Autors: Hristeva, T. H., Marinova, M. P., V. Lazarov.
Title: Deep learning model for object detection
Keywords: deep learning, algorithm, object detection, cnn

Abstract: Machine learning is entering in the everyday life of people in different forms. The reasons for this are the continuous development of computer systems, the increase of their computing power and the increase of data stored on electronic media. The main goals of developing self-learning models are to improve or replace existing methods for processing large amounts of information, to improve the services offered by different institutions, and generally to improve and facilitate the lifestyle of modern man. Machine learning can be used to detect complex relationships between a large set of input data, making it an appropriate method for solving a wide range of issues in different spheres such as Bioinformatics, Computer networks, Computer vision, Marketing, Medicine, Natural Language Processing (NLP) and many others.

References

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Issue

AIP Conference Proceedings, issue 2172, pp. 020001-1 - 020001-8, 2019, United States, https://doi.org/10.1063/1.5133483

Full text of the publication

Цитирания (Citation/s):
1. Vetova, S., Big data workflow platforms for science, (2021) AIP Conference Proceedings, 2333, art. no. 030008, 46th International Conference on Applications of Mathematics in Engineering and Economics, AMEE 2020, ISSN: 0094243X - 2021 - в издания, индексирани в Scopus и/или Web of Science
2. Vetova, S., Comparative analysis on CNN and wavelet features based technology for medical image classification, (2021) AIP Conference Proceedings, 2333, art. no. 030003, 46th International Conference on Applications of Mathematics in Engineering and Economics, AMEE 2020, ISSN: 0094243X - 2021 - в издания, индексирани в Scopus и/или Web of Science
3. Kaushik P., Chouhan P., Dasari N., Namdeo A.K., Kiran K., Yadav M., An In-Depth Comparative Analysis of Deep Learning Object Detection Models for Autonomous Vehicles: Performance Evaluation, Accuracy, and Real-Time Constraints, 2026, 2026 1st International Conference on AI Data Science Cyber Security and Smart Manufacturing for Sustainable Development Icadcs 2026, issue 0, pp. 721-725, DOI 10.1109/ICADCS70036.2026.11583746 - 2026 - в издания, индексирани в Scopus

Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus