Covid

Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall.
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This page is a 2 part submission, for which2 open- access pages are used. Research Papers related with each part are mentioned in that part page only.
In this Part-I, the common building blocks, libraries, fundamental theories(made by me only), are given, so that huge dataset, models, all can be comprehensible to doctors, professors, researchers. These are given
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Image : Image was made by me for other International Contest (held by some Medical Institute,USA in the year 2021), 'An intuitive of electromagnetic radiation flowing over epithelial tissue'.
Related Research paper and book* - 'Electro-Magnetic Radiations and Human Body : Magnetism Either Flows And Can Heal or Disable or Annihilate' and 'Observational Analysis of Common & Abnormal Electromagnetic Fields'.
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This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey.
Currently dataset contain 4,8 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 6 May 2020.
The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file:
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