The rapid digitalization of healthcare records has accelerated adoption of automated healthcare solutions. But healthcare records associated with patients can reach orders of petabytes in a few years. Therefore, to completely harness the full potential of digital healthcare technologies, scalable yet secure storage solutions are needed. Tamper-proofing medical records on decentralized storage, paired with data encryption can solve this problem. This paper proposes a working solution for this purpose using Storj as the underlying decentralized storage technology. In addition, a combination of Dynamic AES and AES-GCM is used for lossless content encryption. The viability of the proposed solution is discussed using quantitative and qualitative methods.

Machine literacy refers to the creation of digital mechanisms which could make significant contributions without formal training by interpreting as well as extrapolating from attack patterns using mathematical techniques from various models. It's research wherein the system can improve itself autonomously as a consequence of the interaction and data. It's an example of artificial intelligence in action. In this study, we'll look at recognizing bike riders wearing or not wearing helmets in a video. To get the movies back into circulation, we'll have used the OpenCV Software. The YOLO and CNN models, that are utilized for enabling real-time license plate retrieval for a non-helmeted rider, the above two models are indeed the prescribed strategy for our device. The technology in our approach looks to see if the user who is riding the bike is wearing a helmet. If the rider is not wearing the required protective gear, the license plate is nonetheless extracted so that it could be supplied to activity recognition technology for evaluating and giving the fines to the prosecutor. For this design, we have a truly vast compass. It can be used to detect bikers who are not wearing a helmet. If the individual in the video is not wearing a helmet, the system is designed to detect the license plate of the bike and send an appropriate alert message to the appropriate person. This system is found to be more robust and effective than other algorithms.