Deliver uncompromised performance for diverse workloads across multiple architectures.
Reducing the carbon footprint by increasing the use of solar panels and decreasing the use of Coal for producing electricity, ultimately reducing carbon footprint and saving the earth from some catastrophic event/ uncertainty.
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URL: https://github.com/iurk1702/intelOneAPI_submission
Refugee status determiner using OneAPi toolkit
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URL: https://github.com/sayashraaj/oneapi
Obtain a differentially private dataset mirroring the original while safeguarding individual privacy
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URL: https://github.com/AKHILVANKAYALA99/oneAPI-Online-AI-Hackathon/blob/main/oneAPI_Hackathon.ipynb
The project aims to predict water quality, with a focus on its suitability for consumption. Access to safe water is crucial for human survival and ecosystem preservation. The solution utilizes techniques like SMOTE and uses Random Forest Machine Learning algorithm.
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URL: https://github.com/scholar-2001/Semi_labelling/tree/master
Creating an open source auto labelling tool that labels image using pretrained models. We can then edit those labelling too using this tool. Currently, only supported for yolov5 models but working on expanding it. Also, it is using stable PyTorch in backend for better acceleration by intel libraries
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URL: https://github.com/VinayVaishnav/Water_Quality_Prediction
Using Intel's AI Analytics Toolkit we tried to predict freshwater quality, achieving an F1 score of 0.81786 with TabNet ensemble model, addressing data challenges and optimizing performance for sustainable water assessment.
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URL: https://github.com/Senthil-Riddhish/Intel-One-Api-Hackathon
Harnessed the power of Intel technologies to enhance the capabilities of the YOLOv5 algorithm for object detection. By leveraging Intel's optimized libraries and frameworks, such as Intel oneDAL, Intel optimized PyTorch, and the SYCL/DPC++ libraries, we have achieved superior performance, accuracy,
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URL: https://github.com/Ashutoshjha0007/intelunnati_AshutoshJha/tree/main
The Fashion-MNIST dataset is used as a standard for assessing how well image classification models perform. Classifying fashion items presents a difficult task that is applicable to real-world applications. I have tried to add to the body of knowledge in the field of computer vision by creating
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URL: https://github.com/shruthi2608/CureQuest/
AI revolutionizes health: Predict. Prevent. Prevail
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URL: https://github.com/SSKlearns/IntelOneAPI/
Trained ResNet-50 for 5 epochs, optimized with IPEX for 15 epochs. Converted to ONNX, further refined with OpenVINO, all on Intel Dev Cloud. The Gradio app takes video input and detects accidents.
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URL: https://github.com/saianurag234/APS-Sensor-Fault-Detection
The APS Sensor, a critical element in heavy-duty vehicles, facilitates brake pad pressure by converting compressed air into piston force for deceleration. This study investigates the correlation between component failures and the APS.
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URL: https://github.com/AghoraGuru/1API
An interactive image upscaling application, combining Gradio and OpenVINO. The gradio app offers users the ability to upload images and experience real-time upscaling.
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URL: https://github.com/SuperSecureHuman/Segmentation_OneAPI
Implemented inference of segmentation network with oneAPI. Compressed the model with NNCF for openVINO.
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URL: https://github.com/nikibeep/first_aid
The project aims to develop an AI-driven voice-assisted first aid and medical condition detection system to provide real-time rescue techniques to bystanders during emergencies. This project demonstrates the implementation of speech-to-text and text-to-speech functionality using Python and Google Cl
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URL: https://github.com/Dishanth-Prabhakar/DermNet.git
DermNet - A skin disease prediction application that takes dermoscopic and normal images as input and predicts the output for 32 different classes of skin disease, which involves the use of advanced deep learning techniques and medical knowledge.
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URL: https://github.com/Kevin-Benny/IntelOneAPI_DepressionAnalysis
This study focuses on the development of an innovative depression prediction model using the Intel OneAPI framework, with the application of the powerful Random Forest Classifier. The main aim is to create a cutting-edge tool capable of accurately identifying and forecasting depressive tendencies in
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URL: https://github.com/kp-Captain02/The_Game_Changers.git
This project uses geographic information system (GIS) data, satellite imagery, and clustering techniques to identify and map green spaces within urban areas. The primary goal is to combat global warming and address climate change by promoting efficient urban planning strategies prioritising regions
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URL: https://github.com/neilxndr/MedBot
Introducing a healthcare chatbot using advanced encoder-decoder architecture with attention. It offers personalized, round-the-clock medical advice, leveraging a vast knowledge base. By tailoring responses and analyzing symptoms, it empowers users with accurate information, bridging technology and h
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URL: https://diagnosify.streamlit.app/
Diagnosify is an innovative brain disease classification project designed to leverage the power of machine learning and Intel oneAPIs for accurate and efficient brain disease prediction. In the modern healthcare landscape, early and accurate diagnosis plays a pivotal role in ensuring timely medical
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URL: https://github.com/ANNDANTIES/Hackathon.git
Our project offers a user-friendly platform where users can upload a photo of their hair. Our advanced image analysis model swiftly identifies whether the hair is straight, curly, or normal. Beyond classification, we provide tailored recommendations for hair care products that best suit the hair.
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