OLAP over Big COVID-19 Data: A Real-Life Case Study

This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided.

On Beta Exponentiated Lomax-Exponential Distribution with Applications

This study introduces a new Beta Exponentiated Lomax-Exponential Distribution (BELED) with special reference to its quantile function to enhance closed form solution of its parameters and make its proprietorial effect on data modeling quantifiable with exactness. The obtained BELED which accommodates heavy and light data, either skewed or not, addressed the challenges of intractable parameter estimation in previous related distributions from same family. The BELED properties which includes rth moments, characteristics function, Renyi and Shanon entropy, reliability measures as well as stress-strength model were derived. The estimator (β

Search method for two cutoff values in clinical trial using Kruskal-Wallis test by minimum P-value approach

In clinical trials, age is often converted to binary data by the cutoff value. However, when looking at a scatter plot for a group of patients whose age is larger than or equal to the cutoff value, age and outcome may not be related. If the group whose age is greater than or equal to the cutoff value is further divided into two groups, the older of the two groups may appear to be at lower risk. In this case, it may be necessary to further divide the group of patients whose age is greater than or equal to the cutoff value into two groups. This study provides a method for determining which of the two or three groups is the best split. The following two methods are used to divide the data. The existing method, the Wilcoxon-Mann-Whitney test by minimum P-value approach, divides data into two groups by one cutoff value. A new method, the Kruskal-Wallis test by minimum P-value approach, divides data into three groups by two cutoff values. Of the two tests, the one with the smaller P-value is used. Because this was a new decision procedure, it was tested using Monte Carlo simulations (MCSs) before application to the available COVID-19 data. The MCS results showed that this method performs well. In the COVID-19 data, it was optimal to divide into three groups by two cutoff values of 60 and 70 years old. By looking at COVID-19 data separated into three groups according to the two cutoff values, it was confirmed that each group had different features. We provided the R code that can be used to replicate the results of this manuscript. Another practical example can be performed by replacing x and y with appropriate ones.

Statistical properties and estimation of the three-parameter lindley distribution with application to COVID-19 Data

In a recent article by Shanker et al. (2017), the three-parameter Lindley distribution has been studied. The present paper is a continuation of the investigation of the properties of this distribution because of its high flexibility for modeling lifetime data. We studied some statistical properties of this distribution as central tendency measures, dispersion measures, and shape measures. In addition, the mode and the quantile function of the distribution were derived by the authors. The three parameters were estimated by the Maximum Product of Spacing Method (MPS) due to its fame in estimating parameters. A simulation study is carried out to examine the consistency of estimators using mean square error (MSE). The estimators showed that they have the property of consistency because MSEs decrease with increasing the size of the sample. On the practical side, the MPS estimates were used to obtain statistical properties, probability density function (p.d.f), cumulative distribution function (c.d.f), survival function, and hazard function for real data which represents COVID-19 Data in Iraq/Al-Anbar Province. We found the flexibility of the distribution in representing life data and the possibility of getting the patients probability of death and probability of survival for the time. © 2023 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Generalized plithogenic whole hypersoft set, PFHSS-Matrix, operators and applications as COVID-19 data structures

This article is a preliminary draft for initiating and commencing a new pioneer dimension of expression. To deal with higher-dimensional data or information flowing in this modern era of information technology and artificial intelligence, some innovative super algebraic structures are essential to be formulated. In this paper, we have introduced such matrices that have multiple layers and clusters of layers to portray multi-dimensional data or massively dispersed information of the plithogenic universe made up of numerous subjects their attributes, and sub-attributes. For grasping that field of parallel information, events, and realities flowing from the micro to the macro level of universes, we have constructed hypersoft and hyper-super-soft matrices in a Plithogenic Fuzzy environment. These Matrices classify the non-physical attributes by accumulating the physical subjects and further sort the physical subjects by accumulating their non-physical attributes. We presented them as Plithogenic Attributive Subjectively Whole Hyper-Super-Soft-Matrix (PASWHSS-Matrix) and Plithogenic Subjective Attributively Whole-Hyper-Super-Soft-Matrix (PSAWHSS-Matrix). Several types of views and level-layers of these matrices are described. In addition, some local aggregation operators for Plithogenic Fuzzy Hypersoft Set (PPFHS-Set) are developed. Finally, few applications of these matrices and operators are used as numerical examples of COVID-19 data structures. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Data analytics for healthcare monitoring and inferencing

Big Data has changed the manner in which we manage and explore data in all types of sectors. Healthcare analytics has potential to decrease expenses of treatment and predict all types of pandemics. We can also avoid and control contagious diseases to improve the quality of life. The average human life of expectancy is also increasing continuously, which presents new challenges to treatment conveyance strategies. Nowadays, healthcare information are generating from various applications like patient data storage and monitoring in healthcare management systems. Healthcare data is continuously increasing and distributed in a manner for sharing among medical experts and healthcare service provider. Cloud gives most incredible strategies to explore the information arrange from medical care by analyzing continuous previous medical data. As of now existing framework could not support both analysis and procedure for large quantity of healthcare data. In this book chapter, we present a study on data analytics and machine learning techniques using R and Python programming for efficient inference, monitoring, and transmission of medical data in healthcare industries. We also brief about bioinformatics approaches in order to see usefulness in dealing with pandemics like COVID-19 situation. As a reader progresses through this book chapter, he or she will be able to: 1. Define key terms in healthcare. 2. Identify the challenges and opportunities facing healthcare organizations. 3. Summarize the role of oversight and research in healthcare infrastructure. 4. Describe how value, quality, and variation in healthcare impact outcomes and expenditures. 5. Summarize current healthcare trends. 6. Understand the Bioinformatics Data Analytics for COVID-19 pandemic. © 2022 Scrivener Publishing LLC.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Diffusion Convolutional Recurrent Neural Network-Based Load Forecasting During COVID-19 Pandemic Situation

Infected by the novel coronavirus (COVID-19 – C-19) pandemic, worldwide energy generation and utilization have altered immensely. It remains unfamiliar in any case that traditional short-term load forecasting methodologies centered upon single-task, single-area, and standard signals could precisely catch the load pattern during the C-19 and must be cautiously analyzed. An effectual administration and finer planning by the power concerns remain of higher importance for precise electrical load forecasting. There presents a higher degree of unpredictability's in the load time series (TS) that remains arduous in doing the precise short-term load forecast (SLF), medium-term load forecast (MLF), and long-term load forecast (LLF). For excerpting the local trends and capturing similar patterns of short and medium forecasting TS, we proffer Diffusion Convolutional Recurrent Neural Network (DCRNN), which attains finer execution and normalization by employing knowledge transition betwixt disparate forecasting jobs. This as well evens the portrayals if many layers remain stacked. The paradigms have been tested centered upon the actual life by performing comprehensive experimentations for authenticating their steadiness and applicability. The execution has been computed concerning squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Consequently, the proffered DCRNN attains 0.0534 of MSE in the Chicago area, 0.1691 of MAPE in the Seattle area, and 0.0634 of MAE in the Seattle area. © 2022 Lavoisier. All rights reserved.

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.

A New Chapter for Medical Image Generation: The Stable Diffusion Method

Data collecting and sharing have been widely accepted and adopted to improve the performance of deep learning models in almost every field. Nevertheless, in the medical field, sharing the data of patients can raise several critical issues, such as privacy and security or even legal issues. Synthetic medical images have been proposed to overcome such challenges;these synthetic images are generated by learning the distribution of realistic medical images but completely different from them so that they can be shared and used across different medical institutions. Currently, the diffusion model (DM) has gained lots of attention due to its potential to generate realistic and high-resolution images, particularly outperforming generative adversarial networks (GANs) in many applications. The DM defines state of the art for various computer vision tasks such as image inpainting, class-conditional image synthesis, and others. However, the diffusion model is time and power consumption due to its large size. Therefore, this paper proposes a lightweight DM to synthesize the medical image;we use computer tomography (CT) scans for SARS-CoV-2 (Covid-19) as the training dataset. Then we do extensive simulations to show the performance of the proposed diffusion model in medical image generation, and then we explain the key component of the model. © 2023 IEEE.

Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques. © 2023

Social and Mental Well-Being-COVID-19 After Effects Survey and Data Analysis

COVID-19 pandemic affected the entire globe in 2019. This pandemic is considered as the first one with its defense more than pharmaceutical measures such as: Personal hygiene (hand sanitization, wearing masks) and social distancing. This pandemic has affected people with different factors such as: anxiety, emotions, social life, mental and physical health, and economic crisis. These factors helped this pandemic to turn up with digital solutions for its prevention and prediction estimation. These prediction techniques can analyze the previous data set of this pandemic and provide interesting insights about such a situation to occur in future along with its prevention measures. In this article, we tried to systematize various research activities-using machine learning, data science, and data visualization to extract meaningful information about COVID-19. Data collection has been done by conducting open surveys on different platforms such as: social media, university survey as well as community survey. Based on the collected data, analysis has been done on the emotional, social and mental health of people in order to provide future research directions and collective fight against such pandemics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

On a global scale, 213 countries and territories have been affected by the coronavirus outbreak. According to researchers, underlying co-morbidity, which includes conditions like diabetes, hypertension, cancer, cardiovascular disease, and chronic respiratory disease, impacts mortality. The current situation requires for immediate delivery of solutions. The diagnosis should therefore be more accurate. Therefore, it's essential to determine each person's level of risk in order to prioritise testing for those who are subject to greater risk. The COVID-19 pandemic's onset and the cases of COVID-19 patients who have cardiovascular illness require specific handling. The paper focuses on defining the symptom rule for COVID-19 sickness in cardiovascular patients. The patient's chronic condition was taken into account while classifying the symptoms and determining the likelihood of fatality. The study found that a large proportion of people with fever, sore throats, and coughs have a history of stroke, high cholesterol, diabetes, and obesity. Patients with stroke were more likely to experience chest discomfort, hypertension, diabetes, and obesity. Additionally, the strategy scales well for large datasets and the computing time required for the entire rule extraction procedure is faster than the existing state-of-the-art method. © 2023 Faculty of Engineering, Alexandria University

Data analysis of Global Perceived Stress Scores during Covid-19

The Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. This work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. Machine Learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

Discrete Erlang-2 distribution and its application to leukemia and COVID-19

Via the survival discretization method, this research revealed a novel discrete one-parameter distribution known as the discrete Erlang-2 distribution (DE2). The new distribution has numerous surprising improvements over many conventional discrete distributions, particularly when analyzing excessively dispersed count data. Moments and moments-generating functions, a few descriptive measures (central tendency and dispersion), monotonicity of the probability mass function, and the hazard rate function are just a few of the statistical aspects of the postulated distribution that have been developed. The single parameter of the DE2 distribution was estimated via the maximum likelihood technique. Real-world datasets, leukemia and COVID-19, were applied to analyze the effectiveness of the recommended distribution. © 2023 the Author(s), licensee AIMS Press.

Visualizing COVID-19 Vaccination Rate and Vaccination Centre in Malaysia using DBSCAN Clustering model

In February 2021, the Malaysian government launched a vaccination campaign against coronavirus disease 2019 (COVID-19). However, there is a problem in identifying suitable location for vaccination centre should be allocated. At the same time, there are population that living in the rural area and has difficulty to travel to the nearest vaccination centres. Therefore, based on the data of vaccination rate collected by Ministry of Health, the proposed project aims to classify and visualise the data based on number of COVID-19 vaccination rate and centre in Malaysia for the adult and adolescent populations. This project uses machine learning technique called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The system is developed in Python language platform for back-end development, and PyCharm is utilised for front-end development in web-based platform. This project follows four phases in Waterfall model: requirement analysis, design, implementation, and testing. The system is evaluated for functionality and usability based on user satisfaction and the accuracy of the model. The results of the testing shows that all the functionality of the system have been implemented successfully in the system. The system also rated good according to SUS Questionnaire in usability testing with score of 88.5%. The model of machine learning also achieved a good accuracy score which is greater than 0.3. In conclusion, the data visualization web-based application helps the Malaysian government to identify location for additional vaccination centres in strategic locations and it helps Malaysian people to locate nearby vaccination centres in their area. © 2022 IEEE.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

A Modified Artificial Neural Network (ANN)-Based Time Series Prediction of COVID-19 Cases from Multi-Country Data

The deadly Corona virus that first appeared in a seafood market in the Wuhan city of China in December 2019 has been causing global distress by claiming lives and collapsing economies. Given its serious nature, there is an urgent need to understand the virus's future trajectory. The current study predicts the next day confirmed, death and recovery cases of COVID-19 pandemic for India, Italy, Spain, and the USA by using a modified multilayer neural network (MMLNN) model. The spread of the COVID-19 data is collected from the Kaggle website for the period of 22nd January 2020 to 20th April 2020 (i.e., for 90 days). The predicted figures of the spread of the disease have been estimated and compared with the actual values. Higher precision of the estimates has been observed from the MMLNN model compared to the conventional multilayer neural network (MLANN) model. Specifically, the MMLNN model does faster and more efficient training of the data resulting in less error. The paper forecasts the next day figures (i.e., for 21st April) for all the three cases and does the comparison of the results with the actual values reported. A deviation of 6% is obtained for India, and for the other three countries the deviation is below 3.5%. Given the high accuracy predictive power, the authors recommend that the MMLNN model can be integrated into the health policy of the countries that are struggling with the spread of the virus. Specifically, a decision on health policies such as restrictions on movement can be based on the short-range predictions of the spread of the virus infection.

CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses

There is an ever-urgent need for accessing real-time crowdedness and airflow information for indoor study spaces in universities, for example, to control COVID-19 transmission risk. Even before the pandemic, many students spent valuable time finding suitable study areas with proper lighting, low noise, and ample seating. This paper presents a pilot system, CampusX, which aims to provide students with useful real-time information about study spaces on campus. Our system collects and analyzes environmental data before presenting them to students as useful information. This helps them to select the most suitable study spaces. The main components of this system include a sensor platform, data collection and processing pipelines, networking, and an interactive web-application. © 2023 IEEE.

A deeper understanding of switching intention and the perceptions of non-subscribers

PurposeMost previous studies on new technologies and services have concentrated on their acceptance, seldom exploring in depth why users may choose not to accept technology or service and remain "non-users.” This study aims to understand free platform users' intention to switch to paid subscription platforms.Design/methodology/approachThis study utilized push-pull-mooring (PPM) theory to investigate free OTT platform viewers' switching intentions toward paid OTT platforms. A research model was established and examined via a two-stage partial least square (PLS) method. A total of 446 free users were collected from Facebook and Line for data analysis.FindingsResults show that perceived intrusiveness is the push factor and alternative attractiveness is the pull factor and that both have a positive impact on the switching intention of non-subscribers. Habit represents the mooring factor and negative affects switching intention. Perceived convenience and perceived enjoyment are shown to be two significant habitual antecedents. Furthermore, habit is revealed to moderate the effect of users' perceived advertisement intrusion and alternative attractiveness on switching intention to strengthen positive impact when the habit is strong.Originality/valueThis study is one of the pioneering studies to consider free-to-paid switching behavior on media services using PPM's structural equation model. Contrary to previous research, the study found that, in the context of the free-to-paid transition, highly accustomed users' perception of pull factors and push factors were strengthened, thus generating the tendency to switch platforms.