[TIFS 2019] Skeleton-based Gait Recognition via Robust Frame-level Matching (RFM)

This project classifies diseases in grape plant using various Machine Learning classification algorithms.

A Credit Card Fraud Detection System using Adaboost and Majority Voting, designed to identify fraudulent credit card transactions by combining the strength of multiple classifiers.

I use an agent-based model to explore the impact of imperfect competence and social influence on majority voting. This repository contains the code for an agent-based model and simulations of majority voting, for producing some figures, and for a statistical analysis.

Application for soft voting algorithm demonstration

Worked on a classification analysis (class imbalance) for a business problem. Analysis was done using Anaconda Python.

Rust crate to manage majority judgment polls

We have developed a Hybrid Model which consists of Random Forest, K-Nearest Neighbors, and Artificial Neural Network Algorithms using the Majority Voting Approach for detecting frauds in Credit Cards effectively and efficiently🙂.

In this problem statement, a sequence of genetic mutations and clinical evidences, i.e. descriptive texts as recorded by domain experts are used to classify the mutations to conclusive categories, to be used for diagnosis of the patient.

The project demonstrates the effectiveness of combining AdaBoost and Majority Voting for credit card fraud detection, providing a reliable and accurate solution to combat fraudulent activities in financial transactions.