Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional ...

A new study finds that many popular image datasets used to train AI models are contaminated with test images or near-duplicates, allowing models to cheat by memorizing answers instead of learning. The ...

A machine learning project to classify Iris flower species (Setosa, Versicolor, Virginica) using physical features. Implemented using Jupyter, Flask, Streamlit, and JavaScript. Part of CodSoft ...

This repository contains the code for the study "Self-Supervised Learning Advances Crop Classification and Yield Prediction". The project aims to train, and evaluate a convolutional neural network ...

Feature Selection for Self-Supervised Classification With Applications to Microarray and Sequence Data Abstract: Learning strategies are traditionally divided into two categories: unsupervised ...

The prediction of disease risk using SNP genotype data can be considered as a binary classification problem within supervised learning. There is a generalized workflow for creating a predictive ML ...

In remote sensing image classification, really it is an intimidating when kernel supervised learning approaches stands in need of adequate amount of training samples. Often there is a vital problem ...