10 skills found
outbreak-info / Outbreak.infoDuring outbreaks of emerging diseases such as COVID-19, efficiently collecting, sharing, and integrating data is critical to scientific research. outbreak.info is a resource to aggregate all this information into a single location.
shhubhxm / Skin Diseases Detection HackbashDermatological Issues/disorders are most commonly spread worldwide. This can be caused by various fungal, bacterial, or skin allergies. Effective use of Emerging technologies like AI/ML can recognize such diseases. Computer Vision is one such platform that made the possibility of detecting the cause accurately through Images.The problem here is to develop an Application Programming Interface which can be easily integrated with Android app to detect the skin disease without any physical interaction with a Dermatologist.
roidtman / Eid Ensemble Forecastingcode and data for "Trade-offs between individual and ensemble forecasts of an emerging infectious diseases"
yugantgajera / Dilated Inception U Net For Nuclei Segmentation In Multi Organ Histology ImagesMedical image processing using machine learning is an emerging field of study which involves making use of medical image data and drawing valuable inferences out of them. Segmentation of any body of interest from a medical image can be done automatically using machine learning algorithms. Deep learning has been proven effective in the segmentation of any entity of interest from its surroundings such as brain tumors, lesions, cysts, etc which helps doctors diagnose several diseases. In several medical image segmentation tasks, the U-Net model achieved impressive performance. In this study, a Dilated Inception U-Net model is employed to effectively generate feature sets over a broad region on the input in order to segment the compactly packed and clustered nuclei in the Molecular Nuclei Segmentation dataset that contains H&E histopathology pictures. A comprehensive review of published work based on deep learning on this dataset has also been exhibited.
Sanin-Lab / Macrophage FrameworkMacrophages populate every organ during homeostasis and disease, displaying features of tissue imprinting and heterogeneous activation. The disjointed picture of macrophage biology that has emerged from these observations can be difficult to integrate across models or with in vitro macrophage activation paradigms. For these reasons, we set out to contextualize macrophage heterogeneity across tissues and inflammatory conditions, specifically aiming to define a common framework of macrophage activation. We built a predictive model with which we mapped the activation of macrophages across 12 tissues and 25 biological conditions, finding a strikingly common and finite number of transcriptional profiles which we modelled as stages along 4 conserved activation paths. We verified this model with adoptive cell transfer experiments and identified transient RELMɑ expression as a feature of macrophage tissue engraftment. We propose that this integrative approach of macrophage classification allows the establishment of a common predictive framework of macrophage activation that may serve to contextualize the future study of these cells.
ecohealthalliance / Hotspots2This code powers the models for the paper "Global hotspots and correlates of emerging zoonotic diseases".
ecohealthalliance / PubcrawlerPubCrawler uses the EpiTator framework (known at time of writing as Annie) to resolve toponyms in the PubMed Central Open Access Subset. It was used to create the estimate of reporting bias in the paper "Global hotspots and correlates of emerging zoonotic diseases".
dv516 / RNA Transcription Modelling And DSModel Implementation and Design Space Construction to support 'Quality by Design modelling for rapid RNA vaccine production against emerging infectious diseases'
FBK-OpenIoT / PlantVillage AugNoLeavesThis repository contains the code and data representing outcomes of the paper "A TinyMLOps-Based Edge AI Approach for Early Detection of Emerging Plant Diseases," accepted at IEEE MetroInd4.0&IoT 2025.
MadhavSingh2236 / Parallization Of CPU And GPU For Plant Disease DetectionImage classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation. It has always been cumbersome to process real time images. Studies showed that there can be two ways to analyse this. One hand is about central processing unit (CPU) and the other is about Graphics Processing Unit (GPU). To obtain highest possible performance they have to be used at the same time. This project will compare the performance of CPU and GPU for real time image processing. The main drawback of Python’s Multiprocessing module is that it cannot be used for handling large numeric data. It cannot be used in Deep Learning Frameworks such as Keras as it decreases the accuracy of the models. In recent years, parallel computing and soft computing has become a rapidly evolving field of study. The demand for parallel processing in increasing day by day. There are various software tools and libraries by which we can parallelize our programs. For example, we have OPENMP in c++ for parallel computing. OPENMP supports FORTRAN, C and C++. It is basically an Application Programming Interface for shared Memory Model programming. Python has its separate parallel processing module named Multiprocessing. Multiprocessing module enables to spawn multiple processes, allowing programmer to fully leverage the computing power of multiple processors. The main drawback of Python’s Multiprocessing module is that it cannot be used for handling large numeric data. It cannot be used in Deep Learning Frameworks such as Keras as it decreases the accuracy of the models. Shared variables cannot be used in the Multiprocessing Module. Python also has a Parallel and Distributed computing framework called Ray. Ray can be used for developing emerging AI applications such as image classification, face recognition etc. Parallelizing multiple cores of CPU using Ray can also increase the speedup of the model significantly. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant 4 Disease Image dataset containing around 30000 images. The system is configured with 16 GB RAM with 4 CPU Cores and Tesla P100 GPU. This project compares the performance of 2-core, 3-core and 4-core parallelized CPUs with GPU.
