The goal of this project is to build a house prediction API by using the oneAPI machine learning frameworks; Scikit-learn, XGBoost, and an open VINO toolkit. To build and deploy my machine learning model in order to integrate them into other applications.
Cryptocurrencies are fast becoming rivals to traditional currency across the world. The digital currencies are available to purchase in many different places, making it accessible to everyone, and with retailers accepting various cryptocurrencies it could be a sign that money as we know it is about
This project involves ETL and a Machine Learning Pipeline implementation, that classifies messages related to disaster response, and covering multiple language disasters, with a disaster emergency response using a Flask Web App.
Driver drowsiness is a severe issue that causes many road accidents every year. Drowsy driving can lead to accidents due to decreased reaction time, impaired decision-making, and impaired driving performance.
The students always tend to have the issue of figuring out which lift to use to reach their class floor at the peak hours. But to choose the optimal elevator could be worrisome and time consuming. But with the use of this AI/ML model that can figure out which lift to take to reach the desired floor
The Class Monitoring System using AI and ML technology is designed to monitor real-time student behaviour in the classroom. It provides teachers and school administrators with valuable insights into student engagement, attendance, and behaviour. This system is particularly useful in identifying stud
RespiScan 2.0 is a groundbreaking project aimed at enhancing lung cancer prediction through two innovative modes of analysis. Leveraging the power of Intel oneAPI, RespiScan 2.0 offers a comprehensive approach to early detection and prevention of lung cancer.
This is a heart disease prediction application which helps the doctors make informed decision about the heart disease of their patient’s. This application uses machine learning algorithms to analyse patient data and predict the likelihood of developing heart disease.
Implementation of a linear regression model and a neural network, two categories of machine learning models. We are utilising a dataset from the nutritional evaluations and constituents of several cereals.
Forest fires wreaking havoc and destroying several irreplaceable ecosystems is common news in the past few years owing to climate change. The best solution to the issue is having a reliable detection system, which can pick up on the early signs of a forest fire and inform the authorities immediately
The technology that we are proposing is a system that converts sign language to speech, allowing those who are deaf or hard of hearing to communicate more effectively with those who do not understand sign language.
Sentiment Analysis is a popular Natural Language Processing (NLP) task that aims to classify the sentiment of a given text as either positive, negative or neutral. In this project, IMDb reviews are used to train and evaluate a Sentiment Analysis model using the oneDAL too
AI and ML can revolutionize road management by analyzing real-time data from sensors and cameras to detect issues and alert authorities, leading to decreased accidents and infrastructure damage. Data generated can train and refine algorithms, leading to more efficient road management.
Plant disease prediction using AI and ML uses artificial intelligence and machine learning techniques to predict plant diseases accurately. This approach utilizes various technologies, including image processing, data analysis, and predictive modeling, promptly and diagnosing plant diseases.
The prevalence of online hate speech and harassment is a significant social issue with potentially severe consequences for individuals and groups. Machine learning has the potential to aid in combating this problem by analysing large amounts of data to identify abusive behaviour patterns.