Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

This paper focuses on the training of a deep neural network regarding danger sign detection
and recognition in a substation. It involved applying the concepts of neural networks and …

This study presents a novel approach for kernel selection based on Kullback–Leibler
divergence in variational autoencoders using features generated by the convolutional …

Physical education curriculum has been paid more and more attention by teachers and
parents, and having a healthy body is the foundation. School sports competition is also more …

Edge computing offers promising solutions for challenges related to latency, connectivity,
scalability, cost, and privacy. However, the resource requirements of deep learning networks …

Convolutional Neural Networks (CNN) are widely used to face challenging tasks like speech
recognition, natural language processing or computer vision. As CNN architectures get …

При обучении нейронной сети выбор ее архитектуры обычно стараются соотнести с
достижением низкой суммарной ошибки. В данной работе представлен метод выбора …

In a convolutional neural network (CNN), overparameterization increases the risk of
overfitting, decelerates the inference, and impedes edge computing. To resolve these …

This work proposed kernel selection approaches for probabilistic classifiers based on
features produced by the convolutional encoder of a variational autoencoder. Particularly …

Internet of Things (IoT) applications see increasing adoption in various fields, such as
autonomous vehicles and home appliances. Notably, intelligent IoT applications require …