The important thing in the field of deep learning is to find out the appropriate hyper-parameter for image classification. In this study, the main objective is to investigate the performance of various hyper-parameters in a convolutional neural network model based on the image classification problem. The dataset was obtained from the Kaggle dataset. The experiment was conducted through different hyper-parameters. For this proposal, Stochastic Gradient Descent without momentum (SGD), Adaptive Moment Estimation (Adam), Adagrad, Adamax optimizer, and the number of batch sizes (16, 32, 64, 120), and the number of epochs (50, 100, 150) were considered as hyper-parameters to determine the losses and accuracy of a model. In addition, Binary Cross-entropy Loss Function (BCLF) was used for evaluating the performance of a model. In this study, the VGG16 convolutional neural network was used for image classification. Empirical results demonstrated that a model had minimum losses obtain by Adagrad optimizer in the case of 16 batch sizes and 50 epochs. In addition, the SGD with a 32 batch sizes and 150 epochs and the Adam with a 64 batch sizes and 50 epochs had the best performance based on the loss value during the training process. Interestingly, the accuracy was higher while performing the Adagrad and Adamax optimizer with a 120 batch sizes and 150 epochs. In this study, the Adagrad optimizer with a 120 batch sizes and 150 epochs performed slightly better among those optimizers. In addition, an increasing number of epochs can improve the performance of accuracy. It can help to create a broader scope for further experiments on several datasets to perceive the suitable hyper-parameters for the convolutional neural network. Dataset: https://www.kaggle.com/c/dogs-vs-cats/data

Rare earth magnets with excellent magnetic properties are indispensable in the electric device, wind turbine, and e-mobility industries. The demand for the development of eco-friendly recycling techniques has increased to realize sustainable green technology, and the supply of rare earth resources, which are critical for the production of permanent magnets, are limited. Liquid metal extraction (LME), which is a type of pyrometallurgical recycling, is known to selectively extract the metal forms of rare earth elements. Although several studies have been carried out on the formation of intermetallic compounds and oxides, the effect of oxide formation on the extraction efficiency in the LME process remains unknown. In this study, microstructural and phase analyses are conducted to confirm the oxidation behavior of magnets pulverized by a jaw crusher. The LME process is performed with pulverized scrap, and extraction percentages are calculated to confirm the effect of the oxide phases on the extraction of Dy during the reaction. During the LME p rocess, Nd i s completely e xtracted a fter 6 h, w hile D y remains as D y2Fe17 and Dy-oxide. Because the decomposition rate of Dy2Fe17 is faster than the reduction rate of Dy-oxide, the importance of controlling Dy-oxide on Dy extraction is confirmed.