People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
Recent advance in machine learning has made face recognition not a difficult problem. But in the previous, researchers have made various attempts and developed various skills to make computer capable of identifying people. One of the early attempt with moderate success is eigenface, which is based on linear algebra techniques.
Pronouncing words in an error-free and correct way is one of the most relevant skills to acquire. Scientists all over the world are concentrating on identifying pronunciation mistakes with the help of machine learning. This paper describes calculating pronunciation accuracy of a person using machine learning.
In today's fast-paced digital world, e-commerce platforms have become a significant part of our daily lives. As more and more people shop online, e-commerce companies strive to enhance user experiences and boost sales by providing personalized product recommendations. The "E-commerce Product Recommendation" project aims to develop a recommendation system that suggests products to users based on their historical interactions and preferences.
Trip Advisor is a popular travel website where users can write reviews about hotels, restaurants, and other travel-related services. These reviews provide valuable insights into the quality, customer experience, and overall satisfaction of various establishments. Analyzing these reviews can help businesses understand customer preferences, improve their services, and make informed decisions.
The objective of this project is to build a Convolutional Neural Network (CNN) model that can accurately detect the presence of brain tumors in Magnetic Resonance Images (MRI). This project aims to help healthcare professionals in identifying brain tumors, which can lead to early detection and treatment.
Image segmentation is a fundamental task in computer vision that involves partitioning an image into multiple segments, where each segment corresponds to a different object or region in the image. The task of image segmentation is challenging due to the high variability of objects and scenes in natural images. Image segmentation has numerous applications in different fields such as autonomous driving, medical imaging, and surveillance.
