mdpi
There are 8 repositories under mdpi topic.
ncornette/gimp-android-xdpi
Gimp plugin to write images and icons for all android densities
pgomba/MDPI_explorer
A simple package to explore MDPI´s articles by journal. A series of functions help to obtain lists of papers, obtain data from them (turnaround times, special issues and articles types) and create summary graphs.
nelsoncardenas/MDPI-Alternative-Style-for-Zotero-Mendeley
Actual csl file for MDPI reference in Zotero repository doesn't use DOI. This is an alternative .csl archive for MDPI format with DOI detection.
datngo93/OTM-AAL
This is the MATLAB source code of a haze removal algorithm published in Remote Sensing (MDPI) under the title "Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light". The transmission map was estimated by maximizing an objective function quantifying image contrast and sharpness. Additionally, an adaptive atmospheric light was devised to prevent the loss of dark details after removing haze.
ddaedalus/ethnopharma-set
[Applied Sciences] Official classification dataset for ethnopharmacology
nar789/iconcopier
Iconcopier-cli help you to copy icon files of various sizes to android resource folder easily.
sarkerrabi/Malaria-detection-with-ML-kit
Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named general training, distillation training and autoencoder training to optimize and improve the model accuracy and inference performance. Our deep learning-based model can detect malarial parasites from microscopic images with an accuracy of 99.23% while requiring just over 4600 floating point operations. For practical validation of model efficiency, we have deployed the miniaturized model in different mobile phones and a server-backed web application. Data gathered from these environments show that the model can be used to perform inference under 1 s per sample in both offline (mobile only) and online (web application) mode, thus engendering confidence that such models may be deployed for efficient practical inferential systems.
myst-templates/mdpi
A template for the MDPI range of Journals