One should recollect the USA 2015 and 2016 U.S. presidential election cycle dealt with numerous scandals which were triggered by the forged news articles that blowout through the social media like Twitter and Facebook. When it was found that these articles were purposefully uploaded
for financial and political gain, it’s become evident that bogus news has to be identified and removed to prevent public from being deceived for someone’s personal gain. This study builds a supervised machine language model to detect the fake news articles published during 2015
and 2016 U.S. election cycle. The data set contains identical number of bogusand factual news. The standard set of machine learning algorithms like K-Nearest Neighbors, Support Vector Machine, Naive Bayes and Passive Aggressive Classifier are trained using either the title or the content
of the article. There results show that the PAC classifier produces the highest accuracy of 94.63% over the other three classifiers using diagram term frequency.
Machine learning has become an essential tool for drug research to generate pertinent structural information to design drugs with higher biological activities. In this paper, we used python program language on pyrazoline scaffold, which is collected from diverse literature for the inhibition of Mycobacterium tuberculosis. Pyrazoline, a small molecule scaffold could block the biosynthesis of mycolic acids, resulting in mycobacteria death and leading to anti-tubercular drug discovery. The generated QSAR model afforded the ordinary least squares (OLS) regression as R2 = 0.380, F=4.909, and Q2 =0.303, reg. coef_ developed were of 0.00651593 (molecular weight), -0.0069445 (hydrogen bond acceptor), - 0.07576775 (hydrogen bond donor), -0.239021 (LogP) and reg. intercept of 3.10331589018553 developed through statsmodels.formula module. The support vector machine of the sklearn module generated the model score of 0.6294242262068762, the developed model was cross-validated by using the test set compounds and plotting the linear curve between the predicted and actual pMIC50 value. We have found that the values obtained using this script correlated well and may be useful in the design of a similar group of pyrazoline analogs as anti-tubercular agents.
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