In recent decades, the quantity of fake educational certificates has increased, damaging testing centers’ reputations. The most common verification methods include email, phone, and proxy for background checking, which require additional costs, security, and time. As these processes are not transparent enough, it is possible to create counterfeit certificates easily. Blockchain- based solutions have basic features such as security, proof of disruption, transparency, and compliance, which give a secure network the ability to store only genuine certificates. Additionally, data history can be managed on a blockchain network in case of suspicious activity. In this project, we analyzed blockchain technology and established a blockchain-based education certification system for the issuance and certification of certificates using the hash algorithm technology for encryption and confidentiality of educational certificates to prevent disruption and copyright restrictions. The InterPlanetary File System, a peer- to-peer based decentralized storage, is used to store the soft copy of the certificates by generating a hash for each certificate. This study offers Hyperledger Fabric-based application development concepts, including the processing of assets and generating immutable transactions, to create a comprehensive private blockchain-based educational certificate management.
Canadian Association of General Surgeons (CAGS) Canadian Association of Thoracic Surgeons (CATS) Canadian Hepato-Pancreato-Biliary Association (CHPBA) Canadian Hernia Society (CHS) Canadian Society of Colon and Rectal Surgeons (CSCRS) Canadian Society of Surgical Oncology (CSSO) Trauma Association of Canada (TAC) Canadian Obesity Network (CON) Canadian Association of Bariatric Physicians and Surgeons of Canada (CABPS) FINAL PROGRAM SCHEDULE LEGEND
With the advancement of medical technology, the improvement of healthcare requires the integration of cutting-edge technology with medical science to make an effective system. Medical care has increased significantly in recent years as a consequence of technological breakthroughs. A fundamental demand for any hospitalized patient includes advanced care and continuous monitoring. Intravenous (IV) treatment is the fastest and most common way of treatment since it skips the digestive system. When saline is supplied to a patient, the patient is administered it continuously by a nurse. Unfortunately, the nurse could forget to turn off the saline injection system for a variety of reasons, including hectic schedules, forgetfulness, or sleeping at night. The saline tubing system is then re-flexed by the blood of the patient, endangering their lives. Additionally, air bubbles can enter the patient's body. Consequently, there is a necessity for an automated system for assuring the continual monitoring and regulation of the fluid flow. To answer this, we have developed a remotely monitored saline level and automated alarm system to keep patients safe and preserve their lives during saline feeding hours by employing a microcontroller, load cell, switching valve, and GSM module. The proposed system has a weight measuring sensor that monitors the weight of the saline bottle and processes that weight via a microcontroller unit, which subsequently signals when the saline bottle is empty and automatically shuts off the saline flow when it is completely empty. This system is highly beneficial for accurate intravenous treatment, especially at night.
COVID-19 pandemic causes a global catastrophe that remarkably affects individual lives and society, as well as the economy. The world has taken numerous defenses against these contagious diseases and the use of facemasks is one of the most crucial defense mechanisms. However, this preventative strategy is fruitful if the face masks are worn correctly. Unfortunately, only less than 25% of people use masks correctly. In this paper, we proposed to apply a most recent deep learning-based image classification model named vision transformer to automatically detect improper face mask-wearing, i.e the facemasks are being worn correctly or not. In contrast with the traditional image processing or deep learning model, the vision transformer base model shows a significant impact on incorrect face mask detection. We did the experiment on a large custom dataset consisting of 2,03,780 digital images with 3 class labels namely With Mask (when people are wearing the mask properly), Without Mask (when people are not wearing the mask) and Incorrect Mask (when people are not wearing the mask properly) to train, validate and test vision transformer model that can classify the use of masks correctly. The results show that the accuracy achieved with pre-trained vision transformer models is highly remarkable.
In the process of image restoration from images corrupted with non-Gaussian noise, the nonlinear filters perform superior over linear filters, and over the conventional sequential demosaicking and denoising methods, the parallel demosaicking cum denoising is highly efficient and fast. These assumptions in this paper are analysed, implemented, evaluated and established objectively by the authors. Most the research is found to be non pragmatic as the restoration of the images from non-Gaussian noise infected images is rarely considered. Hence the authors, by implementing sixteen nonlinear filter techniques and one linear filter technique, reconstructed the color from the images infected with Gaussian and Non- Gaussian Noise.Objective performance metrics MMSE, CPSNR, and SCIELAB are obtained and compared for objective performance evaluation, and the reconstructed images are also presented for the viewers’ subjective evaluation. The standard image dataset is used for implementation of the proposed nonlinear filter based parallel demosaicking cum denoising of non-Gaussian noise infected images.
