Neural Technologies for Objects Classification with Mobile Applications

Authors

  • Ievgen Sidenko Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine https://orcid.org/0000-0001-6496-2469
  • Galyna Kondratenko Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine
  • Oleksandr Heras Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine
  • Yuriy Kondratenko 1) Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine 2) Institute of Artificial Intelligence Problems, Mala Zhytomyrs’ka Str., 11/5, Kyiv, 01001, Ukraine

DOI:

https://doi.org/10.13052/jmm1550-4646.2039

Keywords:

Neural technologies, objects classification, ResNet neural network, mobile application

Abstract

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.

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Author Biographies

Ievgen Sidenko, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Ievgen Sidenko is a Ph.D., Associate Professor, Associate Professor of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU), Ukraine. He has received master degree in speciality "Intelligent decision-making systems" (2010) at PMBSNU and Ph.D. degree in "Information technologies" (2015) at PMBSNU. His research interests include fuzzy sets and fuzzy logic, decision-making, optimization methods, neural networks, data mining, clustering and classification.

Galyna Kondratenko, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Galyna Kondratenko is a Ph.D., Associate Professor, Associate Professor of the Intelligent Information Systems Department, Senior Researcher at Petro Mohyla Black Sea National University, Ukraine. She is a specialist in control systems, decision-making, fuzzy logic. She worked in the framework of international scientific university cooperation during the implementation of international projects with the European Union: TEMPUS (Cabriolet), Erasmus + (Aliot) and DAAD-Ostpartnerschaftsprogramm (project with the University of Saarland, Germany). Her research interests include computer control systems, fuzzy logic, decision-making, intelligent robotic devices.

Oleksandr Heras, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Oleksandr Heras is a master’s student of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU). He received his bachelor's degree with a major in 122 "Computer Science" at PMBSNU. Oleksandr participated in the implementation of various projects related to the creation of mobile applications and management systems using machine learning technologies. Oleksandr is interested in studying machine learning and computer vision technologies.

Yuriy Kondratenko, 1) Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine 2) Institute of Artificial Intelligence Problems, Mala Zhytomyrs’ka Str., 11/5, Kyiv, 01001, Ukraine

Yuriy Kondratenko is a Doctor of Science, Professor, Honour Inventor of Ukraine (2008), Corr. Academician of the Royal Academy of Doctors (Barcelona, Spain), Head of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU), and Leading Researcher at the Institute of Artificial Intelligence Problems under MES and NAS of Ukraine. He has received (a) a Ph.D. (1983) and a Dr.Sc. (1994) in Elements and Devices of Computer and Control Systems from Odessa National Polytechnic University, (b) several international grants and scholarships for conducting research at Institute of Automation of Chongqing University, P.R.China (1988–1989), Ruhr-University Bochum, Germany (2000, 2010), Nazareth College and Cleveland State University, USA (2003), (c) Fulbright Scholarship for researching in USA (2015/2016) at the Dept. of Electrical Engineering and Computer Science in Cleveland State University. Research interests include robotics, automation, sensors and control systems, intelligent decision support systems, and fuzzy logic.

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Published

2024-05-06

How to Cite

Sidenko, I., Kondratenko, G., Heras, O., & Kondratenko, Y. (2024). Neural Technologies for Objects Classification with Mobile Applications. Journal of Mobile Multimedia, 20(03), 727–748. https://doi.org/10.13052/jmm1550-4646.2039

Issue

2024: Vol 20 Iss 3

Section

Control and Decision-making Systems with Mobile Applications

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