Coronavirus (COVID-19) is an infectious disease that has resulted in an ongoing pandemic. The disease was first identified in Wuhan, China, and the first case was identified in December 2019. As of 21st August 2020, more than 22 million cases have been reported across 180 countries and territories. The sheer scale of this pandemic has led to myriad problems for the current generation. One of the acute problems that I have come across is the circulation of bogus news articles and in today’s world, spurious news articles can cause panic and mass hysteria. I realized the gravity of this problem and decided to base my next machine learning project on resolving this issue.
To develop a fake news classifier that appropriately classifies a news article on COVID-19 into real news or fake news.
Before starting with this project, I had to search for datasets that had a list of news articles related to COVID-19. This was a challenge since there are not many datasets out there that record COVID-19 news articles. After scouring the internet for days, I finally found a data set that had news articles related to COVID-19. The only task required now was to clean the data, fit the appropriate machine learning model on it, and assess the model’s performance.
Step 1: Checking for missing values.
I started the project by exploring the data and looking out for missing values in it. Each column in the data set had some missing values in it but most importantly, the “Label” column had 5 missing values. Fortunately, the source from where I downloaded the data set had values for the missing labels and that helped me to eliminate missing values from the “Label” column. As for the other columns i.e. “Title”, “Source” and “Text”, the missing values were replaced with an empty string.
Step 2: Looking for inconsistencies in the “Label” column.
After handling the missing data, I thought of checking the target labels to look for any inconsistencies that may be present. After exploring the “Label” column, I discovered two different Fake labels, the same can be seen in the image below. After discovering this anomaly, I decided to change the label for fake news. Final labels can be seen in the second image given below.
#data-science #towards-data-science #nlp #machine #covid19
Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.
The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.
We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”
Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.
#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script
With the current pandemic spreading like wildfire, the requirement for a faster diagnosis can not be more critical than now. As a matter of fact, the traditional real-time polymerase chain reaction testing (RT-PCR) using the nose and throat swab has not only been termed to have limited sensitivity but also time-consuming for operational reasons. Thus, to expedite the process of COVID-19 diagnosis, researchers from the University of Oxford developed two early-detection AI models leveraging the routine data collected from clinical reports.
In a recent paper, the Oxford researchers revealed the two AI models and highlighted its effectiveness in screening the virus in patients coming for checkups to the hospital — for an emergency checkup or for admitting in the hospital. To validate these real-time prediction models, researchers used primary clinical data, including lab tests of the patients, their vital signs and their blood reports.
Led by a team of doctors — including Dr Andrew Soltan, an NIHR Academic Clinical Fellow at the John Radcliffe Hospital, Professor David Clifton from Oxford’s Institute of Biomedical Engineering, and Professor David Eyre from the Oxford Big Data Institute — the research initiated with developing ML algorithms trained on COVID-19 data and pre-COVID-19 controls to identify the differences. The study has been aimed to determine the level of risk a patient can have to have COVID-19.
#opinions #covid screening #covid-19 news #covid-19 screening test #detecting covid
COVID-19 cases have only been on the rise. With the non-availability of effective drugs and vaccines, one of the effective ways to control it is to detect it early in patients. However, the task is easier said than done. While a large number of test kits are being produced, they are not enough to conduct testing in large numbers.
Government-run body, C-CAMP or Centre for Cellular and Molecular Platform, has been a key enabler in driving COVID-19 testing as it has been aggressively building, managing and scaling the ecosystem of MSMEs to produce test kits indigenously. However, they might not be enough.
#opinions #c-camp #c-camp tcs #covid-19 #covid-19 testing #tcs #tcs covid-19
Ever read a piece of news which just seems bogus? We all encounter such news articles, and instinctively recognise that something doesn’t feel right. Because of so many posts out there, it is nearly impossible to separate the right from the wrong. Here, we are not only talking about spurious claims and the factual points, but rather, the things which look wrong intricately in the language itself.
Did you ever wonder how to develop a fake news detection project? But there is no easy way out to find which news is fake and which is not, especially these days, with the speed of spread of news on social media. Still, some solutions could help out in identifying these wrongdoings.
There are two ways of claiming that some news is fake or not: First, an attack on the factual points. Second, the language. The former can only be done through substantial searches into the internet with automated query systems. It could be an overwhelming task, especially for someone who is just getting started with data science and natural language processing.
The latter is possible through a natural language processing pipeline followed by a machine learning pipeline. It is how we would implement our fake news detection project in Python. It is another one of the problems that are recognized as a machine learning problem posed as a natural language processing problem.
There are many datasets out there for this type of application, but we would be using the one mentioned here. The data contains about 7500+ news feeds with two target labels: fake or real. The dataset also consists of the title of the specific news piece.
The steps in the pipeline for natural language processing would be as follows:
#data science #fake news #fake news detection #fake news detection project #python project #python project ideas
Short news apps are the future, and if they will play a defining role in changing the way consumers consume their content and how the news presenters write their report.
If you want to build an app for short news then you can check out some professional app development companies for your app project As we head into the times where mobile applications and smartphones will be used for anything and everything, the short news applications will allow the reader to choose from various options and read what they want to read.
#factors impacting the short news apps #short news applications #personalized news apps #short news mobile apps #short news apps trends #short news apps