HerbNet: Intelligent Knowledge Discovery in MySQL Database for Acute Ailments
5 Pages Posted: 4 Apr 2019
Date Written: April 3, 2019
Artificial intelligence is omnipresent in every confabulated conversation of this century, as this paradigm had proven to be the ace in the deck of technology in the last five decades. Machine learning is one of the paths to be followed in order to achieve this intellect of a human by the computer i.e. Artificial Intelligence. From nanoscale to heavy mechanical machines the machine learning is being constantly applied everywhere including biotechnology and drug discovery processes. Biology holds a sharper edge as it offers huge data sets to be fed in the logic of machine learning algorithms. Knowledge discovery (KDD) in huge biological and relevant databases is a smart way to search relevant information which could be further utilized in process of drug discovery. And with the aid of Machine learning models, the accuracy and precision of KDD processes increase manifolds. In this article, we propose the working logic and relevance of an Artificial Neural Network model (ANN) to intelligently search a therapeutically relevant data set of common ailments along with their household remedies. The Artificial Neural Network algorithm is used to retrieve therapeutically important household remedies for quotidian ailments from the database based on pharmacologically independent user input variables. The Artificial Neural Network approach provides a personalized searching protocol based on an individual’s age, gender, height, weight, and ailments condition. The fetched results from the MySQL database include the ayurvedic home remedy along with its usage and dosage. This personalized prescribed therapeutic increases the probability of individuals shifting towards ayurvedic home remedies rather than antibiotics that eventually reduce the antibiotic resistance phenomenon and increases immunity towards common infections in long run.
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