A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

E-Passport COVID-19 Adopting RFID Implants based on Microservices

COVID-19 has spread throughout the global and has restrained humans in all aspects of life including society, economy, education, etc. The first Corona virus appeared in China on 2019 and had mutated into several variants such as B.1.1.7 (Alpha), B.1.351 (Beta), E484Q (Delta), and BA.2 (Omicron). In order to block the virus to mutate and spread in the communities, we proposed a design of E-Passport by adopting RFID Implants with integrated Microservices technology. Our concepts is we design an android application (E-passport) that will be used by every institution on public places to stop the positive patients getting inside the public space to spread the virus. The checker in every public places need to scan the RFID implants on every humans hand by using NFC technology embedded on android phone with installed E-Passport to determine whether can enter or forbidden to enter. The RFID Implant stored a unique code that can be read as the reference of the person's data information stored in cloud database based on Microservices infrastructure. Our proposed design and architecture are dedicated to stop the COVID-19 virus to spread among public communities. © 2022 IEEE.

Design of epidemic detection system in railway station based on FPGA

Since the outbreak of COVID-19 in 2020, wearing masks and displaying green codes in and out of public places has become a habit. The identity verification of the existing railway station is mainly based on face recognition. Due to the outbreak and persistence of the epidemic, people often need to call out the green code on their mobile phones for staff verification, which takes a lot of time. At the same time, the existing face recognition equipment requires the inbound personnel to take off their masks, which also increases the infection risk of the inbound personnel. In order to reduce the above infection risk and speed up people's entry and exit speed, we have designed a system that can identify people wearing masks and judge whether they are confirmed or suspected cases at the same time. Firstly, the system measures the passenger's body temperature through the infrared temperature measurement module, carries out face detection and recognition at the same time, and queries the recognition results in the database to judge whether the passenger is diagnosed or in close contact. When the passenger is normal, it is allowed to pass, otherwise it is not allowed to pass, and updates the relevant data in the cloud database. The system uses Yolo algorithm as the face detection algorithm, and then carries out face recognition through FaceNet network, so as to judge its identity and query the relevant information of the person in the cloud database. After testing, the iterative loss rate of the system is basically below 0.1 and the accuracy is basically stable above 99%. Considering that we need to use it on embedded devices and the amount of calculation operation of deep learning algorithm is large, and FPGA can well build circuits according to the needs of the model because of its reconfigurability, and FPGA can realize hardware acceleration because it can run in parallel, so we finally choose to deploy the model to FPGA to complete face recognition. © 2022 IEEE.

Robo-Nurse Healthcare Complete System Using Artificial Intelligence

Significant with COVID-19 pandemic breakout, and the high risk of acquiring this infection that is facing the Healthcare Workers (HCWs), a safe alternative was needed. As a result, robotics, artificial intelligence (AI) and internet of things (IoT) usage rose significantly to assist HCWs in their missions. This paper aims to represent a humanoid robot capable of performing HCWs’ repetitive scheduled tasks such as monitoring vital signs, transferring medicine and food, or even connecting the doctor and patient remotely, is an ideal option for reducing direct contact between patients and HCWs, lowering the risk of infection for both parties. Humanoid robots can be employed in a variety of settings in hospitals, including cardiology, post-anesthesia care, and infection control. The creation of a humanoid robot that supports medical personnel by detecting the patient's body temperature and cardiac vital signs automatically and often and autonomously informs the HCWs of any irregularities is described in this study. It accomplishes this objective thanks to its integrated mobile vital signs unit, cloud database, image processing, and Artificial Intelligence (AI) capabilities, which enable it to recognize the patient and his situation, analyze the measured values, and alert the user to any potentially worrisome signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design of Campus Health Information System using Face Recognition and Body Temperature Detection

Currently under the epidemic crisis of COVID-19, campus epidemic prevention has become a hot topic, and temperature detecting equipment has become a necessity in public spaces. However, temperature detection systems that are widely sold on the market are relatively simple and cannot recognize personal identity. Besides, they cannot record individual temperature changes, and they are still inadequate in terms of managing personal health information. In this study, we proposed a system that can meet the needs of campus epidemic prevention called CHIS (Campus Health Information System). In CHIS, an infrared sensor is used for temperature detection and combined with face recognition. The body temperature is recorded while face recognition is performed, and the face ID and the collected real-time body temperature are transmitted to the cloud for viewing and managing by the school. The data will be managed centrally in the cloud and will be cleaned during daily processing. In the end, the student's health data history will be stored only in their personal Pod (Personal online datastore), a decentralized personal cloud data model that prevents the risk of large-scale data leakage due to centralized management. The combination of body temperature detection and face recognition avoids substituting the presence of a real person with photos or pictures, which further enhances security. It also reduces the risk of infection prompted by human detection, which increases safety. © 2021 IEEE.