Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks

April 20, 2023

Through the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have the inherent capabilities of humans, such as pattern recognition in images and signals. However, conventional methods are based on deterministic models, which cannot express the epistemic uncertainty of their predictions. The alternative consists of probabilistic models, although these are considerably more difficult to develop. To address the problems related to the development of probabilistic networks and the choice of network architecture, this article proposes the development of an application that allows the user to choose the desired architecture with the trained model for the given data. This application, named “Graphical User Interface for Probabilistic Neural Networks”, allows the user to develop or to use a standard convolutional neural network for the provided data, with networks already adapted to implement a probabilistic model. Contrary to the existing models for generic use, which are deterministic and already pre-trained on databases to be used in transfer learning, the approach followed in this work creates the network layer by layer, with training performed on the provided data, originating a specific model for the data in question.

Authors in the community:

• 

  S

  Sheikh Shanawaz Mostafa

  ist427013

  Affiliations and sources

  In: cienciavitae

  ID: 3556300

AO - Author's Original

Signals

297

314

4

2

10.3390/signals4020016

electrical-engineering-electronic-engineering-information-engineering - Electrical engineering, electronic engineering, information engineering

eng - English

https://doi.org/10.3390/signals4020016