ABSTRACT
With the recent global spread of the COVID-19 (also known as the corona virus) pandemic, several governments have attempted to control its transmission through preventive and precautionary measures. Education is one of the factors that has been impacted by the pandemic. As a result, to limit the spread of the virus, many countries adopted distance education instead of traditional education to ensure the continuity of the educational process. Cloud computing is a technology that offers numerous advantages in the field of education. The Kingdom of Saudi Arabia was one of the countries that had decided and continues to use various cloud platforms for distance education. In this study, we look at how effective cloud computing platforms are in the learning process in Saudi Arabian schools. The primary goal of this research was to investigate the teacher's ability to access different cloud computing services, as well as their ease of use and utility, by evaluating the effectiveness of these platforms as a mode of teaching before and after the pandemic. A total of 559 male and female schoolteachers' data was collected using self-administered questionnaires in Al-Bahah region and was analyzed using the IBM SPSS Statistics software. The analysis of this study expanded our understanding on the possibility of using educational platforms across schools in the kingdom. The findings also revealed that the use of cloud platforms during the pandemic increased by 28% in the region which have now become integral part of education. Furthermore, the findings revealed that teachers frequently encountered difficulties in implementing cloud-based educational processes, particularly in rural and mountainous areas. © 2023 IEEE.
ABSTRACT
The covid-19 epidemic is causing a world pandemic crisis. The powerful device in these situations is to wear a mask in public entry, schools, and super markets to reduce the Covid-19 spread. There are many convolutions face recognition technologies to distinguish effective images for monitoring the discovery of a face mask. Therefore, it is very important to improve the effectiveness of the acquisition methods available in the existing system. The data set value increases in the proposed input to improve the maximum accuracy. The proposed method is used to determine body temperature, face mask, and social retention using advanced machine learning methods. Using the EM8RFID scanner personal data such as temperature value, face mask identification and public distance detection are collected. It is used to indicate the state of human health in a cloud platform. A wireless heat sensor issued to determine a person's body temperature using MLX90614 without anyone. The Raspberry integrated with the pi camera is used in detecting a face mask and a social distance. Raspberrypi captures the image and detects with the convolution neural network algorithm verifying a person is wearing a face mask, following social distance. Therefore, authorities should monitor the human condition in the cloud platform area. By applying this concept, the spread of Covid-19 can be greatly reduced and it is easier to identify peoplewith Covid-19symptoms. © 2022 IEEE.
ABSTRACT
This paper presents a development of internet of things (IoT)-based indoor air quality monitoring system. The system is purposed to monitor quality of air in offices. It is to assure the health and safety of the working place which is especially being a big concern during the COVID-19 pandemic. Implementing the IoT concept allows to do monitoring from anywhere at anytime. A prototype of the monitoring system is built using three major components, such as an air quality sensor (BME680), a microcontroller (NodeMCU ESP-12), and an IoT cloud platform (ThingSpeak). The experimental test result shows that system was able to monitor the air temperature, air humidity, air pressure, IAQ (indoor air quality) index, carbon dioxide quantity, and VOC (volatile organic compound). These data is presented real-time in a web application and accessible from anywhere by using computers or smartphones. © 2022 IEEE.
ABSTRACT
Introduction: To meet the multi-user, cross-time-and-space, cross-platform online demand of work, and professional training teaching in nuclear reactor safety analysis under the normalization of Coronavirus Disease 2019. Method: Taking the nuclear accident simulation software PCTRAN as an example, this study adopts cloud computing technology to build the NasCloud, a nuclear accident simulation cloud platform based on Browser/Server architecture, and successfully realizes multi-user, cross-time-and-space, cross-platform applications. Targeting the AP1000, a pressurized water reactor nuclear power plant, the simulation of cold-leg Small Break Loss of Coolant Accident and cold-leg Large Break Loss of Coolant Accident were carried out to verify the correctness of the NasCloud's accident simulation function. Results: The result shows that the simulation functions and results of the NasCloud in multi-terminal are consistent with the single version of PCTRAN. At the same time, the platform has high scalability, concurrency and security characteristics. Discussion: Therefore, the nuclear accident simulation cloud platform built in this study can provide solutions for the work and training of nuclear reactor safety analysis, and provide reference for other engineering design and simulation software cloud to computing transformation. Copyright © 2023 Chen, Chen, Xie, Xiong and Yu.
ABSTRACT
Objective To analyze the prescription rules for pestilence in ancient books of case records, and provide reference for treatment of coronavirus disease 2019 (COVID19);Methods The medical cases of warm diseases in ancient times were selected as the source before data extraction rules were made. TCM Miner was used to conduct the counts and analysis of association rules, and Cloud Platform of Ancient and Modern Medical Cases was used for complex network analysis. Results A total of141 medical cases were found in the 14 ancient books of case records, involving 66 formulae and 142 Chinese medicinals. The formulae mainly included Xiao Chaihu Tang (Minor Bupleurum Decoction), Dayuan Yin (Membrane Source-Opening Beverage), and JiuweiQianghuo Tang (Nine Ingredients Notopterygium Decoction), while the medicinals mainly included Lianqiao (Fructus Forsythiae), Fuling (Poria), and Shichangpu (RhizomaAcoriTatarinowii). Conlusion The prescriptions against pestilence in ancient times highlight clearing heat and toxins, cooling the blood, resolving dampness and opening the orifices, which is also combined with releasing the exterior. © 2022 IEEE.
ABSTRACT
A new wave of the Covid-19 pandemic struck Hong Kong in February 2022. It led to construction of a temporary 1000-bed hospital and 10 000-bed isolation and treatment facility on an island site in just 51 days using factory-made modules. To achieve such rapid construction, module assembly was carried out at a separate site between the factories and site. Several new modular construction technologies were also developed, including adjustable base supports, large-span roof modules, universal safety barriers and an intelligent cloud platform for construction management. But to enable sustainable construction of such emergency buildings in future, further studies on demolition, recycling and relocation of modular buildings need to be carried out in the post-pandemic era. © 2022 ICE Publishing: All rights reserved.
ABSTRACT
Today, Cloud Computing is a distributed system environment. These days the services are available pay as you go model. Cloud users are paying as per their services in the cloud environment. The services available to the Cloud users are Infrastructure as a service, platform as a service, software as a service and security as a service. Nowadays, most users are migrating to cloud platforms. In Covid-19 pandemic situation, most large and small scale organizations operating their business using cloud platforms. On the other end due to industrial automation, the companies switched their operations to a cloud environments. Due to the rapid business migration, the demand for cloud computing increased. With the increase of demand in the cloud, the service providers are satisfied. On the other end, a challenging issue is resource allocation. The best resource allocation strategy will provide quick services to the cloud users and minimum cost to the cloud providers. In this paper, we will discuss, resource allocation procedure, the throttled load balancing algorithm and the results are compared with other resource optimization techniques. © 2022 IEEE.
ABSTRACT
Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has forced universities world-wide to adjust to an online delivery model resulting in many unforeseen challenges for module delivery and student engagement. The Glasgow Caledonian University (GCU) Cloud Platform Development (CPD) final year undergraduate module requires hands-on lab work and coursework, and thus call for more deliberation and interventions to maintain student engagement. The module teaching material created at GCU is also taught at the African Leadership College (ALC), Mauritius using a flipped classroom model. However, for the current academic year, the ALC campus was closed, with the enrolled students spread over many countries and time zones in Africa, resulting in a compounding of the challenges for maintaining student engagement. In this study we describe the approaches and techniques employed at increasing the students' engagement for the CPD coursework. © 2021 ACM.
ABSTRACT
In the recent years, especially after covid-19 became a thing, people started finding information about Retail On- Demand Services online rather than relying on local contacts. This makes it a lot easier for people to find and book services online, rather than going out and booking them in person. So, a Django-based Web application with a real time database deployed on cloud that provides details of some service providers (Carpenters, Electricians etc.) in region of Kanuru (Vijayawada, India) has been designed. Data are collected from multiple sources using a scraping code that Beautiful Soup framework is used and stored in PostgreSQL database that is later deployed on Cloud platforms. This helps people from this region to access information quickly and help them book these On-Demand Services Faster. © 2022 IEEE.
ABSTRACT
Because of Covid-19, schools, colleges, and institutions have moved to online learning. The education system has encountered and continues to encounter various challenges in this online format in managing the attendance of the students. The teacher used to call out the students' roll numbers or names when they were in the physical education mode. Nowadays as the world is developing towards a digital era, numerous techniques of collecting attendance such as attendance via biometric technologies like eye recognition, face scanning, voice recognition, fingerprint analysis have earned a lot of fame. Face recognition is the most efficient of these approaches as the face can be captured using a camera and compared using a trained model, but the others are more complex to implement at the user end, and some even need hardware. A lot of research work has been already done related to face recognition using models such as YOLO, MTCNN, FaceNet, HOG, LBPH, C2D-CNN. Models are usually loaded in the backend which causes latency issues and makes the system inefficient to use. Our proposed system aims to perform face recognition within the browser itself with the help of serverless edge computing. For the students, a simple web portal is developed, from which they can navigate to our plugin extension, where the model will capture attendance and dynamically update it in a Google Sheet. Face detection was done with Tiny Face Detector, while face recognition was done with Face Recognition Net. A few more models operate in conjunction with these two, recognizing the student from his or her livestream, checking the student's authenticity using logged in credentials, and updating the attendance in real-time across the browser. © 2021 IEEE.
ABSTRACT
Healthcare is a human right that must be accessible to all disregarding the social or economic conditions of any human being. The burden on healthcare system has increased immensely in the last few months. The COVID19 pandemic has brought to the fore gaps in the healthcare system world over. The doctors and front-line workers are directly getting exposed to the virus and patients that might need other healthcare services are vulnerable to the exposure. These problems would be catered by the proposed device as it would be operated by the patients and the real time data can be collected by the doctors to assess the vital body parameters through cloud without being physically present in the same environment. The parameters that can be monitored are body temperature, pulse rate, and oxygen saturation level. Hence, the proposed device includes sensors for measuring the body temperature (i.e. LM35) and pulse and oxygen level (i.e. MAX30100). The experimental setup has been built using Android based Blynk Cloud Platform where data is collected from remote places and stored the cloud. It is further available for assessment by the healthcare professionals. © 2021 IEEE.
ABSTRACT
The global cloud market has recently increased dramatically due to the Covid-19 pandemic. In such situation, people are looking for flexibility to work from home without going to the office physically and hence many organizations have started to look for a cloud-based solution. Clearly, Cloud not only offers flexibility, but also provides scalability and availability to an organization especially for startups and SMEs which have not established a firm and stable architecture yet. Hence, this paper aims to provide a better understanding on the model, features, and services to select the most suitable cloud platform based on the objectives or requirements of an organization. Finally, potential challenges of cloud development are also described to assist organizations in deploying cloud as a service in order to make a wise decision beforehand as it would be a long-run processes. © 2021 IEEE.
ABSTRACT
Higher Education is considered vital for societal development. It leads to many benefits including a prosperous career and financial security. Virtual learning through cloud platforms has become fashionable as it is expediency and flexible to students. New student learning models and prediction outcomes can be developed by using these platforms. The appliance of machine learning techniques in identifying students at-risk is a challenging and concerning factor in virtual learning environment. When there are few students, it is easy for identification, but it is impractical on larger number of students. This study included 530 higher education students from various regions in India and the outcomes generated from online survey data were analyzed. The main objective of this research is to predict early identification of students at-risk in cloud virtual learning environment by analyzing their demographic characteristics, previous academic achievement, learning behavior, device type, mode of access, connectivity, self-efficacy, cloud platform usage, readiness and effectiveness in participating online sessions using four machine learning algorithms namely K Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Random Forest (RF). Predictive system helps to provide solutions to low performance students. It has been implemented on real data of students from higher education who perform various courses in virtual learning environment. Deep analysis is performed to estimate the at-risk students. The experimental results exhibited that random forest achieved higher accuracy of 88.61% compared to other algorithms © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.