With the progress of the times and the rapid development of science and technology, machine learning and artificial intelligence are increasingly used in transportation, logistics, and homes. In terms of pets, pet monitoring has also become very popular in recent years. In this study, a real-time monitoring system for home pets using raspberry pie is developed. The proposed method consists of a raspberry Pi based YOLOv3-Tiny identification system for rapid detection and better boundary frame prediction of the cat behavior. Based on the YOLOv3-Tiny method, the following fine-tuning is implemented:(1) The images collected using the mobile phones are uniformly cropped to 416*416 pixels. (2) The images were captured in different periods and randomly rotated to plus or minus 20 degrees to make the dataset more robust for training. (3) One thousand four hundred pictures of the cat's movements in the room for marking and training are used. The dataset is also used to train the YOLOv3 model. According to the input image categories, the results of the output are categorized into six cat actions. They were sleeping, eating, sitting down, walking, going to the toilet, and search for a trash can. The average accuracy of both models was 98%. Based on environmental constraints like speed and memory consumption, any one of these models can be used for image recognition. The raspberry Pi system continuously captures the images and instantly sends a message to the registered mobile phone to achieve an instant preventive measure if the cat goes to the toilet for too long or flips through the trash can.

No abstract

Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of the existing deep networks are based on or related to generative models and image classification. Many applications for RBMs have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer (50–750 layers). Then, we compare and analyze the classification performance in depth of regular RBM use RBM () function, classification RBM use stackRBM() function, and Deep Belief Network (DBN) use DBN() function with the different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compared to regular RBM.

No abstract