Browsing by Author "Attia, Ayman"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Blockchain for tracking serial numbers in money exchanges
    (WILEY, 2019) Mohamed, Kareem; Aziz, Amr; Mohamed, Belal; Abdel‐Hakeem, Khaled; Mostafa, Mostafa; Attia, Ayman
    Money exchange is one of the most common day‐to‐day activities performed by humans in the daily market. This paper presents an approach to money tracking through a blockchain. The proposed approach consists of three main components: serial number localization, serial number recognition, and a blockchain to store all transactions and ownership transfers. The approach was tested with a total of 110 banknotes of different currency types and achieved an average accuracy of 91.17%. We conducted a user study in real‐time with 21 users, and the mean accuracy across all users was 86.42%. Each user gave us feedback on the proposed approach, and most of them welcomed the idea
  • Thumbnail Image
    Item
    Human Activity Recognition in Car Workshop
    (Science and Information Organization, 2022-05) Magdy, Omar; Attia, Ayman
    Human activity recognition has become so widespread in recent times. Due to the modern advancements of technology, it has become an important solution to many problems in various fields such as medicine, industry, and sports. And this subject got the attention of a lot of researchers. Along with problems like wasted time in maintenance centers, we proposed a system that extracts worker poses from videos by using pose classification. In this paper, we have tested two algorithms to detect worker activity. This system aims to detect and classify positive and negative worker's activities in car maintenance centers such as (changing the tire, changing oil, using the phone, standing without work). We have conducted two experiments, the first experiment was for comparison between algorithms to determine the most accurate algorithm in recognizing the activities performed. The experiment was done using two different algorithms (1 dollar recognizer and Fast Dynamic time warping) on 3 participants in a controlled area. The one-dollar recognizer has achieved a 97% accuracy compared to the fastDTW with 86%. The second experiment was conducted to measure the performance of a one-dollar algorithm with different participants. The results show that a 1 dollar recognizer achieved an accuracy of 94.2% when tested on 420 different videos. © 2022. All Rights Reserved.