@inproceedings{WansingBanosPomaresetal.2015,
  author    = {Wansing, Christian and Banos, Oresti and Pomares, Hector and Gl{\"o}sek{\"o}tter, Peter and Rojas Ruiz, Ignacio},
  title     = {Development of a platform for the exchange of biodatasets with integrated opportunities for artificial intelligence using MatLab},
  editor    = {Junta de Andalucia Project P12-TIC-2082., Conference Paper},
  doi       = {10.1109/ICoCS.2015.7483278},
  pages     = {1 -- 6},
  year      = {2015},
  abstract  = {This paper deals with the issue of automating the process of machine learning and analyzing bio-datasets. For this a user-friendly website has been developed for the interaction with the researchers. On this website it is possible to upload datasets and to share them, if desired, with other scientists. The uploaded data can also be analyzed by various methods and functions. The signals inside these datasets can also be visualized. Furthermore several algorithms have been implemented to create machine learning models with the uploaded data. Based on these generated models new data can be classified or calculated. For all these applications the simplest possible handling was implemented to make the website available to all interested researchers.},
  language  = {de}
}
@inproceedings{RoerupGloesekoetterPomaresetal.2021,
  author    = {R{\"o}rup, Tim and Gl{\"o}sek{\"o}tter, Peter and Pomares, Hector and Ruiz, Ignacio},
  title     = {Deep Learning Based Neural Network for Six-Class-Classification of Alzheimer's Disease Stages Based on MRI Images},
  series = {IWANN2021},
  booktitle = {IWANN2021},
  publisher = {Springer International Publishing},
  doi       = {10.1007/978-3-030-85030-2_1},
  pages     = {3 -- 14},
  year      = {2021},
  abstract  = {State of the art classifiers split Alzheimer's disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer's disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.},
  language  = {de}
}