Keel-Data set repository | Time Series Analysis | Features Selection | Bioinformatics | Expert Systems in Medicine
Areas and Software developed by the group and coworkers.
Keel-Data set repository. Time Series
Software for Time Series Analysis
Nonlinear time series models with regime
switching, an R
package which implements nonlinear autoregressive (AR) time series models.
For univariate series, a non-parametric approach is available through
additive nonlinear AR. Parametric modeling and testing for regime switching
dynamics is available when the transition is either direct (TAR: threshold AR)
or smooth (STAR: smooth transition AR, LSTAR).
Software for Feature Selection
Orange SNNS module is an interface module to use artificial neural networks from SNNS software as learning algoritms in Orange. OrangeSNNS.py allows using SNNS to create, train and simulate neural networks as learners inside Orange.
Orange is a data mining software that is specially good for researching and teaching. It is developed in Python and C++ combining the best from both: interpretability and quick use from Python and efficiency from C++.
Feature Selection for Time Series
Software for bioinformatics
A tool devoted to the classification of Strachan Transition
States in samples of satellite DNA. It performs several computations,
including nucleotide diversity and transitions/transversions
It shows results graphically in html format. http://satdna.sourceforge.net/
Software for Expert Systems in Medicine
Thyroid diagnosis (NoseTiroides)
Expert system for Thyroideal diagnosis, which can emulate the diagnosis process of and expert cytologist for cervix diseases, assists user in searching the best diagnoses, interpret the result of these set, and help user to archive most suitable diagnosis.
An image based expert system for cervical lesion diagnosis
Expert system for cervical lesion diagnosis, which can emulate the diagnosis process of and expert cytologist for cervix diseases, assists user in searching the best diagnoses, interpret the result of these set, and help user to archive most suitable diagnosis.Cytopathologist are specialist and have their own private procedural knowledge acquired in years of direct experience, this procedural knowledge has been studied and then developed on web platform.
The system supports the expert in cervical cell diagnosis mainly on the base of the Bethesda2001 system.
The application is also hypothesis-based. Certain cell characteristics are presented with the help of prototypical images.The user expert can then check for these characteristics in the examined microscope slide.A fuzzylogic-driven inference kernel computes a list of possible diagnoses, sorted by their probability, and chooses characteristics next to check for,according to this list of hypotheses. The list is continuously updated,until a certain probability is reached which converts the most probable hypothesis into the diagnosis,orthe system has no other cell characteristics to check for,and the process is stopped without solution.
Survival in laryngeal squamous cell carcicomas (SEAPSCEL)
This is an expert system that helps us estimate the probability of survival of patients with squamous cell carcinoma of the larynx.
It is a system that shows the most important prognostic factors in cancer of the larynx represented by images.
Images will be marked according to the characteristics of the patient to achieve a result: the probability of survival at 5 years.
Cervix Image Processing 2 (CIP2)
This software was developed to handle high-resolution microscopic
images. Microscopic slides are managed in a list with thumbnail images
and commentaries. The software can import data in the Aperio svs file
format and makes them available through a slide viewer which uses the
Google Maps API. Furthermore, regions can be defined and extracted to
jpg files, where they can be processed by image filters. A bunch of
standard image filters is implemented, using ITK and OpenCV.
Furthermore, the segmentation results can be reintegrated, and can be
displayed in the image viewer as overlays onto the original image data.
VO2Max Estimation Based on ANN
Fitness can be measured by the volume of oxygen you can consume while
exercising at your maximum capacity. VO2
Max. is the maximum amount of
oxygen in millilitres, one can use in one minute per kilogram of body
Max is a new model proposed to estimate VO2
This model is based on powerful artificial neural network and
outperforms previous proposals