WITHDRAWN: Realtime analysis and visualization of data for instant decisions: A futuristic requirement of the digital world

Goswami, Pankaj K.; Sharma, Avinash

doi:10.1016/j.matpr.2021.02.193

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Data security needs to be maintained when sharing such data. Various tools are used to analyze data, such as SAMOA [106] and Mahout [107] for mining big data and Tableau for big data visualization [108].…”

Section: Big Data Analytical Layermentioning

confidence: 99%

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

et al. 2022

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The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.

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Section: Big Data Analytical Layermentioning

confidence: 99%

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

et al. 2022

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“…Time series analysis helps in analysing the huge data to find the trend or seasonality behind it. Real time data analysis plays a vital role in creating human intelligence [6]. Various extended models such as VAR, Vector Auto-Regressive Moving Average (VARMA) have been extensively used in multivariate time series analysis for PM2.5 [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ensemble Multivariate Time Series Forecasting Model for PM2.5

Muruganandam¹,

Arumugam²

2023

Computer Systems Science and Engineering

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In forecasting real time environmental factors, large data is needed to analyse the pattern behind the data values. Air pollution is a major threat towards developing countries and it is proliferating every year. Many methods in time series prediction and deep learning models to estimate the severity of air pollution. Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality. This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter (PM) PM2.5. To perform experimental analysis the data from the Central Pollution Control Board (CPCB) is used. Prediction is carried out for Chennai with seven locations and estimated PM's using the weighted ensemble method. Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction. Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting. Computational time of ensemble decreases with parallel processing in each sub model. Weighted ensemble model shows high performance in long term prediction when compared to the traditional time series models like Vector Auto-Regression (VAR), Autoregressive Integrated with Moving Average (ARIMA), Autoregressive Moving Average with Extended terms (ARMEX). Evaluation metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the time to achieve the time series are compared.

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