In different areas of the same forest, differences in the micro-ecological environment in the canopies were prevalent, with their internal growth mechanism being affected by different cultivation ways and the external environment. Besides, the optimal color index also varies with species in phenological response, that is, different color indexes are used for different forests. With the data of color indexes as the training set and forecast set, the feasibility of the LSTM model in phenology forecast is verified.

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Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients

Scientific Reports ◽

10.1038/s41598-021-03341-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Syed Mohammed Arshad Zaidi ◽

Varun Chandola ◽

Muhanned Ibrahim ◽

Bianca Romanski ◽

Lucy D. Mastrandrea ◽

...

Keyword(s):

Blood Glucose ◽

Diabetes Management ◽

Mean Squared Error ◽

Absolute Error ◽

Target Range ◽

Percentage Error ◽

Diabetic Patients ◽

Average Percentage ◽

Squared Error

AbstractContinuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values for root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and normalized mean squared error (NRMSE) are $$23.22 \pm 6.39$$ 23.22 ± 6.39 mg/dL, 16.77 ± 4.87 mg/dL, $$12.84 \pm 3.68$$ 12.84 ± 3.68 and $$0.08 \pm 0.01$$ 0.08 ± 0.01 respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of $$80.17 \pm 9.20$$ 80.17 ± 9.20 and $$84.81 \pm 6.11,$$ 84.81 ± 6.11 , respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.

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FORECASTING HIGH-FREQUENCY FINANCIAL DATA VOLATILITY VIA NONPARAMETRIC ALGORITHMS: EVIDENCE FROM TAIWAN'S FINANCIAL MARKETS

New Mathematics and Natural Computation ◽

10.1142/s1793005706000543 ◽

2006 ◽

Vol 02 (03) ◽

pp. 345-359 ◽

Cited By ~ 1

Author(s):

WO-CHIANG LEE

Keyword(s):

Computational Intelligence ◽

High Frequency ◽

Mean Squared Error ◽

Absolute Error ◽

Financial Data ◽

Percentage Error ◽

Integrated Volatility ◽

Squared Error ◽

Performance Indexes ◽

Forecasting Performance

This paper uses two computational intelligence algorithms, namely, artificial neural networks (ANN) and genetic programming (GP), for forecasting the volatility of high-frequency TAIEX financial data with four different horizons and compares the out-sample forecasting performance with the GARCH(1,1), EGRACH(1,1) and GJR-GARCH(1,1) models. Based on intraday integrated volatility, the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's U and the VaR backtest are used as performance indexes. Our empirical results reveal that the GP and ANN perform reasonably well in forecasting out-sample volatility compared to other parametric volatility forecasting models for most of the performance indexes. Our results also suggest that nonparametric computational intelligence algorithms are powerful for modeling the volatility of high-frequency intraday financial data.

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