Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis

An integrated framework for predicting the best financial performance of banks: evidence from Egypt

“…Although, these conclusions sort of diverge from studies that considered both approaches as an alternative (Mousavi et al , 2019; Samoilenko and Osei-Bryson, 2010; Azadeh et al , 2007; Santin et al , 2004; Wang, 2003), as well as distinguishing in the prediction scope of ROA and ROE as the dominant pillars of financial performance. They are consistent with those of similar studies that adopted the DEA-ANN integration to exploit their aforementioned advantages (Tsolas et al , 2020; Shokrollahpour et al , 2016; Kwon and Lee, 2015; Kwon, 2014; Kheirkhah et al , 2013; Azadeh et al , 2011; Çelebi and Bayraktar, 2008; Angelidis and Lyroudi, 2006), which emphasized that banks can rely on DEA-ANN combination to assess and forecast the best financial performance, determine causes of inefficiency and proactive management of performance regardless of the priority of either DEA or ANN usage. The findings have also provided evidence that benefiting from the integrative DEA-ANN capabilities is not restricted to organizations in developed countries.…”

“…Neural network performance depends on model-building parameters such as training sequence, number of hidden neurons and learning mechanisms. However, network optimization remains a challenging task for practitioners (Kwon, 2014; Kheirkhah et al , 2013; Emrouznejad and Shale, 2009).…”

An integrated framework for predicting the best financial performance of banks: evidence from Egypt

“…Although, these conclusions sort of diverge from studies that considered both approaches as an alternative (Mousavi et al , 2019; Samoilenko and Osei-Bryson, 2010; Azadeh et al , 2007; Santin et al , 2004; Wang, 2003), as well as distinguishing in the prediction scope of ROA and ROE as the dominant pillars of financial performance. They are consistent with those of similar studies that adopted the DEA-ANN integration to exploit their aforementioned advantages (Tsolas et al , 2020; Shokrollahpour et al , 2016; Kwon and Lee, 2015; Kwon, 2014; Kheirkhah et al , 2013; Azadeh et al , 2011; Çelebi and Bayraktar, 2008; Angelidis and Lyroudi, 2006), which emphasized that banks can rely on DEA-ANN combination to assess and forecast the best financial performance, determine causes of inefficiency and proactive management of performance regardless of the priority of either DEA or ANN usage. The findings have also provided evidence that benefiting from the integrative DEA-ANN capabilities is not restricted to organizations in developed countries.…”

“…Neural network performance depends on model-building parameters such as training sequence, number of hidden neurons and learning mechanisms. However, network optimization remains a challenging task for practitioners (Kwon, 2014; Kheirkhah et al , 2013; Emrouznejad and Shale, 2009).…”

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

“…found out the MAPE values to be 14% and 18%, respectively for ANN and ARIMA models they used to estimate wind speeds of certain regions. Contrary to these results,Kheırkhah et al (2013), argued that ARIMA model provided a better result in their energy demand forecasting models due to its dynamic structure (MAPE: 0,103). .........…”

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

“…ANN has the ability to learn from iterations, which is widely used in various fields of science. The advantages of ANN are being able to adopt, study and generalize [43,44]…”

An integrated framework for predicting the best financial performance of banks: evidence from Egypt

“…Although, these conclusions sort of diverge from studies that considered both approaches as an alternative (Mousavi et al , 2019; Samoilenko and Osei-Bryson, 2010; Azadeh et al , 2007; Santin et al , 2004; Wang, 2003), as well as distinguishing in the prediction scope of ROA and ROE as the dominant pillars of financial performance. They are consistent with those of similar studies that adopted the DEA-ANN integration to exploit their aforementioned advantages (Tsolas et al , 2020; Shokrollahpour et al , 2016; Kwon and Lee, 2015; Kwon, 2014; Kheirkhah et al , 2013; Azadeh et al , 2011; Çelebi and Bayraktar, 2008; Angelidis and Lyroudi, 2006), which emphasized that banks can rely on DEA-ANN combination to assess and forecast the best financial performance, determine causes of inefficiency and proactive management of performance regardless of the priority of either DEA or ANN usage. The findings have also provided evidence that benefiting from the integrative DEA-ANN capabilities is not restricted to organizations in developed countries.…”

“…Neural network performance depends on model-building parameters such as training sequence, number of hidden neurons and learning mechanisms. However, network optimization remains a challenging task for practitioners (Kwon, 2014; Kheirkhah et al , 2013; Emrouznejad and Shale, 2009).…”

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

“…found out the MAPE values to be 14% and 18%, respectively for ANN and ARIMA models they used to estimate wind speeds of certain regions. Contrary to these results,Kheırkhah et al (2013), argued that ARIMA model provided a better result in their energy demand forecasting models due to its dynamic structure (MAPE: 0,103). .........…”

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

“…ANN has the ability to learn from iterations, which is widely used in various fields of science. The advantages of ANN are being able to adopt, study and generalize [43,44]…”

An integrated framework for predicting the best financial performance of banks: evidence from Egypt

“…Although, these conclusions sort of diverge from studies that considered both approaches as an alternative (Mousavi et al , 2019; Samoilenko and Osei-Bryson, 2010; Azadeh et al , 2007; Santin et al , 2004; Wang, 2003), as well as distinguishing in the prediction scope of ROA and ROE as the dominant pillars of financial performance. They are consistent with those of similar studies that adopted the DEA-ANN integration to exploit their aforementioned advantages (Tsolas et al , 2020; Shokrollahpour et al , 2016; Kwon and Lee, 2015; Kwon, 2014; Kheirkhah et al , 2013; Azadeh et al , 2011; Çelebi and Bayraktar, 2008; Angelidis and Lyroudi, 2006), which emphasized that banks can rely on DEA-ANN combination to assess and forecast the best financial performance, determine causes of inefficiency and proactive management of performance regardless of the priority of either DEA or ANN usage. The findings have also provided evidence that benefiting from the integrative DEA-ANN capabilities is not restricted to organizations in developed countries.…”

“…Neural network performance depends on model-building parameters such as training sequence, number of hidden neurons and learning mechanisms. However, network optimization remains a challenging task for practitioners (Kwon, 2014; Kheirkhah et al , 2013; Emrouznejad and Shale, 2009).…”

The Forecasting of Renewable Energy Generation for Turkey by Artificial Neural Networks and a Auto-Regressive Integrated Moving-Average Model -2023 Generation Targets by Renewable Energy Resources

“…found out the MAPE values to be 14% and 18%, respectively for ANN and ARIMA models they used to estimate wind speeds of certain regions. Contrary to these results,Kheırkhah et al (2013), argued that ARIMA model provided a better result in their energy demand forecasting models due to its dynamic structure (MAPE: 0,103). .........…”

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

“…ANN has the ability to learn from iterations, which is widely used in various fields of science. The advantages of ANN are being able to adopt, study and generalize [43,44]…”