Cybersecurity awareness has a huge impact on individuals and an even bigger impact on firms, universities, and institutes to those individuals belong. Consequently, it is essential to explore and asses the factors affecting the awareness level of cybersecurity. More specifically this research study examines the impact of demographic features of individuals on cybersecurity awareness. The Studied literature’s limitations have been addressed and overcome in our research from the variability, and ambiguity aspects. A questionnaire was developed and responses were collected from 613 participants. Reliability and validity tests as well as correlations have been applied for the instruments and data employed in this study. Coefficients were calculated via multiple linear regression for the weights of each of the cybersecurity components. Data reliability test showed that Cronbach’s Alpha value of 0.707 for the used data which is acceptable for research purposes. Results analysis showed r-value for each of the questions is greater than the r table which was 0.07992. Examining the proposed hypotheses showed that there is a difference as the null hypothesis is rejected for one of the demographic features being tested namely, gender. While there is no significant difference when it comes to the other two factors, education level, and age. Using the weight for each of the components, password security, technical behavior, and social influence could provide a solid base for decision-makers to focus on and implement the available resources for gender-specific developments to raise the cybersecurity awareness level..
Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%.