INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. To overcome this problem, we have created a dataset named FruitsGB (Fruits Good/Bad) dataset.


The data set contains 12 classes of fruits namely Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate.


The rapid outbreak of COVID-19 due to the novel coronavirus SARS-COV-2 is the biggest issue faced by mankind today. It is important to detect the positive cases as early as possible to prevent the further spread of this pandemic.


This dataset contains the trained model that accompanies the publication of the same name:

 Anup Tuladhar*, Serena Schimert*, Deepthi Rajashekar, Helge C. Kniep, Jens Fiehler, Nils D. Forkert, "Automatic Segmentation of Stroke Lesions in Non-Contrast Computed Tomography Datasets With Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 94871-94879, 2020, doi:10.1109/ACCESS.2020.2995632. *: Co-first authors



The dataset contains 3 parts:

  • Pre-processing: Script to extract brain volume from surrounding skull in non-contrast computed tomography (NCCT) scans and instructions for further pre-processing.
  • Trained convolutional neural network (CNN) to perform automated segmentations
  • Post-processing script to improve CNN-based segmentations


Independent Instructions for each part are also contained within each folder.


The PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield (UWF) fundus photography (FP). PRIME-FP20 provides 15 high-resolution UWF FP images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the valid data region for the images. For each UWF FP image, a concurrently captured UWF fluorescein angiography (FA) is also included. 


UWF FP images, UWF FA images, labeled UWF FP vessel maps, and binary UWF FP validity masks are provided, where the file names indicate the correspondence among them.


Users of the dataset should cite the following paper

L. Ding, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning,'' IEEE Trans. Medical Imaging, accepted for publication, to appear.



Several pathological phenomena are closely associated with mechanical properties of vessel and interactions of blood flow–wall dynamics. However, conventional techniques cannot easily measure these features. In this study, new deep learning-based simultaneous measurement of flow–wall dynamics (DL-SFW) is proposed by devising integrated neural network for super-resolved localization and vessel wall segmentation and combining with tissue motion measurement technique and flow velocimetry.


This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.


This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.


This dataset has been collected in the Patient Recovery Center (a  24-hour,  7-day  nurse  staffed  facility)  with  medical  consultant   from  the  Mobile  Healthcare  Service of Hamad Medical Corporation.


Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 


This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks.


This package only contains data which belongs to the work "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" (B. Matthiesen, A. Zappone, K.-L. Besser, E. Jorswieck, and M. Debbah, IEEE Transactions on Signal Processing, vol. 68, pp. 3887-3902).To use it, the source code included with this data set is required. The most recent version is available on GitHub.


The "results" directory contains the trained models.

  • "final4users" contains the big model used for the numerical evalution in the paper cited above.

  • "final16" is the smaller model analyzed in the last subsection of the numerical evaluation.

  • "final7users" contains the models of the 7 user scenario.


The "data" directory contains the channel data.

  • "channels-4.h5": Channels used for training and validation for 4 users

  • "channels-7.h5": Channels used for training and validation for 7 users


The zizania image dataset consists of a total of 4900 zizanias. The quantity of high quality samples is 2648 and defective quality samples is 2252.

There are four classes in the apple image dataset, which are apples with a diameter greater than 90 mm, between 80 mm and 90 mm, less than 80 mm, and diseases and insect pests. The quantity distributionin above categories are 3647 (51.19%), 2464 (34.59%), 558 (7.83%), 455 (6.39%).