The primary focus of this article was to employ Ultralytics technology, specifically YOLOv8, in object recognition. This involved utilizing supervised learning and other machine learning techniques. The article took into consideration the definitions of object detection and model training to effectively categorize solid waste, thereby facilitating recycling efforts. Following this, each object class was manually identified using the LabelImg tagger, considering the positions of the objects within the images. This approach led to the analysis of 1517 images and produced notably high-quality and significant results.

The increasing use of QR codes across various sectors has facilitated the transfer of information but has also exposed users to new cybersecurity threats, such as quishing, a variant of phishing that leverages these codes to redirect users to malicious websites. To address this issue, the study aimed to implement an antimalware system that employs machine learning alongside the VirusTotal API to analyze and classify links embedded in QR codes in real time. The methodology was structured into four stages: capturing and decoding QR codes using OpenCV, analyzing extracted URLs with the VirusTotal API, issuing preventive alerts based on the link classification, and evaluating system performance with a dataset of 100 QR codes (50 safe and 50 malicious). The results showed 100 % accuracy, 95 % sensitivity, and an average response time of 48.95 ms. No false positives were detected, and only a s...