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.


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.



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.


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 '||'


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.


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


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


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.


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).


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.


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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Game Building statistical analysis


The data uploaded here shall support the paper 

Decision Tree Analysis of  ...

which has been submitted to IEEE Transactions on Medical Imaging (2020, September 25) by the authors

Julian Mattes, Wolfgang Fenz, Stefan Thumfart, Gerhard Haitchi, Pierre Schmit, Franz A. Fellner

During review the data shall only be visible for the reviewers of this paper. Afterwards this abstract will be modified and complemented and a dataset image will be uploaded.


The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.


Experimental Setup:

The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.

- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.

- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.

- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.

Monitoring condition:

All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.

- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.

- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.

Data Set Overview:

-          Three-phase Voltage

-          Three-phase Current

-          Five Vibration Signals



            The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.