Trees provide a wide range of benefits to humans and other living organisms. An accurate method tree species identification will improve their management and conservation. Also, tree identification and description are crucial for genetic study, biodiversity conservation, management and regeneration strategies. The conventional methods of tree identification are time-consuming and requires a high level of expertise, necessitating development of a more efficient tree identification means. In this research, a QR code system for tree identification was developed. Tree data were collected from campuses of two tertiary institutions in Akure, Nigeria: Federal University of Technology and Federal College of Agriculture. System design was built around a three-tier architectural model. PostgresSQL was used as the Database System, the lowest tier. The Middle tier is the Web Server, Apache HTTP Server. Php 8. 1 was the scripting language that communicates with the database. For the Client tier, HTML, CSS and Javascript were used. The QR code generator was developed using PHP 8. 1. The PHP script used a QR code library to generate the QR code image. The QR code is linked to the website database containing all tree species information. The generated QR codes were attached to trees, and when scanned, the website is automatically launched and the tree information is retrieved. A survey was conducted to get end-users’ feedback within the study sites. The results obtained revealed that the QR codes are easy to use, and can make tree identification more interesting, thus increasing people’s knowledge about trees and improving Trees management.

Aim: The aim of this study was identify the needed elements in academic Textbooks’ s QR code based on point of view of professors. Methodology: The methodology of this research is qualitative and it is as a kind of practical research. At first, Interviews with professors were done using a semi-structured questionnaire on the criteria for selecting a university textbook and the important criteria for insertion of a QR code. In order to analyze the interviews, thematic content analysis technique was used. Results: A total of 21 elements were identified using the analysis of professorial interviews for the most important criteria for choosing an academic textbook. The most important element which is known is the author of the book element. And the most important elements needed to select a university textbook in order to be present at the QR Book code were a total of 43 elements. These elements are classified into five criteria of the book's author (7 elements), book publishing (10 elements), book appearance (9 elements), book content (14 elements), and the importance of the book in academic environment (3 elements). Originality: It seems that there is no research related to the subject of the QR code and academic text books. Most studies research on QR code in the library. Publishers of academic books can improve their marketing and distribution through the selection of the right elements through this research. However, consideration of the views of other stakeholders, such as librarians and users, is necessary for final conclusions.

In this paper, a procedure is reported that discuss how linear algebra can be used in image compression. The basic idea is that each image can be represented as a matrix. We apply linear algebra (QR ‎factorization and wavelet ‎transformation ‎algorithm‏s) on this matrix and get a reduced matrix out such that the image corresponding to this reduced matrix requires much less storage space than the original image.

Todays, barcodes play a significant role in various industries, and among the two-dimensional barcodes, the most famous one is QR code (Quick Response code) that has grown widely.The main purpose of this paper is to provide a noise-cancellation method based on a autoencoder deep neural network that can be used to restore distorted and illegible QRs to readability.To create noise and distortion, unlike other articles that used added simulated noise to the image, the challenge of extracting QR coded into a color image was used to collect more realistic data by collecting real-world dataset. therefore we Have more reliable estimation of proposed QRs noise-canceling method. As a result, we created a comprehensive data set of distorted QRs from three different watermark extraction approaches after the screen-camera attack. For the noise reduction process, three independent MCNN networks ( which is an upgrade from the U-net network) are used for each of the three extraction approaches,