Retinal Fundus Multi-disease Image Dataset (RFMiD) consisting of a wide variety of pathological conditions. 

Instructions: 

Detailed instructions about this dataset are available on the challenge website: https://riadd.grand-challenge.org/.

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This dataset brings some problem sets and results from some classical algorithms from the evolutionary computational community.

We have used some tools: Pymoo, Platypus and Pagmo

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Diabetic Retinopathy is the second largest cause of blindness in diabetic patients. Early diagnosis or screening can prevent the visual loss. Nowadays , several computer aided algorithms have been developed to detect the early signs of Diabetic Retinopathy ie., Microaneurysms. The AGAR300 dataset presented here facilitate the researchers for benchmarking MA detection algorithms using digital fundus images. Currently, we have released the first set of database which consists of 28 color fundus images, shows the signs of Microaneurysm.

Instructions: 

The files corresponding to the work reported in paper titled " A novel automated system of discriminating Microaneurysms in fundus images”. The images  are taken from Fundus photography machine with the resolution of 2448x3264. This dataset contains Diabetic Retinopathy images and users of this dataset should cite the following article.

 

D. Jeba Derwin, S. Tamil Selvi, O. Jeba Singh, B. Priestly Shan,”A novel automated system of discriminating Microaneurysms in fundus images”, Biomedical Signal Processing and Control,Vol.58, 2020, pages: 101839,ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2019.101839.

(http://www.sciencedirect.com/science/article/pii/S1746809419304203)

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Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.

Instructions: 

In folder 'referenceFronts', you can find the corresponding Pareto-Fronts (.pf) (comma seperated values) and -Sets (.ps)Deserilisation of the sets is possible through Gson/JSON. Each line contains all nodes of Path, delimited by '||'

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This dataset is a supplemental document fot the study '' Evolution of Controllers under a Generalized Structure Encoding/Decoding Scheme with Application to Magnetic Levitation System''.  

Detailed simulation and experiment results are included in the dataset, as well as the source code programmed  in Matlab.

Instructions: 

This file  'supplemental document. pdf' provides more detailed experimental results  and paramater sensitivity anlaysis  results.

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The dataset provides Abilify Oral user reviews and ratings for drug’s satisfaction, effectiveness, and ease of use on different age groups.

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The dataset has Gaussian Blobs of varying samples, centers and features.  The number of samples ranges from 500 to 50,000. Similarly, the number of centers varies from 2 to 100, while the number of features varies from 2 to 2048. These different sets of Gaussian blobs can be used for testing clustering algorithms for their scalability and effectiveness. There are two kinds of files inside the compressed sets. Files ending with "_X.csv" consist of datapoints, while the files ending with "_y.csv" represent respective class data.

Instructions: 

Please go through the documentation file before downloading the compressed zips. The PDF contains list of files that are within each compressed file.

The datapoints have real numbers up to 15 decimal places. The algorithm might converge, taking a long time because of such decimal precision. So if you need to round off the numbers, you can do that through DataFrameName.round(decimals=decimal_place).

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2169 Views

Most text-simplification systems require an indicator of the complexity of the words. The prevalent approaches to word difficulty prediction are based on manual feature engineering. Using deep learning based models are largely left unexplored due to their comparatively poor performance. We have explored the use of one of such in predicting the difficulty of words. We have treated the problem as a binary classification problem. We have trained traditional machine learning models and evaluated their performance on the task.

Instructions: 

The data is in CSV format. Please check the research paper for obtaining the difficulty label from the I_Z score.

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1763 Views

Cyber attacks are a growing concern for small businesses during COVID-19 . Be Protected While You Work. Upgrade Your Small Business's Virus Protection Today!

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Game Building statistical analysis

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