Energy- efficient model "Inception V3 based on deep convolutional neural network" using cloud platform for detection of COVID-19 infected patients

Objectives: COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy. Methods: We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification. Results: Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays. Conclusions: In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper. © 2023 Walter de Gruyter GmbH. All rights reserved.

Design and implementation of cloud platform for nuclear accident simulation

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.

Energy- efficient model "Inception V3 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

COVID-19 is frightening the health of billions of persons and speedily scattering worldwide. Medical studies have revealed that the majority of COVID-19 patients. X-ray of COVID-19 is extensively used because of their noticeably lower price than CT. This research article aims to spot the COVID-19 virus in the X-ray of the chest in less time and with better accuracy.We have used the inception-v3 available on the cloud platform transfer learning model to classify COVID-19 infection. The online Inception v3 model can be reliable and efficient for COVID-19 disease recognition. In this experiment, we collected images of COVID-19-infected patients, then applied the online inception-v3 model to automatically extract features, and used a softmax classifier to classify the COVID-19 images. Finally, the experiment shows inception v3 is significant for COVID-19 image classification.Our results demonstrate that our proposed inception v3 model available on the cloud platform can detect 99.41% of COVID-19 cases between COVID-19 and Lung Mask diseases in 44 min only. We have also taken images of the normal chest for better outcomes. To estimate the computation power of the model, we collected 6018 COVID-19, Lung Masks, & Normal Chest images for experimentation. Our projected model offered a trustworthy COVID-19 classification by using chest X-rays.In this research paper, the inception v3 model available on the cloud platform is used to categorize COVID-19 infection by X-ray images. The Inception v3 model available on the cloud platform is helpful to clinical experts to examine the enormous quantity of human chest X-ray images. Scientific and clinical experiments will be the subsequent objective of this paper.

Construction and Application of Waterway Engineering Design Virtual Simulation Cloud System Based on Outcome-Based Education

Hydraulic design is automatically inherent in hydraulic engineering courses, conventional teaching of the Waterway Engineering Design course tends to have limitations such as low participation, poor interactivity, disconnection between theoretical and experimental training, and restriction of experimental design by time and space. To address these needs, a virtual simulation cloud system of Waterway Engineering Design is developed based on outcome-based education. Taking real engineering projects as prototypes, this system adopts virtual reality technology and cloud platform to simulate the scene structure and instrument function with high precision. The multi-model, integrational teaching expands the experimental content, enhances the interactivity of the design process, and provides a high-quality, immersive online learning experience for students. Since its application, the Waterway Engineering Design Virtual Simulation Cloud System has received good feedback from both teachers and students. During the Covid-19 epidemic, it provided significant support for experiments and teaching of the Waterway Engineering Design course and became a pivotal supplement to the existing teaching system. The Waterway Engineering Design Virtual Simulation Cloud System adheres to the “student-centered” teaching principle, builds up students’ ability for independent learning and engineering practice, and facilitates their personal development and training for excellent engineers © 2022, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Analysis of Student Satisfaction on Virtual Learning Platforms During COVID-19

As a result of the global spread of COVID-19, e-Learning has recently experienced extraordinary growth. Many educational sectors have made the transition from traditional classroom learning to virtual learning via various online platforms. In this epidemic, virtual learning has enabled all schools and universities to continue to provide education. This rapidly growing alternative modality necessitates the provision of robust and high-quality education. It is also important to figure out whether online learning satisfies the needs of pupils. Even if learning has become easier, many people still confront difficulties, poor connectivity and e-platform. This study aims to identify the students' satisfaction by conducting a survey and analyzing it by means of data analysis and data visualization.

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments.

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students' hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.

A Django Web Application to Promote Local Service Providers

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.

IoT-Based Smart Health Monitoring System for COVID-19.

With the commencement of the COVID-19 pandemic, social distancing and quarantine are becoming essential practices in the world. IoT health monitoring systems prevent frequent visits to doctors and meetings between patients and medical professionals. However, many individuals require regular health monitoring and observation through medical staff. In this proposed work, we have taken advantage of the technology to make patients life easier for earlier diagnosis and treatment. A smart health monitoring system is being developed using Internet of Things (IoT) technology which is capable of monitoring blood pressure, heart rate, oxygen level, and temperature of a person. This system is helpful for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions. However, if any changes occur in a patient's health based on standard values, then the IoT system will alert the physician or doctor accordingly. The maximum relative error (%ϵ r) in the measurement of heart rate, patient body temperature and SPO2 was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients.

[Design and Development of Cloud Platform of Emergency COVID-19 Nucleic Acid Detection].

According to the characteristics of short time and large amount of samples for out of hospital emergency nucleic acid detection, this study introduces an out of hospital emergency nucleic acid detection cloud platform system, which realizes the functions of rapid identification of the detected person and one-to-one correspondence with the samples, and real-time upload of the detection results to Zhejiang Government service network for quick viewing and statistics, so as to complete the task of national nucleic acid screening efficiently and accurately that we must provide information support.

In-Browser Attendance System using Face Recognition and Serverless Edge Computing

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.

Android based Cloud Platform for Health Monitoring of COVID19 Affected Person

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.

A Theoretical Review of Cloud Computing Services in Big Data Analytics

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.

Telemonitoring in the Covid-19 Era: The Tuscany Region Experience.

Covid-19 has brought many difficulties in the management of infected and high-risk patients. Telemedicine platforms can really help in this situation, since they allow remotely monitoring Covid-19 patients, reducing the risk for the doctors, without decreasing the efficiency of the therapies and while alleviating patients' mental issues. In this paper, we present the entire architecture and the experience of using the Tel.Te.Covid19 telemedicine platform. Projected for the treatment of chronic diseases, it has been technologically updated for the management of Covid-19 patients with the support of a group of doctors in the territory when the pandemic arrived, introducing new sensors and functionalities (e.g., the familiar use and video calls). In Tuscany (Central Italy), during the first wave of outbreak, a model for enrolling patients was created and tested. Because of the positive results, the latter has been then adopted in the second current wave. The Tel.Te.Covid19 platform has been used by 40 among general practitioners and doctors of continuity care and about 180 symptomatic patients since March 2020. Both patients and doctors have good opinion of the platform, and no hospitalisations or deaths occurred for the monitored patients, reducing also the impact on the National Healthcare System.

Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics.

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

Practical exploration of the informationization support for prevention and control of the new coronavirus pneumonia with Tongji Hospital as an example / 中华医院管理杂志

December of 2019 witnessed the outbreak of new coronavirus pneumonia in Wuhan city and a few localities. As a designated hospital, Tongji Hospital is designated as a hospital for the diagnosis and treatment of numerous patients of such a disease. Based on the medical cloud platform, the hospital has initiated a regional remote diagnosis center; based on its IT system, the hospital to operate its epidemic prevention and management mechanism, set up the self-service system for patients at the fever clinic, launched its online diagnosis and treatment services, and established a hospital epidemic supervision platform. By strengthening the informational support needed for epidemic prevention and control, the hospital has enhanced its efficiency of epidemic prevention and control, reducing the risk of cross-infection, and ensuring data security. Its experiences offer references for informationization support for other regions and hospitals in China.