Machine learning activity detection using ML.Net

Alexan, Anca; Alexan, Alexandru; Oniga, Stefan

doi:10.1109/siitme50350.2020.9292294

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, it was possible to collect a dataset of photos for training a neural network, the size of which is 802 photos for all 4 types of aircraft. First, a model was created and trained in the ML.Net environment [3,6,[51][52][53], and a web application was developed using Visual Studio 2022 to test the created neural network. A machine learning model was added to the created web application and the "image classification" scenario was selected in it.…”

Section: The Software Of the Developed System And The Results Of Mode...mentioning

confidence: 99%

Neural Technologies for Objects Classification with Mobile Applications

Sidenko,

Kondratenko,

Heras

et al. 2024

JMM

View full text Add to dashboard Cite

This paper is related to the study of the features of the neural technologies’ application, in particular, ResNet neural networks for the classification of objects in photographs. The work aims to increase the accuracy of recognition and classification of objects in photographs by using various models of the ResNet neural network. The paper analyzes the features of the application of the corresponding models in comparison with other architectures of deep neural networks and evaluates their efficiency and accuracy in the classification of objects in photographs. The process of data formation for training neural networks, their processing and sorting is described. A web application and a mobile application for recognizing and classifying objects in a photo were also developed. A system for classifying objects, in particular airplanes in photographs, was developed using neural network technologies. It gives a recognition and classification accuracy of about 95%. Research results of ResNet models are of great practical importance, as they can improve the classification accuracy of various images. Features of ResNet, such as the use of skip connections or residual connections, make it effective in the relevant tasks. The results of the study will help to implement ResNet in various fields, including medicine, automatic pattern recognition and other areas where the classification of objects in photographs is an important task.

show abstract