Evidence map›Paper›PMID 36743794›Full record

ArticleProcedia computer science2023

Data Mining Based Techniques for Covid-19 Predictions.

Rahul Rane, Aditya Dubey, Akhtar Rasool, Rajesh Wadhvani

Abstract read
In one paragraph

Article in Procedia computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rahul RaneDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India.
Aditya DubeyDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India.
Akhtar RasoolDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India.
Rajesh WadhvaniDepartment of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal and 462003, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.

Indexed as

Deep Learning ModelsSoft Computing-based ModelsStochastic Forecasting ModelsSupervised ML Models

Identifiers

PMID36743794
PMCPMC9886325

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.