ABSTRACT
An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken.
ABSTRACT
Machine learning algorithms are used for various purposes to predict, classify, or forecast by training the algorithms with the specific dataset. SVR and multiple linear regression can take numerous features to forecast or predict scores through the train-test-split. The education sector has been changed rapidly due to the pandemic of COVID-19 where online classes are being a module worldwide. However, junior schools or colleges stubbed into a position where student performance measurement is a hindrance due to the lack of taking physical examinations. During the COVID-19, student performance can be acquired using the previous achievement of individual students where multiple conditions can be applied. The aim of this paper is to train and test the conditional dataset of student's results through SVR and Multiple Linear Regression to predict and justify the results in accordance with using the proposed model in the future. As conditions have been applied to the individual subjects when calculating new results based on the previous achievement of student’s performance so that each subject’s score has been trained and tested individually through the machine learning algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%. © 2021. All Rights Reserved.
ABSTRACT
Algorithm is an efficient way to solve issues or minimizing efforts in an existing system by imposing new technique or rules on the existing system. Technology creates new platform or elements by which a vast majority of business growing and new idea or algorithm can be more effective if one can attain the algorithm on its system. Online e-commerce platform is increasing and due to COVID-19 the world has been mostly depending on it. Customer always expects fastest delivery when customer uses online platform and the eagerness of customer's expectation always impacts on online platform. In the thought of fastest delivery for the manufacturer or B2C oriented business a fastest shipping algorithm has been proposed where the algorithm will use the dataset of its existing system and will calculate the shortest path to find the nearest located outlets. It will gradually support the manufacturers or brands by identifying the nearest distance outlets for fastest delivery of products and at the same time it will minimize the efforts of work, time and cost.
ABSTRACT
Background: Coronavirus disease 2019 pandemic causes significant strain on healthcare infrastructure and medical resources. So, it becomes crucial to identify reliable predictor biomarkers for COVID-19 disease severity and short term mortality. Many biomarkers are currently investigated for their prognostic role in COVID-19 patients. Our study is retrospective and aims to evaluate role of semi-quantitative CT-severity scoring versus LDH as prognostic biomarkers for COVID-19 disease severity and short-term clinical outcome. Results: Two hundred sixty-six patients between April 2020 and November 2020 with positive RT-PCR results underwent non-enhanced CT scan chest in our hospital and were retrospectively evaluated for CT severity scoring and serum LDH level measurement. Data were correlated with clinical disease severity. CT severity score and LDH were significantly higher in severe and critical cases compared to mild cases (P value < 0.001). High predictive significance of CT severity score for COVID-19 disease course noted, with cut-off value ≥ 13 highly predictive of severe disease (96.96% accuracy);cut-off value ≥ 16 highly predictive of critical disease (94.21% accuracy);and cut-off value ≥ 19 highly predictive of short-term mortality (92.56% accuracy). CT severity score has higher sensitivity, specificity, positive, and negative predictive values as well as overall accuracy compared to LDH level in predicting severe, critical cases, and short-term mortality. Conclusion: Semi-quantitative CT severity scoring has high predictive significance for COVID-19 disease severity and short-term mortality with higher sensitivity, specificity, and overall accuracy compared to LDH. Our study strongly supports the use of CT severity scoring as a powerful prognostic biomarker for COVID-19 disease severity and short-term clinical outcome to allow triage of need for hospital admission, earlier medical interference, and to effectively prioritize medical resources for cases with high mortality risk for better decision making and clinical outcome. © 2021, The Author(s).
ABSTRACT
Background and objective: The pandemic Coronavirus Disease 2019 (COVID-19) is the most threatening infectious disease nowadays that affecting people's health worldwide. The disease symptoms were attributed to infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The current article aimed to discriminate different lymphocyte subtypes and pro-inflammatory mediators in COVID-19 patients with an emphasis on variations in their levels in mild and severely infected individuals that might help in the disease early intervention. Subjects and methods: The count of the Cluster of Differentiation (CD)3+ T, CD4+ T, CD8+ T cells, B lymphocytes, and Natural Killer (NK) cells were measured in the blood of healthy control and COVID-19 people with mild and severe symptoms using a flow cytometer. The plasma levels of Nuclear Factor kappa B (NF-kappa B), Tumor Necrosis Factor-alpha (TNF-alpha), Interleukin (IL)-6, IL-1 beta, IL-8, Procalcitonin (PCT), and Platelet-Activating Factor (PAF) were also detected by Enzyme-Linked Immunosorbent Assay (ELISA). Results: Total lymphocyte count and lymphocyte subsets significantly decreased in the blood of COVID-19 patients with more decrements in severely infected patients. The inflammatory markers levels remarkably increased in COVID-19 patients with higher increments in severe COVID-19 infected patients. Conclusion: In conclusion, the reduced level of the lymphocyte subsets and the induced pro-inflammatory response were vital signs that were concomitant with COVID-19. Besides, they were associated with the severity of the diseases.
ABSTRACT
The rapid adoption of social media technologies has resulted in a fundamental shift in the way communication and collaboration take place. As staff and students use social media technologies in their personal lives, it is important to explore how social media technologies are being used as an educational tool especially during a pandemic where teaching and learning has to be done via mediated communication. However, the question arises as to how effective the use of social media in terms of Academic Performance and Social Capital of the students. Academic Performance and Social Capital of university students are two interrelated and critical issues which can define the efficacy of social and academic activities of students at their universities. However, few studies have adopted a holistic approach to determine the direct role of online social network activities such as Facebook Intensity, and Community Factors on both Academic Performance and Social Capital of university students. Thus, this study attempted to conduct a cross-sectional survey using self-administered questionnaires which were distributed to 518 undergraduate students at the University of Sirte, Libya. The data was analyzed using various analytical techniques including descriptive statistics, preliminary and inferential analyses by using SPSS and PLS-SEM. The results revealed that Facebook Intensity and Community Factors significantly relate to Social Capital and Academic Performance. This study concludes that the intensity of Facebook Usage among university students and the interactions maintained by students can be used to develop Social Capital. Also, university students with good social communications skills and high self-esteem will benefit from interactions with lecturers and peers and these benefits will certainly enhance their Academic Performance. This study also demonstrates the potential of using Facebook as a teaching aid during pandemic. © 2020 All Rights Reserved