Machine Learning
Detecting Spam Email with Machine Learning Optimized with Harris Hawksās optimizer (HHO) Algorithm
Diagnosis of Liver Disease using ANN and MLAlgorithms with Hyperparameter Tuning
The aim of this project is to develop a system for the diagnosis of liver disease using Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms with hyperparameter tuning. The project focuses on leveraging advanced models and optimization techniques to enhance predictive capabilities, aiding in the early detection and effective management of liver disease.
Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches
E-Commerce Fraud Detection Using Generated Data From BANKSIM Using Machine Learning
Early Detection of Childhood Malnutrition using Survey Data and Machine Learning Approaches
Efficient Machine Learning Approach For Crime Detection In India
The goal of this project is to create a reliable model for predicting droughts in regions that are vulnerable to them. Using Indian rainfall data, the project applies ARIMA and SARIMAX models to forecast droughts. The project aims to support better planning and response strategies, helping communities prepare for and mitigate the effects of droughts.
Efficient Machine Learning Approach for Crime Detection in India
Efficient Machine Learning Models for Solar Radiation Prediction Using Ensemble Techniques: A Case Study in Low-Rainfall Arid Climates
Enhancing Smishing Detection A Deep Learning Approach for Improved Accuracy and Reduced False Positives
The aim of this work is to explore and develop advanced methods for enhancing the detection and prevention of smishing attacks. This involves utilizing cutting-edge technologies such as machine learning, artificial intelligence, and behavioral analysis to identify and block fraudulent SMS messages, protecting users from financial and personal data theft. The goal is to create more effective, real-time detection systems to mitigate the growing threat of smishing attack




