Gamification offers an innovative approach to learning by increasing user-interactivity, motivations and real-world skill development. This paper investigates the existing gamification methods in cybersecurity education and introduces a gamified cryptography teaching tool designed to explore the impact of such techniques. The paper discusses design, development and implementation of the application, along with preliminary evaluation results based on user feedback and engagement metrics. The findings suggest that gamification can significantly address the limitations of traditional teaching methods. Additionally, future enhancements and potential integration into broader cybersecurity education programs are outlined.
Existing studies on web scraping security primarily focus on large-scale platforms or generic detection mechanisms. In contrast, this study provides an empirical security assessment of WooCommerce-based SME websites, demonstrating how predictable structural patterns and sitemap exposure enable high-volume data extraction under real-world conditions.
This project presents a smartphone-based approach for estimating Grain Moisture Content (GMC) using image processing and machine learning techniques. Images of grain samples were analysed to extract colour-based features, which were used to train regression models for GMC estimation. An Android application developed using Python was implemented to enable real-time, non-destructive GMC assessment on smartphones. The proposed method offers a low-cost and practical alternative to conventional laboratory-based moisture measurement, supporting timely harvest decision-making in agricultural settings.
This project introduces a spam message filtration system utilizing logistic regression, a powerful machine learning technique. By preprocessing text data and extracting relevant features, the model accurately distinguishes between spam and legitimate messages. Evaluation reveals strong performance metrics, highlighting the system's effectiveness in bolstering digital communication security. Leveraging logistic regression, this solution offers a practical means to combat spam, with potential integration into diverse communication platforms for proactive mitigation.
Through meticulous preprocessing and feature extraction, this project employs logistic regression to develop a robust spam message filtration system. Evaluation results demonstrate its efficacy in accurately identifying spam while minimizing false positives. By leveraging the strengths of logistic regression, this approach presents a practical solution for enhancing digital communication security, poised for seamless integration into various platforms to bolster spam detection and prevention efforts.
The goal of this study is to find solutions to many such fluid flow issues in curve shaped cavities containing a wavy cylinder inside. The model is examined when the bottom wall is partially heated, as well as the upper wall. COMSOL Multiphysics, MATLAB and Techplot were used in this project to develop the model.
In this study, an evaluation of the influences of internal heat absorption in a closed cavity is performed. The fluid is considered laminar and the convection is generated because of the buoyancy force only. For this natural convection problem, a set of dimensionless governing equations is disclosed. Then the solution of this problem is generated by using the Galerkin weighted residual method of finite elements. A wide range of dimensionless parameter eg. Reynolds number, Hartmann numbers etc , were introduced for better understanding of the fluid flow. The model is evaluated for different values of these dimensionless parameters to understand the influence of it to the fluid flow. The study concludes that certain parameters alter and influence the flow field, transfer of heat and temperature distribution significantly.