A Computational Intelligence Approach to Software Component Repository Management

2014 ◽  
pp. 109-132
Author(s):  
D. Vijay Rao ◽  
V. V. S. Sarma
Keyword(s):  
Computational Intelligence ◽  
Software Component ◽  
Intelligence Approach ◽  
Component Repository ◽  
Software Component Repository

Green factors of referential value-based software component repository

2020 ◽  
Vol 9 (2/3) ◽  
pp. 178
Author(s):  
Pradeep Kumar ◽  
Shailendra Narayan Singh ◽  
Sudhir Dawra
Keyword(s):  
Software Component ◽  
Component Repository ◽  
Software Component Repository

Green factors of referential value-based software component repository

2020 ◽  
Vol 9 (2/3) ◽  
pp. 178
Author(s):  
Pradeep Kumar ◽  
Shailendra Narayan Singh ◽  
Sudhir Dawra
Keyword(s):  
Software Component ◽  
Component Repository ◽  
Software Component Repository

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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