Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

A supervised learning approach for the influence of comorbidities in the analysis of COVID-19 mortality in Tamil Nadu.

COVID-19 has created many complications in today's world. It has negatively impacted the lives of many people and emphasized the need for a better health system everywhere. COVID-19 is a life-threatening disease, and a high proportion of people have lost their lives due to this pandemic. This situation enables us to dig deeper into mortality records and find meaningful patterns to save many lives in future. Based on the article from the New Indian Express (published on January 19, 2021), a whopping 82% of people who died of COVID-19 in Tamil Nadu had comorbidities, while 63 percent of people who died of the disease were above the age of 60, as per data from the Health Department. The data, part of a presentation shown to Union Health Minister Harsh Vardhan, show that of the 12,200 deaths till January 7, as many as 10,118 patients had comorbidities, and 7613 were aged above 60. A total of 3924 people (32%) were aged between 41 and 60. Compared to the 1st wave of COVID-19, the 2nd wave had a high mortality rate. Therefore, it is important to find meaningful insights from the mortality records of COVID-19 patients to know the most vulnerable population and to decide on comprehensive treatment strategies.

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.

Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals

The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient's triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients' symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

Prioritizing COVID-19 Vaccine Delivery for the Indian Population

As India has successfully developed a vaccine to fight against the COVID-19 pandemic, the government has started its immunization program to vaccinate the population. Initially, with the limited availability in vaccines, a prioritized roadmap was required to suggest public health strategies and target priority groups on the basis of population demographics, health survey information, city/ region density, cold storage facilities, vaccine availability, and epidemiologic settings. In this paper, a machine learning-based predictive model is presented to help the government make informed decisions/insights around epidemiological and vaccine supply circumstances by predicting India's more critical segments that need to be catered to with vaccine deliveries as quickly as possible. Public data were scraped to create the dataset;exploratory data analysis was performed on the dataset to extract important features on which clustering and ranking algorithms were performed to figure out the importance and urgency of vaccine deliveries in each region. Copyright © 2022 IGI Global.

Click Analysis: How E-ommerce Companies Benefit from Exploratory and Association Rule Mining

Electronic commerce (henceforth referred to as e-commerce) has attracted many people to buy things online because of its convenience. With Covid-19 pandemic, the popularity of e-commerce increases as many people are working from home. Ability to understand customers' surfing and buying behavior on the e-commerce platform provides competitive advantage to e-commerce companies by being able to devise specific marketing plans to increase their market coverage and subsequently revenues from online sales of products. This paper discusses how the results derived from both, the exploratory data analysis (EDA) and association rule mining (ARM) can assist e-commerce companies to design specific marketing plans. The methodology consists of data understanding, data pre-processing, EDA, ARM, and analysis of results. A public dataset that is made available in the year 2020 consisting of clickstream data that are collected in 2018 from a popular fashion e-commerce website is used as a case study to prove the viability of the methodology in deriving results that can be used to design specific marketing plans. This study proves that it is possible to use clickstream data consisting of customers' surfing and buying behavior and apply the methodology to derive analysis and devise better marketing plans. © 2022, Success Culture Press. All rights reserved.

Detecting Early Warning Indicators of Covid-19 Pandemic in the Context of United States: An Exploratory Data Analysis

This work aims to investigate if social media data, Twitter in particular can be used to detect early warning indicators of COVID-19 pandemic in the United States (US). To demonstrate the viability of this work, English tweets were collected with a hasghtag of COVID-19 related topics ranges from 12th March to end of April 2020. With the help of with N-gram language model and Term Frequency and Inverse Document Frequency (TF-IDF) significant bi-grams such as ("new york”), ("social, distancing”), ("stay, safe”), ("toilet, paper”), ("wash, hand”), ("tested, positive”), (look, like), ("front, line”), ("grocery, store”) etc. are extracted. Our analysis shows that, the natures of the bi-grams directly reflect the characteristics of the infection cases and are almost similarly distributed over different clusters. This study also reveals that, the tweets of ("new york”) increases with ("stay, home”), ("social, distancing”), ("stay, safe”), ("look, like”) and ("tested positive”);and decreases with ("toilet, paper”). Bi-grams with such relationships are recognized as indicators and are validated with the number of infection cases on each day. Results show that, social media data can project the actual scenario of infection curve and able to detect warning indicators once the pandemic is moderately recognized. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Worldwide Real-time Recovery and Death Ratio of COVID-19 Pandemic

Covid-19 is a fast-spreading viral disease that not only infects humans but also animals. The daily lives of people, their health, and a country's economy are all impacted by this devastating virus. Unfortunately, India again got hit with the second wave of COVID-19, and this time the double mutant virus is much stronger than the one involved in the first wave of Pandemic. According to the scientists, the double mutant virus is 70% more deadly than the previous one that makes the situation even more alarming. This study shows that COVID-19 infected patients mostly suffer from lung infections after coming in contact with the virus. To perform the analysis, the data was collected from the Kaggle repository. This method only focuses on various trends and analyses of COVID-19 infected patients. This paper aims at performing exploratory Data Analysis to retrieve various kinds of important insights from the extracted data that involves the positivity rate of the top 10 countries around the world, positivity ratio of different states of India, death ratio of various states, and also the positivity rate based on gender is retrieved that is further important in claiming this virus' susceptibility towards a particular gender. The result of this analysis shows the man is at a greater risk of encountering the COVID-19 virus due to lack in presence of certain hormone that is only found in women. © 2022 American Institute of Physics Inc.. All rights reserved.

Click Analysis: How E-commerce Companies Benefit from Exploratory and Association Rule Mining

Electronic commerce (henceforth referred to as e-commerce) has attracted many people to buy things online because of its convenience. With Covid-19 pandemic, the popularity of e-commerce increases as many people are working from home. Ability to understand customers' surfing and buying behavior on the e-commerce platform provides competitive advantage to e-commerce companies by being able to devise specific marketing plans to increase their market coverage and subsequently revenues from online sales of products. This paper discusses how the results derived from both, the exploratory data analysis (EDA) and association rule mining (ARM) can assist e-commerce companies to design specific marketing plans. The methodology consists of data understanding, data pre-processing, EDA, ARM, and analysis of results. A public dataset that is made available in the year 2020 consisting of clickstream data that are collected in 2018 from a popular fashion e-commerce website is used as a case study to prove the viability of the methodology in deriving results that can be used to design specific marketing plans. This study proves that it is possible to use clickstream data consisting of customers’ surfing and buying behavior and apply the methodology to derive analysis and devise better marketing plans. © 2022, Success Culture Press. All rights reserved.

Reptile: Aggregation-level Explanations for Hierarchical Data

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user "zoom-in" on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from. In each drill-down iteration, Reptile must train a large number of predictive models. We thus extend factorised learning from countjoin queries to aggregation-join queries, and develop a suite of optimizations that leverage the data's hierarchical structure. These optimizations reduce runtimes by >6x compared to a Lapack-based implementation. When applied to real-world Covid-19 and African farmer survey data, Reptile correctly identifies 21/30 (vs 2 using existing explanation approaches) and 20/22 errors. Reptile has been deployed in Ethiopia and Zambia, and used to clean nationwide farmer survey data;the clean data has been used to design national drought insurance policies.

Exploratory Data Analysis of Genomic Sequence of Variants of SARS-CoV-2 Reveals Sequence Divergence and Mutational Localization.

Whole genome sequencing has rapidly progressed in recent years, with sequencing the SARS-CoV-2 genomes, making it a more reliable clinical tool for public health surveillance. This development has resulted in the production of a large amount of genomic data used for various types of genomic exploration. However, without a proper standard protocol, the usage of genomic data for analyzing various biological phenomena, such as mutation and evolution, may result in a propagating risk of using an unvalidated data set. This process could lead to irregular data being generated along with a high risk of altered analysis. Thus, the current study lays out the foundation for a preprocess pipeline using data analysis to analyze the genomic data set for its accuracy. We have used the recent example of SARS-CoV-2 to demonstrate the process overflow that can be utilized for various kinds of biological exploration such as understanding mutational events, evolutionary divergence, and speciation. Our analysis reveals a significant amount of sequence divergence in the gamma variant as compared with the reference genome thereby making the variant less infective and deadly. Moreover, we found regions in the genomic sequence that is more prone to mutational localization thereby altering the structural integrity of the virus resulting in a more reliable molecular viral mechanism. We believe that the current work will help for an initial check of the genomic data followed by the biological assessment of the process overflow which will be beneficial for the variant analysis and mutational uprising.

COVID-19 in Bangladesh: An Exploratory Data Analysis and Prediction of Neurological Syndrome Using Machine Learning Algorithms Based on Comorbidity

COVID-19 is caused by the SARS-CoV-2 virus, which has infected millions of people worldwide and claimed many lives. This highly contagious virus can infect people of all ages, but the symptoms and fatality are higher in elderly and comorbid patients. Many COVID-19 survivors have experienced a number of clinical consequences following their recovery. In order to have better knowledge about the long-COVID effects, we focused on the immediate and post-COVID-19 consequences in healthy and comorbid individuals and developed a statistical model based on comorbidity in Bangladesh. The dataset was gathered through a phone conversation with patients who had been infected with COVID-19 and had recovered. The results demonstrated that out of 705 patients, 66.3% were comorbid individuals prior to COVID-19 infection. Exploratory data analysis showed that the clinical complications are higher in the comorbid patients following COVID-19 recovery. Comorbidity-based analysis of long-COVID neurological consequences was investigated and risk of mental confusion was predicted using a variety of machine learning algorithms. On the basis of the accuracy evaluation metrics, decision trees provide the most accurate prediction. The findings of the study revealed that individuals with comorbidity have a greater likelihood of experiencing mental confusion after COVID-19 recovery. Furthermore, this study is likely to assist individuals dealing with immediate and post-COVID-19 complications and its management. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Survival Analysis of COVID-19 Patients in the Philippines Using Kaplan-Meier Estimation in Multiple Decrements

This study presents the recovery patterns of COVID-19 patients in the Philippines using survival analysis in the multiple decrement setting. A total of 152,203 patients during the period January to December 2021 were included in the study. Data processing using Python and exploratory data analysis were employed. Probabilities were obtained using basic actuarial principles on two decrements: recovery and death. Kaplan-Meier estimation was then applied to obtain estimates of the survival function. The average length of treatment before recovery and death was also obtained. Results showed that older patients have higher risk of dying from COVID-19 compared to younger patients. While infection is higher among female population, the risk of death is higher among male patients. Based on the survival rates, the probabilities of recovery are highest during the 3rd week from onset of symptoms and the average length of treatment before recovery is determined to be 6 days. © 2022 ACM.

Exploratory Data Analysis of the Tourist Profile: Case Study in Loja-Ecuador

The determination of the profile of the tourist who visits the destinations arises as a proposal for the improvement of the competitiveness of the territories;data collected is to know the characteristics of the tourists who arrive at the destination, the type of accommodation used, the way of traveling and the main motivation for the trip, the most visited tourist attractions, the tourist expenditure, and the overall rating of the destination. The main aim of this study is to apply a non-traditional methodology composed of data mining methods such as: DEA (Data Envelopment Analysis) and PCA (Principal Component Analysis) to investigate past statistics. Based on these results, the sustainable operation strategies are made to prepare the destination and diversify the offer, to it can be used by the most important managers in the tourism industry to reactivate the economy after the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Worldwide Vaccination Report for COVID-19 Analysis and Visualization Using Deep Learning

Covid-19 is an ongoing pandemic, caused by the Acute Respiratory Disease Coronavirus 2 (SARS-CoV-2). The virus first appeared in Wuhan, China in December 2019. The World Health Organization announced the Public Health Emergency of International Concern on COVID-19 on January 30, 2020, and announced the epidemic on March 11, 2020. Due to high number of death cases the COVID-19 becames a deadliest pandemics in the history. The main aim of this work is to convey the analysis of various current vaccination programs around the globe by performing Exploratory Data Analysis on the scraped data present on the web. In the result analysis, the model visualizes and showcases the caused by Covid in different countries through the last one year and the progress of the vaccination program in various countries around the world. The result analysis shows that United States, United Kingdom, England, India and China are the top five country that are vaccinating maximum people in a day and Gabraltar have the most people vaccinated as compared to others. © 2022, Springer Nature Switzerland AG.

Official statistics: Providing data acumen in the advent and continuous presence of COVID-19

COVID-19 has disturbed people’s patterns of life and sources of income, particularly the income of informal sector business operators and households worldwide. As extraordinary policy initiatives are calibrated, the requirement for timely statistics on health and economic development rises. Thus, the purpose of this research is to analyze the Internally Generated Revenue (IGR) available to Nigerian states in 2020 and 2019 by examining the disparities caused by the COVID-19 pandemic and its impact on the country’s approximately 200 million people. The exploratory research approach was used, with a particular emphasis on descriptive and trend analysis of the data acquired during the study periods. According to the findings, 46% of Nigerian states, including the Federal Capital Territory, suffered reductions in IGR generation as the pandemic surged, whereas 35 per cent of the states experienced a corresponding increase in IGR and confirmed COVID-19 infections. However, 16% of the states reported an inverse growth in their IGR as the number of verified COVID-19 cases decreased. To deal with the unprecedented shocks caused by the ongoing existence of COVID-19, which necessitate the tapping of new information and revenue streams, a continual review of countries’ revenue sources is required. [ FROM AUTHOR] Copyright of Statistical Journal of the IAOS is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Exploratory Data Analysis on SARS-CoV-2 Variants in India: especially Omicron (B.1.1.529) as of 6th December 2021

SAR-CoV-2 is now spreading around the world, resulting in increased hospitalization and catastrophic fatality. The genome for coronavirus disease is vulnerable to abnormalities, which leads to genetic distortion and immunity loss. A novel variant of concern (VoC) with a new mutation having Pango lineage B.1.1.529, namely Omicron by WHO, was first found in South Africa at the end of November 2021. As of date, this new variant has already been spread rapidly in more than 58 countries and no doubt including India. In this work, Exploratory Data Analysis (EDA) analysis has been taken on different types of Covid-19 variants to date, where Omicron has demonstrated to be more increased transmissibility and infectious as compared to other variants. EDA offers several graphical representations to a better comprehension of the data and generates statistics for numerical data present in the dataset, as of 6th December 2021. Starting from 2nd December 2021 India has reported 23 new omicron cases within four days, which is a major challenge both for the doctors and government. Moreover, the EDA technique has been carried out for finding a significant correlation with the total number of Omicron cases as of the date in India using a scatter plot. Also, a conceptual design has been configured in this project that describes the whole process of how EDA analysis has been carried out and a Treemap that looks forward to outliers in all countries representing more than twenty-five covid-19 variants. © 2022 IEEE.

Exploration of dataset COVID-19's impact on airport traffic

Coronavirus Disease 2019 (COVID-19) pandemic has had a huge impact on airport traffic, but few works have researched about this field. In order to have a further understanding of the influence, this paper studied the dataset Covid Impact on Airport Traffic. By exploratory data analysis and visualization of the dataset, this paper has a clear insight of how the airport traffic was influenced by COVID-19. This paper also analyzed the correlation of the data with confirmed covid-19 cases using an auxiliary dataset Time series summary of global confirmed covid-19 cases and forecasted the how would the data in Covid Impact on Airport Traffic change with the number of confirmed covid-19 cases. And finally, this paper estimated the financial loss of airports. The result of this paper showed the influence of COVID-19 from multiple aspects and it has instructive significance in some degree. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Inclusive Analysis of Covid-19 disease and Vaccination in India

The covid-19 outbreak has appeared to be a threat to mankind for all the countries, especially India. The first wave of this virus arrived in the country in 2020, and due to various control measures taken by the government, the situation was somewhat controllable. Unfortunately, the second wave has brought enormous trouble to the citizens, and even the measures couldn't possibly stop the mess. This study presents a comprehensive analysis of the second wave of corona virus spread in India, along with visualized information about vaccination undertaken by the citizens. The datasets over which the study has been performed are taken from 16 January 2021 to 2nd May 2021. © 2021 IEEE.