Explore projects
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This is the unpaired image-2-image and volume-2-volume translation project. It converts images or volumes of an input domain to a target domain using artificial intelligence.
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This project provides a deep learning approach to learn machining features from CAD models using a hierarchical graph convolutional neural network.
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Developed a project utilizing Generative Adversarial Networks (GANs) to convert grayscale images to RGB color images. Leveraged deep learning techniques to train the GAN model on a dataset of grayscale and corresponding color images, achieving realistic colorization results. This project demonstrated proficiency in image-to-image translation and advanced deep learning methodologies within the realm of computer vision.
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Real-time Gender and Age Recognition from Audio
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fra-wa / AiSeg
GNU General Public License v3.0 or laterThis is a project to train, use and analyze 2D and 3D neural networks for segmentation.
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FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
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ICT deep learning lab
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End-to-end meme template extractor & enhancer
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Controversy quantification of topics on twitter, based on user probability to participate in a controversy topic, using GNN and NLP models.
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Tangible AI / public / alt-text-generator
GNU General Public License v3.0 or laterGenerate ALT text (captions for low vision website users or book readers).
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A trainable AI with data in text format. Deterministic
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The aim of this project is to provide an exploratory analysis of Domain Adaptation (DA) techniques in the context of PHM for Bearings fault prognosis, focusing on Health Index (HI) estimation and Remaining Useful Life (RUL) prediction. The adopted dataset is the PRONOSTIA/FEMTO-ST bearings dataset.
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A project focused on weather classification using advanced deep learning techniques, specifically leveraging TensorFlow and a custom Convolutional Neural Network (CNN). The project involved the integration of four diverse weather datasets, namely ACDC, MWD, UAVid, and Syndrone, covering various weather conditions, including clear sky, cloudy, rainy, and sunny weather. Developed a custom CNN architecture using TensorFlow's Keras API, incorporating convolutional layers for feature extraction and dense layers for classification.
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Johann Benerradi / BenchNIRS
GNU General Public License v3.0 or laterBenchmarking framework for machine learning with fNIRS
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A little more about me... Graduated in Bachelor of Information Systems, in college I had contact with different technologies. Along the way, I took the Artificial Intelligence course, where I had my first contact with machine learning and Python. From this it became my passion to learn about this area. Today I work with machine learning and deep learning developing communication software. Along the way, I created a blog where I create some posts about subjects that I am studying and share them to help other users.
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Repository to generate synthetic MRI from q*MRI maps with deep learning physics-informed approached as described by Jacobs Luuk et al. Medical Physics 2023 "Generalizable synthetic MRI with physics-informed convolutional networks".
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Code for Graph Representation of 3D CAD models for Machining Feature Recognition with Deep Learning paper. This is an approach using graph neural networks to learning from planar B-Rep CAD models.
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