Kuldeep Charan

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V V S Vijaya Krishna

All India Institute of Medical Sciences Bhubaneswar

Veeresh G. Kasabegoudar

Central University of Karnataka

Harsh Agarwal

Vellore Institute of Technology University

S. Harsha Vardhan

Siddhartha Medical College

P. Someshwar Goud

Jalesh Kumar

Jawaharlal Nehru Medical College

Cooperative Institutions

All India Institute of Medical Sciences Bhubaneswar

Anna University, Chennai

Indian Institute of Technology Hyderabad

Central University of Karnataka

Indian Institute of Science Bangalore

Vellore Institute of Technology University

Manipal University Jaipur

Indian Institute of Space Science and Technology

Indian Institute of Technology Jodhpur

Indraprastha Institute of Information Technology Delhi

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Weather prediction using Machine Learning

International journal of advance research, ideas and innovations in technology (2020)

Harsh Agarwal Kuldeep Charan S. Harsha Vardhan Jalesh Kumar P. Someshwar Goud

The burgeoning research in the fields of Artificial Intelligence and machine learning has given rise to numerous weather prediction models. But the problem of accurately predicting or forecasting the weather persists. Numerical weather prediction is taking the existing numerical data on weather conditions and applying machine learning algorithms to forecast the weather. This paper is the application of machine learning algorithm-decision Tree to predict the weather based on a few parameters such as temperature, pressure, humidity. Using the stepwise regression process along with the decision tree, weather forecasting is achieved.

Weather prediction

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Stepwise regression

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Phishing Detection Using Artificial Intelligence

Design Engineering (2021)

Kuldeep Charan Veeresh G. Kasabegoudar

The essential target of our endeavor is to do the machine learning technique to detect Phishing and Malicious websites. The end solution or the result of this project will be a software product which will help in detecting the phishing and malicious websites. Phishing is a technique to extract user credentials and sensitive data by the phishers. The phishing website is usually designed in such a way that they mirror the actual legit website, where not so regular users miss to identify whether the website is malicious or phishing blacklisting the known phishing websites or check the attributes in the phishing page whether it contains any malicious codes. This process has poor accuracy and very low adaptability to new phishing links. That is the reason that we have designed this project where we generated an idea to implement machine learning technique by using some classification algorithms and comparing these algorithms with our existing data set. we'll test assessments, for instance, SVT, Decision tree, and Neural relationship on a dataset of phishing joins from the UCI Machine Learning account, and pick the simplest model to support a program module, which may be flowed as a chrome advancement. The phishing is standard technique that duplicates trustful URL and site page links. The major need is to style AI type on a dataset created for phishing avoidance.

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