Intel oneAPI Based Emotion Recognition�using NLP (Audio & Text)
Human-Computer interactions are make it mandatory to get accuracy communications, like both human. If computer identify means we will get clever interaction so NLP based recognition gives better accuracy.
The problem is to predict the type of customer visiting a hotel based on various attributes related to the customer and the hotel. The classification of the customer type could be a contract, transient, group, etc.
This project detects forest cover and the barren land in a specific area and predicts soil and temperature in that specific area(barren land) and recommends the tree which can thrive at that environment.
The drowsiness detection system monitors the driver's condition and issues an alert if it detects signs of drowsiness using CNN - Python, OpenCV.
This system aims to reduce the number of accidents on the road by detecting the driver's drowsiness and warning them using an alarm.
The project aims to provide properties based on the Habitability Index for people finding properties to rent. This Habitability index is found by an ML model that's backed by oneAPI.
Image classification for recycling refers to the use of machine learning to automatically classify images of waste materials into their respective categories. We have made use of Intel's oneAPI and its oneDNN library which provides highly optimized routines for various deep learning operations.
We aim to create an accurate and efficient model that can determine fresh water quality based on various factors such as source, location, season, etc. The repository contains the code and data used in the development of the model, as well as the results and findings of the project.
OMPify is a tool for creating a comprehensive database that contains multiple modalities of code, including source code, AST format, etc. Using this database, OMPify can be used to train large language models, allowing them to learn the semantics of code and generate OpenMP pragmas automatically.
This project aims to build an OneAPI-supported EEG-based BCI system for paralyzed patients to communicate with the world. With the help of EEG signals, this project decodes the words/speech thought inside the brain and identifies them with the help of machine learning models.
The objective of the study is to analyze the flight booking dataset obtained from “Ease My Trip” website and to conduct various statistical hypothesis tests in order to get meaningful insights from it. Ease My Trip is an internet platform for booking flight tickets.
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.