SignCraft: Sign Language Gesture Translator uses MediaPipe and Intel's oneAPI platform for real-time translation of sign language gestures. It bridges communication gaps by capturing and interpreting gestures, enabling effective communication between sign language and non-sign language users.
Our project
Efficiently trained with Intel® oneAPI: oneDAL, our Deep Learning Model enhances deepfake detection, ensuring robustness and accuracy. This agile approach offers superior protection against evolving threats in manipulated multimedia content.
Farm Friend is an intelligent farming system that harmoniously integrates IoT and AI technologies to optimize agricultural practices and enhance crop management for farmers.
Sexual harassment in workspace has become a serious issue nowadays. Our solution is an attempt to build a safer environment. We have developed 3 CNN-based models to detect this issue, complete CNN model, VGG16 model, Xception model. Out of which VGG16 gained us a maximum accuracy of 92%(approx.)
Project Description: In this project, we conducted a head-to-head comparison between two powerful object detection approaches, YOLOv7 native and Intel OneAPI Libraries, in the context of pothole detection. 🕳️
📌 Objective: Our goal was to determine which approach offered the best trade-off between
This is a project regarding the use of Artificial Intelligence in reducing the effort and time of taking attendance by scanning the face of the registered user. This is in the developing phase with integration of college cameras in classes for many classes. The GUI also shows the details like the
This is a Machine Learning project made using Intel's Extension for Tensorflow for the identification of various medicinal plants found near us. It is just a model yet to be deployed on a mobile application.
An expense tracker is a tool or application used to monitor and record one's financial expenditures. It helps individuals keep a close eye on where their money is going by categorizing and tracking expenses such as bills, groceries, entertainment, and more.
Predicting solar flares is of critical importance for mitigating the potential adverse effects of these intense bursts of radiation on power grids, GPS systems, and the safety of individuals in space. Intel OneAPI is powerful tools can play a pivotal role in building solar flares prediction systems.
Bird species identification is a deep learning project based on OneAPI-Tensorflow. The data includes a total number of 1856 audio records split into 40 bird species.
This project leverages Intel OneAPI libraries to create a versatile water quality prediction model. It ensures safe drinking water, aids environmental monitoring, and enhances industrial processes by analyzing diverse data sources and features.
Welcome to the English Premier League (EPL) Match Result Prediction Project! In this project, I have implemented three different classification algorithms - K-Nearest Neighbors (KNN), Naive Bayes, and DecisDecision Trees - to predict the outcomes of EPL matches.
, we can evaluate the quality of water based on a range of crucial parameters, allowing us to make informed decisions about its fitness for human consumption. The suggested solution offers comprehensive exploration of various machine learning models and techniques applied to this dataset. From
We have predicted the trend of how, why, when and where of spread of Covid-19 pandemic. This study done as part of Intel OneAPI hackathon is essential for tackling similar situations in the future, and making our world a safer place to live in.
Developed an advanced water quality prediction model using the Intel OneAPI Toolkit and seamlessly integrated it with a water-focused chatbot. This synergy enables real-time monitoring, and data-driven decision-making, revolutionizing water resource management and promoting environmental awareness.
Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants.