ABSTRACT
The lives of pet animals are equally essential as how a human life. Pet owners and the veterinarian are responsible for providing good welfare for pets despite the problems. However, the rise of COVID-19 temporarily disturbs the veterinary services where some of them limit or stop their operations, resulting in the absence and difficulties for the pet owners to locate the available veterinarian, especially when there is an immediate need for treatment, vaccination, or consultation. Aside from that, setting an appointment and buying the pet's needs are seen to be a problem with regards to the situation since most of the pet owners are afraid to go outside because they might be infected with the virus. In line with this, TerraVet: A Mobile and Web Application Framework for Veterinary Clinics and Pet Owners is proposed to resolve the underlying dilemmas in administering and facilitating veterinary care. The main objective of this suggested project is to develop and design a platform where pet owners may locate their nearby veterinarian using the Global Positioning System (GPS) technology. In addition, the application enables the pet owner to arrange an appointment, product reservation, and online consultation. The veterinary clinic may post details regarding their offered services, products, and medicines. TerraVet will also design an electronic pet card to monitor their health status. © 2022 ACM.
ABSTRACT
Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.