Original scientific paper

https://doi.org/10.17559/TV-20250217002383

Image and Signal Processing Approaches Applying in Developing Business Markets Affects Economic Growth

Meilin Wang ; School of Marxism, Central China Normal University, Wuhan, 430079, China
Wangyu Chen ; Wuhan Conservatory of Music, Wuhan, 430060, China *
Ziquan Lin ; Belarusian State Pedagogical University, Minsk, Belarusian

* Corresponding author.

Abstract

Emerging business markets gain strength through their establishment. However, as it grows stronger, there tends to be relatively less progress in science and technology. A variety of scientific and technological achievements have been introduced into business markets to address these issues, and in the process, they have undergone several modifications and driven abundant economic developments. This study explores how the application of image and signal processing methods in developing business markets impacts economic growth. This research breaks down the relevant aspects and focuses on highlighting the most significant outcomes. When expanding company marketplaces, two types of hypotheses emerge. This study concentrates on technology - based research: the first is product reduction, and the second is a manual method to check the availability of items. This research addresses both hypotheses. It introduces image processing methodologies and techniques to detect product reduction using the Retina Net algorithm and enhances them to handle numerous challenges brought on by Business Markets factors. To address these issues, this research report cites three primary technical approaches as follows. (1) The return on investment (ROI) method is used for corner selection. (2) The Retina Net algorithm is utilized to locate product reduction and misplaced products. (3) Finally, the Radio-Frequency Identification Technology (RFID) technique employs two fish algorithms to find product details. The proposed Twofish method requires 3548 Mbps, compared to the Advanced Encryption Standard (AES) with 1236 Mbps and blowfish with 2619 Mbps, all based on the values obtained in this study. As a result, the Two - fish method demonstrates superior performance to existing algorithms in terms of throughput, encryption, and decryption time parameters. Moreover, the proposed system, running on the Ubuntu 14.04 operating system, can recognize objects in 122 milliseconds or less. The results are presented as follows: the existing Dynamic Convolution Neural Network (DCNN), YOLOV3 (You Only Look Once Version 3), and Fast - RCNN (fast Region - based Convolutional Neural Networks) have average accuracies of 77%, 86%, and 88%, respectively, whereas the proposed Deep Retina Net achieved an average accuracy of 94%.This study offers actionable insights for policymakers to prioritize investments in AI and RFID infrastructure, fostering economic resilience.

Keywords

Advanced Encryption Standard (AES); business markets; radio-frequency identification technology (RFID); return on investment (ROI); twofish method

Hrčak ID:

335083

URI

https://hrcak.srce.hr/335083

Publication date:

30.8.2025.

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