deep learnig

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique , namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore , it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development .

Face recognition is crucial in real-world applications like video surveillance, human-computer interaction, and security systems. As one of the most important research issues in computer vision, this biometric authenticating system incorporates a wide range of real human facial characteristics. Problems with Internal covariate shift based on deep learning methods for face recognition systems causes gradient explosion or disappearance, resulting in inappropriate network training, network overfitting, and computational load. This lowers recognition accuracy and slows the network speed. Deep learning techniques for face recognition systems must overcome these difficulties. This research presents a modified Pre-activation Batch Normalization Convolutional Neural Network (PABNCNN), which is characteristic with a batch normalization operation after each convolutional layer in all the four convolutional units. The non-Gaussian rectifier linear unit (Relu) activation function works well with this method. The performance of the proposed models is tested using a new dataset called AS-Darmaset, which was created out of the two public online available databases. The two databases are Caltech 101 Objected Categories and Face Recognition Technology (FERET), respectively. This research compared the convergence behavior of the proposed Pre-activation batch Normalization CNN with that of three distinct CNN models. The Post Activation Batch Normalization CNN, Traditional CNN, Sparse Batch Normalization CNN. The experimental results show that the training and validation accuracy of the proposed Pre-activation BN CNN are up to 100.00% and 99.87%. Post Activation Batch Normalization CNN has an accuracy of 100.00% and 99.81% respectively. Traditional CNN has training and validation accuracy of 96.50%, 98.93% and Sparse Normalization CNN has accuracy of 96.50%. CNN has a training accuracy of 96.25% and a validation accuracy of 97.98%.This result illustrates the regularization effect of Pre-Activation-BN-CNN over the state-of-the-art for face recognition systems.