This communication studies the importance of convective heat transfer is intensified remarkably in various disciplines of modern engineering sciences and technological development such as heat exchangers, refrigeration, air conditioning, food processing, damage to crops, and many more. The main focus of this study is to develop mathematical modeling of the 2D stagnation point flow of configured by an extended heated stretchable sheet subject to nonlinear thermal radiation with the revised nanofluid model. Moreover, the influence of Newtonian heating, MHD flow, and Brownian movement features are invoked for analysis. The essential nonlinear PDEs of this assessment are modeled with the aid of boundary layer theory and then renovated into nonlinear ODEs by invoking appropriate similarity solutions with help of MATHEMATICA 11.0 programming language. The physical insight of relevant flow parameters is highlighted through graphical illustration. Finally, this investigation greatly impacts engineering and industrial applications of the materials, mainly in geophysical and geothermal systems, storage devices, space science, and several other disciplines.

The back-propagation (BP) algorithm is usually used to train convolutional neural networks (CNNs) and has made greater progress in image classification. It updates weights with the gradient descent, and the farther the sample is from the target, the greater the contribution of it to the weight change. However, the influence of samples classified correctly but that are close to the classification boundary is diminished. This paper defines the classification confidence as the degree to which a sample belongs to its correct category, and divides samples of each category into dangerous and safe according to a dynamic classification confidence threshold. Then a new learning algorithm is presented to penalize the loss function with danger samples but not all samples to enable CNN to pay more attention to danger samples and to learn effective information more accurately. The experiment results, carried out on the MNIST dataset and three sub-datasets of CIFAR-10, showed that for the MNIST dataset, the accuracy of Non-improve CNN reached 99.246%, while that of PCNN reached 99.3%; for three sub-datasets of CIFAR-10, the accuracies of Non-improve CNN are 96.15%, 88.93%, and 94.92%, respectively, while those of PCNN are 96.44%, 89.37%, and 95.22%, respectively.

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