Recognizing Motion Images Solid Waste Jumbled on a Neural Network with a Simple Tracking Performed in an Automatic and Robotic Recycling Line

2016 ◽  
pp. 116-124 ◽  
Author(s):  
Martín García-Hernández ◽  
Alejandro Flores ◽  
Eleazar Elizalde ◽  
Alejandro J. García-Arredondo ◽  
Miguel A. Gutiérrez-Muro ◽  
...  
Keyword(s):  
Neural Network ◽  
Solid Waste ◽  
Motion Images

scholarly journals Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor

2016 ◽  
Vol 58 ◽  
pp. 202-213 ◽  
Author(s):  
Daya Shankar Pandey ◽  
Saptarshi Das ◽  
Indranil Pan ◽  
James J. Leahy ◽  
Witold Kwapinski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fluidized Bed ◽  
Fluidized Bed Reactor ◽  
Artificial Neural ◽  
Waste Gasification ◽  
Modelling Approach

Combined imaging and neural-network-based approach in solid waste sorting

2002 ◽  
Author(s):  
Stefano Alunni ◽  
Giuseppe Bonifazi ◽  
Alessandro de Carli ◽  
Paolo Massacci
Keyword(s):  
Neural Network ◽  
Solid Waste ◽  
Combined Imaging

scholarly journals ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

scholarly journals Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2045
Author(s):  
Nehal Elshaboury ◽  
Eslam Mohammed Abdelkader ◽  
Ghasan Alfalah ◽  
Abobakr Al-Sakkaf
Keyword(s):  
Neural Network ◽  
Waste Management ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Mean Bias Error ◽  
Bias Error ◽  
P Value ◽  
Waste Generation

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

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