COVID-19 Prevention Systems within Hardware and Software Design Computer Science Courses

This paper proposes pandemic support system design exercises from both hardware and software perspective as constituent part of higher education computer science courses. Two case studies in context of computer science and automation study programmes at University of Niš, Faculty of Electronic Engineering in Serbia ae covered: Intelligent Information Systems and Microcontroller Programming. In case of the first one, the topics cover information system implementation relying on Java Enterprise Edition (JEE) technology with presence of machine learning elements provided by Weka API, so smart vaccination process support information system is presented as example. On the other side, the focus on the second course is on PIC16 family microcontrollers and RTOS-based system implementation using CCS C compiler and presented example represents control unit for indoor coronavirus safety control. © 2022 IEEE.

Covid Prevention System for Face Mask Detection and Temperature Sensing

Currently, the world is experiencing a serious medical crisis as a result of the Corona virus COVID-19, which now has swept the globe. For several countries, combating this disease outbreak has become an unfortunate reality. Wearing a face mask when going outside or meeting with others is essential for prevention. Some irresponsible people, on the other hand, refuse to wear face masks for a variety of reasons. The development of the face mask detector too is critical in this case. To address this problem, a reliable face mask detector must be created. A face mask can be detected using the object detection algorithm. The mask detection algorithm used to detect the face mask was Haar Cascade in OpenCV from Python. According to the results of the experiments, this device can detect whether or not someone is wearing a face mask and can also measure body temperature. Once these validations are completed automatically door gets opened and sanitization is done. © 2022 IEEE.

Design Suggestion of Epidemic Prevention System for Shared Car Based on Scenario and Data

Entering the post-epidemic era, the travel demand for shared cars is increasing day by day. In the normalized epidemic prevention and control, epidemic prevention in shared cars needs to be designed systematically. This paper analyzes the existing risk of COVID-19 propagation based on two perspectives: scenario and data, and discusses the existing means of protection. Then based on the existing measures, the design suggestions are given from two aspects: scenario-based and data-based. Based on the scenario, the layout design and disinfection is implemented in regard to various ways that COVID-19 is transmitted;based on data, travel data integration should be promoted to achieve macro-structural dynamic adjustment and integrated governance from the overall transportation system. In the context of the industries, the shared car industry should response to new trend immediately and implement innovative ideas to obtain a service that is better suited for individuals in the post-epidemic era. In the end, several major functions of design in terms of developing the urban transportation system are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

lIoT enabled Covid Prevention System with YOLOv4

The covid-19 pandemic has affected both the health and lifestyle of the people, not only this the global economy also affected badly. The virus spreads at a very high rate and people can easily be infected. So for that people have to take a vaccine that is provided by their government but only vaccination is not a complete solution to this virus. It doesn't gives a full guarantee to prevent people's lives even after the vaccination, so we have to defend ourselves from the spread of the viruses as much as possible. For that mask and social distancing are the main key factor that is also recommended by the government and the public health agencies. As people are not habitual of wearing the mask so many times people forget to wear a mask in public places which is one of the main reasons for the spreading of the virus. Sometimes we see that at crowded places like metro stations and malls and universities, two or three guards are always present, to check the thermal temperature and if people are wearing masks or not and telling people to maintain a social distance. So there are a lot of problems in this because the metro station is a crowded place there people have to make a queue for checking their temperature so it is somehow hard to maintain social distancing and if there is an infected person found in the queue then all other surrounded people will also become infected. So we decided to solve this problem by contributing to the public health sector by making a complete system that will check if people are wearing a mask on their face properly or not. It will also check the thermal temperature of the people through the cameras and checks if someone is not violating social distancing rule. This will prevent people from infected people and also save the time of people. Now a maximum of one guard is needed for monitoring. To make this project practically we are taking the help of machine learning and deep learning. We will be using face detection and recognition algorithms that will be detecting the faces of people. We are using python as programming language. For face detection, we are using YOLO v4(You look only once) which supports a Convolutional neural network. So our process flow will be like that first we import all libraries and after that, we will build a neural network and after that training will be done on a model and then testing the model. After that system will become ready to deploy on the cloud. © 2022 IEEE.

Utilizing ML and DL Algorithms for Alert Classification in Intrusion Detection and Prevention Systems: A Detailed Review

Intrusion detection/prevention systems have attracted much interest in recent years due to increased online connectivity. In recent years due to COVID pandemic and due to the increased number of online users, online data has become more and more exposed to different types of attacks. Hence, in order to keep data safe, it has become quite important to detect/prevent such attacks. An IDS is a sensor that is used for the observation of such attacks on the nodes or the network itself, and in this way, it tries to keep the information safe from possible attacks. However, accurately identifying such attacks so that they can be prevented effectively is a concern. This accuracy is measured by the number of false positive & false negative in a dataset. These days ML/DL algorithms are being significantly utilized for improving the accuracy of different systems (e.g., health care, stock market, forecasting etc.). Considering its importance, the work presented here studies the impact of using ML/DL algorithms on the accuracy of IDS/IPS. The impact of these algorithms is studied by using evaluation metrics for classification of network assaults in the intrusion detection system using different datasets. These algorithms are subject to further changes for improving the accuracy parameters based on evaluation metrics. © 2022 IEEE.

Intrusion Detection System for Local Networks - A Review Study

Cyber security is the implementation of smart technologies to safeguard computer systems, mobile devices, communication networks or most importantly the sensitive and confidential data saved in those systems or devices from various types of cyber-attacks, unauthorized access, hackers or intruders. Cyber security can also be considered as a subset of information security because information security is a general term. It aims to protect a wider domain which includes all kinds of information assets either hard copy or soft copy. The recent accelerating rise in digitalization due to Covid-19 has brought in many new challenges. The amount of personal data present on the web due to the same has raised concerns among users. However, it's not only the personal data that is a matter of concern but also the dataset which is given as input to numerous machine learning and deep learning models. Local networks are prone to attacks and intrusion activities now more than ever. As a result, cyber security experts have been working on the development of more complex monitoring systems and algorithms for the detection and prevention of such activities. Various technologies like machine learning and deep learning might play a significant role in improving cyber security. It can help in analyzing patterns and improving the models for recognizing similar attacks in future. This research work aims to study intrusion detection systems in detail and differentiate between intrusion detection systems, intrusion prevention systems and firewalls as IDS and IPS are commonly regarded as the same thing. It also highlights the previous works related to this subject along with their suggested methods. © 2022 IEEE.

Wearable Technology based Face Alert System to Curb the Spread of Covid-19

Increasing people's perception of their habitual face-touching behaviour and ameliorating their acknowledgment of self-inoculation as a medium of transmission may assist to curb the spread of novel coronavirus (COVID-19). On average, human beings generally touch their faces 23 times per hour. Therefore, hand hygiene is an essential preventive measure to stop the spread of COVID-19. This motivates to introduce an alert mechanis m using wearable technology that aims to alert a person whenever he/she brings his/her hands close to the face. The proposed face alert system is based upon deep learning technique to forecast hand movements followed by face touching and imparts sensory response to alert end-user to stop the face touching activities. The proposed system employs IMU to get features belonging to different hand movements resulting in face touching. The data can be effectively classified using CNN where the filters help in extracting temporal features from IMU data. The prediction model based upon CNN is developed with training data from four thousand eight hundred trials recorded from forty participants. The trained dataset of hand movements activities is collected during day-to-day activities, e.g., walking, sitting, etc. Results demonstrated a forecast accuracy of 90% is obtained with 550ms of IMU data. In a research study, the psychophysical experiment is conducted to compare the response time for sensational observation methods, e.g., auditory, visual and vibrotactile. It has been observed that the response time is remarkably higher for visual (VF) and auditory feedback (AF) in comparison to vibrotactile feedback (VTF). Moreover, the rate of success is analytically lesser for visual feedback compared to vibrotactile and auditory feedback. Practically, results indicate a prediction of the movement of hand, and timely generation of sensational response in less than a second, so that one does not touch the face, and thus curbing of the spread of COVID-19. © 2022 IEEE.

A Wearable Warning System Design for Mask Recognition via AR Smart Glasses

Under the influence of COVID-19, various studies have shown that the most important transmission of the epidemic is droplet infection, it is the most effective way to control the epidemic by wearing a mask in a safe range. To confirm the situation of masks-wearing in public, a useful way is to use image-recognition technology to detect the people in the field. On the other hand, with the continued development of wearable devices, smart glasses have been widely used in many files such as handicapped person support. Based on the previous researches, it is already possible to incorporate facial recognition technology into smart glasses. Especially, the application of Augmented Reality (AR) technology on smart glasses can provide users with a lot of additional information, for example, to highlight the targets who been identified. Therefore, to identify the people who are not wearing masks more effectively, in this paper we try to design and wearable mask recognition warning system by using the AR smart glasses. The system can supply the warning messages about the person without masks in both visual and auditory way to the user to support the users including the handicapped persons who not being able to hear or see. The results of this study may provide guidelines to develop the epidemic prevention system and offers useful insights for the supporting of handicapped persons. © 2021 IEEE.

Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2ĝ., which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work. © 2021 ACM.