Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks
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 pat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Signals |
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| Online Access: | https://www.mdpi.com/2624-6120/4/2/16 |
| _version_ | 1797592646863028224 |
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| author | Aníbal Chaves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_facet | Aníbal Chaves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_sort | Aníbal Chaves |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:56:03Z |
| format | Article |
| id | doaj.art-7f68f720b76d424897cddd7c082fca6d |
| institution | Directory Open Access Journal |
| issn | 2624-6120 |
| language | English |
| last_indexed | 2024-03-11T01:56:03Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj.art-7f68f720b76d424897cddd7c082fca6d2023-11-18T12:36:41ZengMDPI AGSignals2624-61202023-04-014229731410.3390/signals4020016Graphical User Interface for the Development of Probabilistic Convolutional Neural NetworksAníbal Chaves0Fábio Mendonça1Sheikh Shanawaz Mostafa2Fernando Morgado-Dias3University of Madeira, 9000-082 Funchal, PortugalUniversity of Madeira, 9000-082 Funchal, PortugalInteractive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, PortugalUniversity of Madeira, 9000-082 Funchal, PortugalThrough 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.https://www.mdpi.com/2624-6120/4/2/16artificial intelligencegraphical interfaceprobabilistic convolutional neural networkno-code development platform |
| spellingShingle | Aníbal Chaves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks Signals artificial intelligence graphical interface probabilistic convolutional neural network no-code development platform |
| title | Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks |
| title_full | Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks |
| title_fullStr | Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks |
| title_full_unstemmed | Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks |
| title_short | Graphical User Interface for the Development of Probabilistic Convolutional Neural Networks |
| title_sort | graphical user interface for the development of probabilistic convolutional neural networks |
| topic | artificial intelligence graphical interface probabilistic convolutional neural network no-code development platform |
| url | https://www.mdpi.com/2624-6120/4/2/16 |
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