Linking CT and SPECT based analysis for quantitative follow-up of vascular perfusion defects in COVID-19

COVID-19 still affects a large population worldwide with possible post-traumatic sequelae requiring long-term patient follow-up for the most severe cases. The lung is the primary target of severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) infection. In particular, the virus affects the entire pulmonary vascular tree from large vessels to capillaries probably leading to an abnormal vascular remodeling. In this study we investigated two modalities for assessing this remodeling, SPECT perfusion scintigraphy and computed tomography, the latter enabling the computation of vascular remodeling patterns. We analyzed on a cohort of 30 patients the relationship between vascular remodeling and perfusion defects in the peripheral lung area, which is a predominant focus of the COVID-19 infectious patterns. We found that such relationship exists, demonstrated by moderate significant correlations between SPECT and CT measures. In addition, a vascular remodeling index derived from the z-score normalized peripheral CT images showed a moderate significant correlation with the diffusing capacity of the lung for carbon monoxide (DLCO) measures. Altogether these results point CT scan as a good tool for a standardized, quantitative, and easy-to-use routine characterization and follow-up of COVID-19-induced vascular remodeling. An extensive validation of these results will be carried out in the near future on a larger cohort. © 2023 SPIE.

Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

BERIS: An mBERT-based Emotion Recognition Algorithm from Indian Speech

Emotions, the building blocks of the human intellect, play a vital role in Artificial Intelligence (AI). For a robust AI-based machine, it is important that the machine understands human emotions. COVID-19 has introduced the world to no-touch intelligent systems. With an influx of users, it is critical to create devices that can communicate in a local dialect. A multilingual system is required in countries like India, which has a large population and a diverse range of languages. Given the importance of multilingual emotion recognition, this research introduces BERIS, an Indian language emotion detection system. From the Indian sound recording, BERIS estimates both acoustic and textual characteristics. To extract the textual features, we used Multilingual Bidirectional Encoder Representations from Transformers. For acoustics, BERIS computes the Mel Frequency Cepstral Coefficients and Linear Prediction coefficients, and Pitch. The features extracted are merged in a linear array. Since the dialogues are of varied lengths, the data are normalized to have arrays of equal length. Finally, we split the data into training and validated set to construct a predictive model. The model can predict emotions from the new input. On all the datasets presented, quantitative and qualitative evaluations show that the proposed algorithm outperforms state-of-the-art approaches. © 2022 Association for Computing Machinery.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Health Monitoring System Using Robotics

The entire world witnessed the covid-19pandemicinthe year 2020. The actual outbreak of this corona virus was first reported in Wuhan, China and later declared to be epidemic by (WHO) World Health Organization. The whole world was under tremendous pressure in monitoring health, managing, and maintaining hospitals and inventing new drugs. Initially, India was very much worried because of the huge population. The pandemic posed a critical challenge for healthcare sectors, since doctors and nursing professionals were among the most severely affected and it's clear that India must adopt new measures to increase healthcare proportional ratio and adoption of new technologies to manage large population groups. Robotics is one area which may largely always support the segment. The proposed research project emphasized on developing robotic devices with robotic vision, sensors-based motion planning, dynamic obstacle detection, and autonomous navigation in a hospital environment and supported the medical and nursing teams in reducing their workload and improving patient health monitoring, also the research explored multi-robot exploration and integration. © 2022 IEEE.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

How does a Rational Agent Act in an Epidemic?

The evolution of a disease in a large population is a function of the top-down policy measures from a centralized planner and the self-interested decisions (to be socially active) of individual agents in a large heterogeneous population. This paper is concerned with understanding the latter based on a mean-field type optimal control model. Specifically, the model is used to investigate the role of partial information on an agent's decision-making and study the impact of such decisions by a large number of agents on the spread of the virus in the population. The motivation comes from the presymptomatic and asymptomatic spread of the COVID-19 virus, where an agent unwittingly spreads the virus. We show that even in a setting with fully rational agents, limited information on the viral state can result in epidemic growth. © 2022 IEEE.

THE PROLIFERATION OF COVID-19 IN SAUDI ARABIA ACCORDING TO GOMPERTZ MODEL

Mathematical modeling can be a powerful tool to predict disease spread in large populations as well as to understand different factors which can impact it such as social distancing and vaccinations. This study aimed to describe the spread the coronavirus disease 2019 (COVID-19) pandemic in Saudi Arabia using a simple discrete variant of the Gompertz model. Unlike time-continuous models which are based on differential equations, this model treats time as a discrete variable and is then represented by a first-order difference equation. Using this model, we performed a short-term prediction of the number of cumulative cases of COVID-19 in the country and we show that the results match the confirmed reports. © 2022 Fractals.

Sentimental analysis of Twitter data and Comparison of covid 19 Cases trend Using Machine learning algorithms

Coronavirus was first detected in the year 2019 in Wuhan, China. The disease rapidly spread across the country in a short span of time. The Government had imposed strict rules and restrictions for lockdown and social distancing, work from home, and online classes to prevent the further spread of these covid cases During this phase, the morality of the covid cases was significantly controlled. But the larger population was affected by this. So, the mindset of the people has been changed. Sentimental analysis is an opinion mining approach to NLP which is used to detect and categorize the data as positive, negative, and neutral. In a situation like the COVID pandemic, one must stay in a positive mindset. In our project, we are implementing sentimental analysis using the Random Forest algorithm along with comparing the trend in variation of COVID 19 cases using the LSTM and KNN algorithms. © 2022 IEEE.

Commuter Student Integration During COVID-19

This Complete Research paper describes the experiences of commuter students pertaining to integration during COVID-19. Many colleges and universities host a large population of commuter students who often live at home and also work part-time or full-time jobs. Although there are varying definitions of commuter students, typically they are defined as someone who does not reside in University housing and primarily live at home with their families. Commuter student needs differ significantly from residential students. On top of academics and extracurricular activities, commuter students face the daily challenge of commuting to and from campus. However, a recent report found that there were more students that wanted to and or chose to live at home even with the extra demands on being a commuter student. The COVID-19 pandemic has added another challenge to commuter students as well. The incorporation of online classes and having almost no opportunity to be in on campus in person has left many students, especially commuter students, feeling isolated and disconnected from university life. The pandemic allowed for many technological solutions to attending classes but the challenge to stay connected and involved was often overlooked and left some commuter students disheartened. The ability to integrate or involve those commuter students fully into the university environment is important to the success and graduation of those students. Commuter students face many challenges that students who live on campus do not. Socialization for college students and peer group interaction positively affects critical thinking skills as well as academic development, thus having this key element to university life is critical to the success of students. However, commuter students often miss out on those opportunities because of their living situation and were directly impacted by having no on-campus interaction because of the online nature of classes caused by the pandemic. Another hurdle faced by commuter students is a lack of face-to-face contact with their instructors. It is also important to understand the connection between student's involvement on campus and the benefits of a high-level involvement, especially in terms of graduation. Those students who integrate more successfully are at less of a risk of dropping out. Students that have higher interaction with university academic and social systems tend to persist at higher rates. In order to ensure the success of commuter students we need to find ways in which to integrate them fully into the campus and create new programs and outreach to ensure future success. Thus, we frame this study in the Model of Co-Curricular Support (MCCS) and focus on four elements of integration: Academic, Social, Professional, and University. Using the MCCS as the framework, this study examines how first-year engineering commuter students are integrated academically, socially, and professionally into a regional university in the mid-west during COVID-19. For this study, we have one research question to examine: During COVID, to what extent do commuter students differ in integration compared to residential students? To answer this question, 146 students in the first-year engineering program gave consent to use their survey responses on the engineering student integration instrument, which is a valid and reliable survey instrument containing 22 questions across four integration constructs (e.g., academic, social, professional, and university). Data are presented for each of the four integration constructs and areas for improvement are discussed. Results show no significant differences for each of the four integration constructs between commuter and residential engineering students. Multiple reasons for this are discussed as well as implications for first-year programs that cater to commuter students in engineering. © American Society for Engineering Education, 2022.

MINI: AN ENERGY-FLEXIBLE POINT-OF-CARE DEVICE FOR HIGH-THROUGHPUT SCREENING

In 2020, the COVID-19 pandemic demonstrated the need for robust, high throughput diagnostics capable of screening large populations. To overcome this limitation, we have developed MINI: a portable, high-throughput screening device for COVID-19 infection in community and limited resource settings for up to 96 samples simultaneously. MINI operates on electrical, solar, or thermal energy and is robust against power interruptions. Here, we provide a complete description of MINI, and preliminary validation of the system utilizing a predecessor device and nasopharyngeal (NP) swabs samples - results in <30 minutes post RNA extraction and purification. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Impact of Chronic Lung Disease Using Deep Learning: A Survey

Artificial intelligence has developed in recent years. It is mostly enviable to discover the facility of contemporaneous state-of-the-art techniques and to examine lung nodule features in terms of a large population. Now a days lung plays a major role all over the world in early prevention in disease identification. The latest progress of deep learning sustains the recognition and categorization of medical images of respiratory problems. There are varieties of lung diseases to be analyzed to select the high mortality rate among them. In this paper, we have provided a comprehensive study of several lung ailments, in particular lung cancer, pneumonia, and COVID-19/SARS, Chronic Obstructive Pulmonary Disease. Existing deep learning methodology used to diagnose lung diseases are clearly explained and it will be helpful for the lung disease identify the system. © 2022, IFIP International Federation for Information Processing.

Designing to Fight Pandemics: A Review of Literature and Identifying Design Patterns for COVID-19 Tracing Apps

Throughout the pandemic, digital contact tracing using smartphone applications (or apps) has been endorsed by many authorities across the globe as a tool to limit the spread of COVID-19. Consequently, to deploy contact tracing in large populations, multiple contact tracing apps have been developed and deployed globally. However, due to the relative recency of the COVID-19 pandemic as well as the suddenness of the need for contact tracing at this scale, app designers are often left with no systematic guidelines. Designers today lack guidelines on what factors might affect perceptions and adoption of their apps. They also lack a knowledgebase of features that could be appropriate to include in their app for a given context. To address this gap, we conducted a review of the academic literature on attitudes towards and adoption of COVID-19 response apps, as well as a feature review of a diverse set of international tracing apps. Our investigation yielded a set of design patterns which can be used readily by designers of contact tracing apps. Our work lays the foundation to identify opportunities for new contextual feature design and use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Immunoinformatics Tools: A boon in vaccine Development Against Covid-19

The first evidences of SARS Covid 19 virus were reported from Labs in Wuhan, China's Hubei Province, at the end of 2019. It spread very quickly throughout China, leading in an epidemic and a global pandemic. A large population was affected and died due to the pandemic in 2019. It shares genetic similarities with SARS-CoV-2 and MERS-COV. The development of an effective SARS-CoV-2 vaccine is important for reducing COVID-19 deaths and giving immunological protection to the worldwide community. The lengthy and expensive process of vaccine production can be shortened by using immunoinformatics approaches. immunoinformatics tools such as Vaxijen, IEDB, NetCTL 1.2, PEP-FOLD etc have previously been used in reverse vaccinology for SARS-CoV-2 vaccine development in areas such as antigen selection, toxicity, predicting vaccine targets, allergenicity prediction and selection of MHC-I and II binding epitopes etc. In this review, we summarize some of the most useful immunoinformatics tools like vexijen, Bepipred 2.0, SVMTrip, FNepitope etc and their role in the development of covid 19 vaccines. The characteristics of such tools have been thoroughly reviewed, and which may provide experimental biologists with prediction insights that may enhance active research attempts to identify therapies for the infectious COVID-19 illness. © 2022 IEEE.

Expert Systems for Infection Control and Containment in Pandemic

The coronavirus cases were first reported on 2019 in Wuhan, following the outbreak of the same worldwide. India is the country with second largest population more than 1.34 billion and for such a country to manage this exponentially increasing deadly virus is a major challenge. In the initial period of this outbreak, we had no medicine/vaccine to put a full stop to this highly contagious and destructing virus. Rather the only armour that protects us is to wash our hands regularly, wear mask whenever we move out of our shelters and maintain social distance. At present though we have various vaccines introduced, it's our duty to follow the preventive measures. This paper aims to streamline the previous issues discussed by introducing a personal assistant that reminds the person on wearing mask while peeking out of shelters, to have sanitizer with them remotely, to restrict the amount of time they spend out and generate a final report with all the information about the places they visited and time spent out will be sent to the user on a monthly basis. © 2022 IEEE.

YOLOv3 based Real Time Social Distance Violation Detection in Public Places

The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it's most unlikely that we'll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces. © 2021 IEEE.

Correlation analysis between Baidu migration network and COVID-19 epidemic in China

Population mobility affected the spread and risk diffusion of COVID-19. Based on Baidu migration big data and COVID-19 cases data released by the national health commission of people's republic of China combined with mathematical statistics analysis and geographic information technology, OLS test and geographically weighted regression were used to analyze the correlation between the spread of COVID-19 and Baidu migration network from January 10 to March 14, 2020.The results showed that the diffusion process of COVID-19 epidemic in China was characterized by stages, including outbreak, potential diffusion, rapid diffusion, diffusion inhibition and diffusion reduction. In the study period, there is a certain spatial correlation between the COVID-19 epidemic data and the difference coefficient of inflow and outflow and the external connection degree of provinces. Through the OLS test of population migration index, it was found that the correlation between the difference coefficient of inflow and outflow and the spread of epidemic was more significant, and there was no collinear effect. The correlation analysis showed that there was a correlation between the epidemic data and the difference coefficient of inflow and outflow in spatial location, and most of them were negative correlation in the early stage, and gradually became positive correlation in the later stage. The negative correlation between Hubei and Hubei was significant, and the positive correlation between Xinjiang, Tibet and Qinghai was significant. It revealed that provinces with large population mobility and high number of confirmed cases were mainly distributed in Hubei Province and the central cities of China's key urban agglomerations, and the epidemic prevention pressure was mainly due to the risk of transmission and diffusion caused by large population mobility and high number of confirmed cases. © 2021 ACM.

CARBON MONOXIDE EMISSIONS MONITORING during COVID-19 PANDEMIC in JAKARTA

Jakarta has been known as the capital city, has been dealing with air pollution since many years ago. The main contributor of air pollution in Jakarta is due to large population, and high numbers of transportation, of which still running on diesel fuels, emitting far higher levels of pollutants. Carbon monoxide pollution is associate with number of transportations run in the city. Mobility index provided by Facebook Data for Good has managed to identify people's movement during COVID-19 period (stay put and change in movement matrix's). Therefore, this study aims to analyze the dynamic of carbon monoxide emissions during the COVID-19 pandemic in Jakarta. The results showed the distribution of CO emission in Jakarta and its mobility index of each quarter showed a similar pattern. The quarter 2 of 2020) is presented the lowest CO emission distribution value compared to the other five quarters, ranging from 0.0309-0.0334 mol/m2. The low value of CO emission distribution during that period was related to the low community mobility index (index of go) in the same period. While in the next quarter which is quarter 3 2020, the CO emission was relatively increased ranging from 0.031-0.037 mol/m2, which associated with the rise of mobility index stay of go value. Therefore, in this study, there is a relationship between the distribution of CO emission with the mobility index provided by Facebook. © ACRS 2021.All right reserved.

Twitter Sentiment Analysis on COVID-19

The COVID-19 pandemic has been a trending topic on social media since it first started in December 2019. This pandemic originated in the city of Wuhan in China. India was vastly affected by this pandemic due to its large population. However, due to its vast population, India has a large number of social media users, which can provide crucial insight into people's perspectives on topics related to the pandemic. This paper uses natural language processing and sentiment analysis on the posts created by users on the social media platform of Twitter. The study uses APIs and keywords to get the data to analyze and understand the emotions of the tweets linked to topics like oxygen, vaccine, beds, and lockdown in the times of COVID-19. The results and observations are presented using various graphs, charts, and word clouds. This paper aims to help the government, researchers, and frontline workers to get an insight into the sentiment on social media about various topics concerning the covid-19 pandemic. © 2021 IEEE.

Analysing Search Engine Trends with Respect to COVID-19 in India

The outbreak of COVID-19 in many regions of the world has been a serious source of concern for all nations. This pandemic, which started in the city of Wuhan in China, swiftly spread to other nations, with many cases recorded all over the world. India, which is currently battling a second wave of the epidemic, has had trouble curbing the spread of the virus due to its large population of more than 1.34 billion people. This huge population, however, contributes to a vast online community, with over 750 million internet users in India. Access to accurate and up-to-date information is important in times of emergency. The most popular way of getting information from the internet is through search engines. In this paper, we have examined how COVID-19 is being searched, a year into the epidemic. We have gathered the most prevalent search phrases associated with”corona” and analysed their search engine trends over the past year with respect to the COVID-19 outbreak in India. We have then compared related terms and drawn similarities in the search patters of those related terms. The results and observations related to the trending topics related to this pandemic provide insightful information about general internet users of the country. © 2021 IEEE.