An Artificial Intelligence URL Parser for Safer Web Browsing and Detection of Suspicious Links

With more than seven billion people actively using the Internet, the number of cyber attacks has increased, and personal data breaches have become a concern among the general public. The COVID-19 pandemic has only increased the use of online platforms and services for work and leisure activities, which opens the door to more scams, viruses, and other cyber security breaches. Guided by SEO techniques and research regarding dangerous website and domain patterns, we have designed and implemented a visual system that tracks suspicious links on an active webpage and marks them in order to alert users to proceed with caution. Our AI utilizes linear regression to best detect trends in URL parsing, comparing them with registered unsafe links to see if they pose similar threats. The results reveal that AI isn’t entirely accurate since some trends are hard to decipher; however, it can reliably flag certain redirects and out-of-domain links that would otherwise remain hidden to users.

Procrash: A Solution To Procrastination by Limiting Online Distractions using Optical Character Recognition

For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.

Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks

COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.