Mean Square Error Values Research Articles

Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures

Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0–96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R2) and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R2 and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R2 and 2.7 MSE values. Our results indicate that ML methods can predict of E. coli O157:H7 growth in ground beef at different temperatures to strengthen food safety professionals and legal authorities to assess contamination risks and determine legal limits and criteria proactively.

Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model

Developing an accurate forecasting model for electricity demand plays a vital role in maximising the efficiency of the planning process in the power generation industries. The time series data of electricity demand in Malaysia is highly volatile with seasonal characteristics. This study aims to evaluate the forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model with GARCH for weekly maximum electricity demand. The weekly maximum electricity demand data (in megawatt, MW) from 2005 to 2016 has been used for this study. The results show that SARIMA(1, 1, 0)(0, 1, 0)52−GARCH(1, 2) with generalized error distribution (GED) is the most appropriate model for forecasting electricity demand due to its parsimonious characteristic with low values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) which are 644.1828, 523.8380 and 3.13%, respectively. The MAPE value of the proposed model which is less than 5% indicates that the SARIMA − GARCH model is relatively good in forecasting electricity demand for the case of Malaysia data. In conclusion, the proposed model of SARIMA with GARCH has great potential and provides a promising performance in forecasting electricity demand with seasonal highly volatile characteristics.

Analisis Peramalan Kebutuhan Komponen Minifix Bolt Dia di PT XYZ Menggunakan Metode Time Series

PT XYZ is a company that produces furniture products that use the "Make to stock" method to meet consumer demand. In the production process, there is often a buildup of raw materials, one of which is the minifix bolt component, which causes a buildup of raw materials in the warehouse. Therefore, it is necessary to forecast raw materials to predict the amount of raw materials each period. In the forecasting process, you can use the moving average and single ex-ponential smoothing methods. From the calculation results using the POM-QM software, the MAD (Mean Absolute Deviation) value was 13,111.11, MSE (Mean Squared Error) was 261,975,300, MAPE (Mean Absolute Percent Error) was 226,303%. From the software output results, it is known that the smallest MAD, MSE, and MAPE values ​​of the three methods are the moving average method. So this method was chosen to solve problems within the company because the smaller the error that occurs, the smaller the possibility of something bad happening.

Application of ARIMA (Autoregressive Integrated Moving Average) model to predict Rupiah selling exchange rate against US Dollar

Currency is a tool in the form of money that is accepted and valid and legal as a payment and economic transactions in a country. US dollar is benchmark for world currencies, so predicting rupiah against US dollar is important. The purpose of this study is to analyze the characteristics of daily selling rate Rupiah against US Dollar, determine best model, and make predictions selling rate of Rupiah against US Dollar. Data used is daily data on selling rate of Rupiah against US Dollar 20 November 2020 - 19 January 2023 with details data training 20 November 2020 - 20 November 2022 and data testing 21 November 2022 - 19 January 2023. The model used is Autoregressive Integrated Moving Averages (ARIMA). The best model was chosen based on the smallest Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Akaike Information Criterion (AIC). From the analysis results, it is found that best model is ARIMA (2,1,2) because it has significant parameters, white-noise residuals and has smallest MSE and MAPE values. With ARIMA model (2,1,2) the forecasting results for January 20 2023 – January 31 2023 is obtained with highest selling price on January 30 2023 Rp.15,932.4 and smallest on January 20 2023 Rp.15,901.9 and the average Rp.15,919.4. Based on these results, exporters and importers can consider their business activities.

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

This research aims to optimize wholesale inventory management at PT Primafood International Pasir Putih 2 by implementing leading algorithms, namely XGBoost, Support Vector Regression (SVR), and Random Forest. In the wholesale industry, effective inventory management plays a crucial role in maintaining smooth production processes and enhancing company profitability. Despite the acknowledged benefits of inventory management, there are aspects that remain not fully disclosed, particularly concerning demand uncertainty and market fluctuations. This study addresses these gaps by exploring the potential of these three algorithms. Experimental methods with a quantitative approach were employed to shape and prepare the dataset. The analysis and predictions' results using XGBoost, SVR, and Random Forest were evaluated using metrics such as Mean Squared Error (MSE), F1-Score, and Accuracy. The evaluation indicates that XGBoost and SVR exhibit optimal performance with low MSE values of 7714.446 and 119.315, high F1-Scores (0.92), and good accuracy levels (0.86 and 0.85), respectively. While Random Forest shows a higher MSE, it still delivers solid performance with an F1-Score of 0.89 and an accuracy rate of 0.81. These findings suggest that all three algorithms can be considered to enhance inventory management performance at PT Primafood International Pasir Putih 2, with significant potential benefits for overall industry development. This research provides valuable insights for decision-making at the business and industrial levels, highlighting the effectiveness of each algorithm in the context of predicting stock level.

Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas.

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.

Forecasting Blood Demand Using the Support Vektor Regression Method (Case Study: Blood Transfusion Unit-PMI Central Lombok)

Blood is an important component produced by the human body. Blood is also a very vital part of human survival. When blood levels in the human body are less than they should be, the way to overcome this is by donating blood or blood transfusion. The health facilities that organize blood donations, provide blood and distribute blood are called Blood Transfusion Units (UTD). UTD in carrying out its duties encountered several obstacles, such as blood only having a shelf life of 35 days from donation. If it has passed the expiration date, it cannot be used anymore for blood transfusions. Meanwhile, regarding the demand for blood, the need for blood is greater than those donating. Making it difficult for UTD if the demand occurs when the existing blood stock is not sufficient. And if the stock in UTD experiences an axcess, it can cause losses because the blood is wasted due to expiration. Apart form that. The problem is that in everyday life, many people’s need for blood is reduced. Many of their families intervened directly to find available donors. They even search on social networks or social media such as WhatsApp, Facebook, Instagram and others. And this shows that many of them lack donors. To anticipate these problems. So it is necessary to carry out research on forecasting blood demand using the Support Vektor Regression method at UTD PMI Central Lombok. The aim of this research is to forecast or predict the demand for blood at UTD PMI Central Lombok in the coming period. To reduce the impact of lack or excess blood. SVR is the application of Support Vektor Machine (SVM) in the case of regression to find the best dividing line in the regression function. The advantage of the SVR model is that it can handle overfiting problems in the data. The tests used to measure the best model are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The results of this research shows that the best model is Support Vektor Regression (SVR) with a polynomial kernel and based on the tuning results, the parameters used are C=10, degree=1, epsilon=1. The SVR model using a polynomial kernel produces a MAPE value of 18.7502% and RMSE value of 0.6919, which means the model has very good predictive ability. Prediction accuracy was achieved with an R2 value of 0.9936 or 99.36% and an MSE value of 0.4787, which means that the prediction of blood demand data at UTD PMI Central Lombok using SVR with a polynomial kernel function had very good prediction accuracy. With predicted result in january for blood type A it was 1654, B was 920, O was 2205 and AB was 1104

Application of AI for Short-Term PV Generation Forecast.

The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).

Forecasting COVID-19 Daily Infected Cases in Sri Lanka by Holt-Winters Exponential Smoothing Method

The novel coronavirus disease (COVID-19) has spread from China since December 2019 and spread worldwide including Sri Lanka. The aim of this study was to forecast the daily infected cases of COVID-19 in Sri Lanka which in turn help administrators for effective management of the pandemic. The method used in this study was Holt-Winters three parameter with additive or multiplicative models. The daily infected cases in Sri Lanka during the period of 22nd January 2020 to 22nd December 2021 were obtained from the publicly available databases of Epidemiology Unit of Sri Lanka and World Health Organization. The pattern recognition of the daily infected cases was examined by time series plot and Auto Correlation Function (ACF). The model validation was performed by the Anderson Darling test which confirmed the normality of residuals (p > 0.05) and ACF that confirmed the independence of residuals of the model. The forecasting ability of the model was assessed by the three measurements of errors; Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Square Error (MSE). Holt-Winters additive and multiplicative model with α (level) 0.61, β (trend) 0.4 and γ (seasonal) 0.3 at a length of repeating behaviour of 3 days, had the least relative and absolute measurement of errors during the model fitting and verification. İn the multiplicative model, MAPE, MAD and MSE were 0.2847, 0.0187 and 0.0005 respectively. Similarly in the additive model, corresponding values of MAPE, MAD and MSE were 0.0207, 0.0187 and 0.0005. The fits and the forecast of these models followed a similar pattern of the actual daily infected cases concluding that the Holt-Winters model can be used to forecast the COVID-19 outbreak in Sri Lanka.

Effects of CO2 concentration and time on algal biomass film, NO3–N concentration, and pH in the membrane bioreactor: Simulation-based ANN, RSM and NSGA-II

The practice of aquaculture is associated with the generation of a substantial quantity of effluent. Microalgae must effectively assimilate nitrogen and phosphorus from their surrounding environment for growth. This study modeled the algal biomass film, NO3–N concentration, and pH in the membrane bioreactor using the response surface methodology (RSM) and an artificial neural network (ANN). Furthermore, it was suggested that the optimal condition for each parameter be determined. The results of ANN modeling showed that ANN with a structure of 5-3 and employing the transfer functions tansig-logsig demonstrated the highest level of accuracy. This was evidenced by the obtained values of coefficient (R2) = 0.998, R = 0.999, mean squared error (MAE) = 0.0856, and mean square error (MSE) = 0.143. The ANN model, characterized by a 5-5 structure and employing the tansig-logsig transfer function, demonstrates superior accuracy when predicting the concentration of NO3–N and pH. This is evidenced by the high values of R2 (0.996), R (0.998), MAE (0.00162), and MSE (0.0262). The RSM was afterward employed to maximize the performance of algal film biomass, pH levels, and NO3–N concentrations. The optimal conditions for the algal biomass film were a concentration of 2.884 mg/L and a duration of 6.589 days. Similarly, the most favorable conditions for the NO3–N concentration and pH were 2.984 mg/L and 6.787 days, respectively. Therefore, this research uses non-dominated sorting genetic algorithm II (NSGA II) to find the optimal NO3–N concentration, algal biomass film, and pH for product or process quality. The region has the greatest alkaline pH and lowest NO3–N content.

Predicting Axial Force in Friction Stir Welded Joints of AA7075-T6 and AA2024-T3 Using Machine Learning Techniques

The friction stir welding (FSW) process has gained popularity in joining various types of aluminum alloys. The heat generated during the process is caused by the friction between the shoulder of the welding tool and the workpiece. The axial force (Fz) of the weld plays a crucial role in the welding process. If the axial force is insufficient to generate heat will result in defective workpieces. Therefore, the aim of this study was to predict the axial force of the FSW process of dissimilar aluminum alloys (AA7075-T6 and AA2024-T3) using machine learning techniques. The data used to create the predictive model was obtained from experiments involving 5 factors, each with 2 levels: 1) welding speed (mm/min), 2) rotational speed (RPM), 3) type of tools, 4) plunge depth (mm), and 5) dwell time (sec). The axial force was measured using a dynamometer with a sampling frequency of 10 Hz. The predictive model was created using all 4 algorithms: AdaBoost, CatBoost, LightGBM, and XGBoost. The performance of the four predictive models was evaluated using four metrics: mean absolute error (MAE), mean absolute percent error (MAPE), mean square error (MSE), and root mean square error (RMSE). The results showed that the AdaBoost algorithm had the best performance, with MAE, MAPE, MSE, and RMSE values of 509.08, 0.24, 452591.73, and 672.75, respectively. The AdaBoost algorithm was then used to predict the axial force using a dataset of 29,282 data, with predicted minimum average axial force of 1871.6 N. When compared to the axial force measured in the experiment, which was 1265 N, the results showed that the AdaBoost algorithm was capable of predicting the axial force with acceptable accuracy.

Forecasting of Solar Power Using GRU–Temporal Fusion Transformer Model and DILATE Loss Function

Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load dynamics of the grid. Consequently, there has been growing attention to developing an accurate forecast model using various machine and deep learning techniques. Temporal attention mechanisms enable the model to concentrate on the critical components of the input sequence at each time step, thereby enhancing the accuracy of the prediction. The suggested GRU–temporal fusion transformer (GRU-TFT) model was trained and validated employing the “Daily Power Production of Solar Panels” Kaggle dataset. Furthermore, an innovative loss function termed DILATE is introduced to train the proposed model specifically for multistep and nonstationary time series forecasting. The outcomes have been subjected to a comparative analysis with alternative algorithms, such as neural basis expansion analysis for interpretable time series (N-BEATS), neural hierarchical interpolation for time series (N-HiTS), and extreme gradient boosting (XGBoost), using several evaluation metrics, including the absolute percentage error (MAE), mean square error (MSE), and root mean square error (RMSE). The model presented in this study exhibited significant performance improvements compared with traditional statistical and machine learning techniques. This is evident from the achieved values of MAE, MSE, and RMSE, which were 1.19, 2.08, and 1.44, respectively. In contrast, the machine learning approach utilizing the Holt–Winters method for time series forecasting in additive mode yielded MAE, MSE, and RMSE scores of 4.126, 29.105, and 5.3949, respectively.

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Viscosity is an essential mud property which enables it to transport cuttings out of a well. Determining the real time value of mud viscosity at downhole conditions is complex and till date lacks a precise model. Literature is filled with models that are only applicable at surface conditions with most of the models being artificial neural network (ANN) based. Neural networks have been criticized for their long training time because they rely on a trial-and-error approach to determine their optimal architecture. The few models related to mud viscosity at downhole conditions are based on non-linear regression which are not flexible in nature. However, Multivariate Adaptive Regression Splines (MARS) has at no time been used notwithstanding some of its known benefits over ANN and other regression methods. The focus of this study is to model mud plastic viscosity at downhole conditions with the MARS method using parameters such as temperature, pressure and initial mud viscosity. MARS is a flexible non-parametric splines-based technique that extends linear regression models by including nonlinearities and interactions between predictors. An experimental database comprising 88 and 149 data points culled from literature for oil-based mud (OBM) and water-based mud (WBM) respectively were utilized for developing the models. For each mud type, the most suitable model was determined by choosing the model with the maximum coefficient of determination (R2) value and the least values of mean square error (MSE), root mean square error (RMSE), and generalized cross-validation criterion (GCV). Using these criteria, the R2, MSE and RMSE of the MARS models according to the mud type were found to be 0.64, 104.35, 10.21 and 0.71, 12.8, 3.57 for OBM and WBM respectively while the GCV values were 129.3 and 14.38 for OBM and WBM respectively. Due to lack of mud rheology data at downhole conditions, it was difficult to compare the performance of the developed models with existing models. Parametric importance analysis indicate that temperature has the highest effect on mud viscosity estimation at downhole conditions. The developed models are valid for 20 °C ≤ Temperature ≤315 °C and 0 psi ≤ Pressure ≤40,000 psi. This pioneer work on using MARS in drilling fluid property prediction points out the implementation protocol, the dataset utilized, the relative importance of input variables and the explicit model for estimating mud viscosity at downhole conditions.

Rapid Detection of Total Viable Count in Intact Beef Dishes Based on NIR Hyperspectral Hybrid Model

The total viable count (TVC) of bacteria is an important index to evaluate the freshness and safety of dishes. To improve the accuracy and robustness of spectroscopic detection of total viable bacteria count in a complex system, a new method based on a near-infrared (NIR) hyperspectral hybrid model and Support Vector Machine (SVM) algorithms was developed to directly determine the total viable count in intact beef dish samples in this study. Diffuse reflectance data of intact and crushed samples were tested by NIR hyperspectral and processed using Multiplicative Scattering Correction (MSC) and Competitive Adaptive Reweighted Sampling (CARS). Kennard–Stone (KS) and Samples Set Partitioning Based on Joint X-Y Distance (SPXY) algorithms were used to select the optimal number of standard samples transferred by the model combined with root mean square error. The crushed samples were transferred into the complete samples prediction model through the Direct Standardization (DS) algorithm. The spectral hybrid model of crushed samples and full samples was established. The results showed that the Determination Coefficient of Calibration (RP2) value of the total samples prediction set increased from 0.5088 to 0.8068, and the value of the Root Mean Square Error of Prediction (RMSEP) decreased from 0.2454 to 0.1691 log10 CFU/g. After establishing the hybrid model, the RMSEP value decreased by 9.23% more than before, and the values of Relative Percent Deviation (RPD) and Reaction Error Relation (RER) increased by 12.12% and 10.09, respectively. The results of this study showed that TVC instewed beef samples can be non-destructively determined based on the DS model transfer method combined with the hybrid model strategy. This study provided a reference for solving the problem of poor accuracy and reliability of prediction models in heterogeneous samples.

One decade of temperature change at the North Pole and South Pole using ANN backpropagation algorithm

Global warming is an event in which the average temperature of the atmosphere, ocean and land rises. Changes in atmospheric temperature cause changes in the physical conditions of the atmosphere to become unstable, causing anomalies in weather parameters that cause climate change. In the last decade there have been temperature changes in the North Pole (Greenland) and the South Pole (Antarctica). The impacts caused by temperature changes are melting ice sheets, rising sea levels, extinction of species in large numbers, people living on the coast are threatened by tidal floods, while small islands can sink. To anticipate temperature changes, a model is needed that can forecast air or temperature conditions using Artificial Neural Network with Backpropagation method using Matlab software media. Data obtained from Data Access Viewer of NASA. After training and testing the data, the average value of the prediction results for the south polar region (Antarctica) is -47.910C with a Mean Square Error (MSE) value of 0.0015. Meanwhile, for the North Pole region (Greenland), the average value of the prediction results is -22.410C with an MSE value of 0.0069. By looking at the average value of the prediction results, it can be concluded that the temperature change for the South Pole region (Antarctica) is higher than the temperature change for the North Pole region (Greenland).

Improvement of Productivity in Buildings Construction

Abstract Improving productivity in construction projects has long been a major concern, and much research has been carried out to try to ameliorate construction productivity. To this end, this study aims to improve and increase the productivity rate of flat slab formwork used in residential construction projects. A survey consisting of 150 questionnaires was undertaken to identify the factors that influence on the productivity. Based on the relative Importance Index (RII), data on eleven factors deemed to affect productivity were selected. A collection of 100 data points from various sites were utilized to develop two models. Firstly, an Artificial Neural Network (ANN) model was employed, and secondly, a parametric approach was investigated. The data were divided into two sets, with 70% of the data used for training and the remaining 30% used for testing. The models’ performance was evaluated using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. In the test phase, the artificial neural network model yielded an MSE value of 2.6610e−4 and a MAPE value of 4.9227, whereas the parametric model produced an MSE of 0.040 and a MAPE of 9.525. It was found that the artificial neural network model provided reliable prediction accuracy compared to the parametric model. However, the artificial neural network approach can be selected as a robust model in predicting and controlling the productivity rate in local construction projects by using the developed model based on the identified factors.

Air Pollution Index Forecasting Model using Automated Machine Learning

Malaysia's Air Pollution Index (API) has steadily climbed in recent decades, posing a severe environmental hazard. In this paper, authors used the Automated Machine Learning (AutoML) technique for, to analyse daily integer value time series data for API in Malaysia from January to December in 2019. The parameters of the models will be calculated using an error rated, and their model’s result will be compared to determine which model best fits the data. The results showed that the AutoML model was more accurate in predicting API values. The MSE value of the AutoML model and the RMSE were lower, indicating that the model had a lower error rate than traditional regression models. Furthermore, predicting ability will be evaluated using mean square error (MSE) and correlation coefficient as accuracy. The findings indicated that the AutoML model produced better results in MSE and better accuracy. The fundamental benefit of utilising the AutoML model is that it is readily automated, making it a valuable tool for academics and scientists. This model may also be used as a forecasting tool to enhance air quality in a certain location. Overall, the results indicate that the AutoML model is a viable and promising technique for predicting air pollution. The findings are critical for managing API outcomes in the future and adopting protective measures for air conservation.

A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China.

Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks. Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA, ETS, and support vector regression (SARIMA-ETS-SVR) hybrid model. The prediction performance was determined by comparing mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) indices. The optimum SARIMA model was SARIMA (0,1,0) (0,0,1)12. Error trend and seasonality (ETS) (M,A,M) was the SARIMA optimal model. For the fitting performance, the SARIMA-ETS-SVR hybrid model achieved the lowest values of MAE, MSE, and RMSE, in addition to the MAPE. In terms of predictive performance, the SARIMA-ETS-SVR hybrid model had the lowest MAE, MSE, MAPE, and RMSE values among the three models. The study demonstrated that the SARIMA-ETS-SVR hybrid model provides better generalization ability than a single SARIMA model and a single ETS model, and the predictions will provide a useful tool for preventing this infectious disease.

Deep residual network for soil nutrient assessment using optical sensors

AbstractBackgroundFarmers need information regarding soil fertility at every location of their fields to attain a higher level of precision in nutrient management. Nonetheless, the acquisition and processing of soil samples are labor‐intensive and time‐utilizing, and the related cost remains high‐priced to farmers. Artificial intelligence is the most speedily growing area combined into approximately all aspects of human life. Soil macronutrients like nitrogen (N), phosphorous (P), and potassium (K) have a significant role in precision agriculture. There is a huge need for powerful and rapid measurement systems to measure accurately the macronutrients in the soil for optimal crop productivity, especially in site‐specific crop management system, where the application of fertilizer can be regulated spatially with respect to crop demand. Nevertheless, it can present a research direction to design an advanced scheme in order to predict the properties of soil. A portable sensor device is a basic need of an agriculture system for the accurate and rapid monitoring of soil macronutrients.AimIn this research, the soil nutrients identified from the collected soil samples using optical sensors are evaluated for their accuracy using a deep learning approach.MethodsA deep residual network is exploited for the soil nutrient prediction after augmenting the gathered soil data. Finally, various performance evaluation measures, like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE), are calculated to detect how accurately the sensor predicted the soil nutrients.ResultsFrom the experimental analysis, it is stated that the proposed model attained low MSE value of 4.59 e−09, the low RMSE value of 6.78 e−05, and the low MAE value of 4.66 e−05 for N prediction. Likewise, the proposed model attained the least MSE value of 1.41 e−05, the least RMSE value of 0.0003, and the least MAE value of 0.0001 for P prediction.ConclusionFinally, for K prediction, the proposed model achieved the least MSE value of 1.54 e−06, least RMSE value of 1.24 e−03, and the least MAE value of 1.38 e−05.

An experimental study for efficiency modeling of grid-connected photovoltaic system

ABSTRACT As a key factor, it is vital to mathematically model the electrical efficiency of photovoltaic (PV) systems. This paper investigates the efficient modeling of a 2.5 kW grid-connected PV system, located in Mahan, Kerman, Iran, with respect to different factors like solar radiation, humidity, dust deposition, temperature, and shading. For this aim, different nonlinear models are considered and a curve-fitting approach is used to optimally determine the unknown parameters. Over the case study, simulation results show that when the dust deposition is not included in the modeling (solar radiation, ambient temperature, cell temperature, humidity, and shading as inputs), the third-degree polynomial model leads to more accurate result than the other models. By use of the third-degree polynomial model, the value of root mean square error (RMSE) for train and test data sets is obtained 0.0123 and 0.0109, respectively. In this case, the average RMSE value is 0.0116. On the other hand, when dust impact is included in the modeling (solar radiation, ambient temperature, humidity, and number of dusty days as inputs), the accuracy of the exponential model is more than the other ones. In this case, the value of RMSE for both train and test data sets is obtained 0.0018. Sensitivity analysis shows that the system efficiency depends mainly on the radiation, cell temperature, and dust and less on the humidity.