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Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak
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Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak

International journal of interactive multimedia and artificial intelligence, 2020-03, Vol.6 (1), p.132-140

COPYRIGHT 2020 IMAI Software ;LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI ;ISSN: 1989-1660 ;EISSN: 1989-1660 ;DOI: 10.9781/ijimai.2020.02.002

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Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments
Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments
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Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments

International journal of interactive multimedia and artificial intelligence, 2020-03, Vol.6 (1), p.84-90

COPYRIGHT 2020 IMAI Software ;LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI ;ISSN: 1989-1660 ;EISSN: 1989-1660 ;DOI: 10.9781/ijimai.2019.06.001

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3
Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network
Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network
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Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network

International journal of interactive multimedia and artificial intelligence, 2021-09, Vol.6 (7), p.39-48

COPYRIGHT 2021 IMAI Software ;LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI ;ISSN: 1989-1660 ;EISSN: 1989-1660 ;DOI: 10.9781/ijimai.2021.08.013

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4
Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network
Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network
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Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

Revista española de enfermedades digestivas, 2023-02, Vol.115 (2), p.75-79 [Peer Reviewed Journal]

COPYRIGHT 2023 Sociedad Espanola de Patologia Digestivas ;ISSN: 1130-0108 ;DOI: 10.17235/reed.2021.7979/2021 ;PMID: 34517717

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5
The Future Digital Work Force: Robotic Process Automation (RPA)
The Future Digital Work Force: Robotic Process Automation (RPA)
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The Future Digital Work Force: Robotic Process Automation (RPA)

Journal of Information Systems & Technology Management, 2019, Vol.16, p.1-17 [Peer Reviewed Journal]

COPYRIGHT 2019 TECSI - FEA - USP ;2019. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This work is licensed under a Creative Commons Attribution 4.0 International License. ;LICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. Por tanto, cualquier acto de reproducción, distribución, comunicación pública y/o transformación total o parcial requiere el consentimiento expreso y escrito de aquéllos. Cualquier enlace al texto completo de estos documentos deberá hacerse a través de la URL oficial de éstos en Dialnet. Más información: https://dialnet.unirioja.es/info/derechosOAI | INTELLECTUAL PROPERTY RIGHTS STATEMENT: Full text documents hosted by Dialnet are protected by copyright and/or related rights. This digital object is accessible without charge, but its use is subject to the licensing conditions set by its authors or editors. Unless expressly stated otherwise in the licensing conditions, you are free to linking, browsing, printing and making a copy for your own personal purposes. All other acts of reproduction and communication to the public are subject to the licensing conditions expressed by editors and authors and require consent from them. Any link to this document should be made using its official URL in Dialnet. More info: https://dialnet.unirioja.es/info/derechosOAI ;ISSN: 1807-1775 ;ISSN: 1809-2640 ;EISSN: 1807-1775 ;DOI: 10.4301/S1807-1775201916001

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6
Time series prediction using artificial neural networks
Time series prediction using artificial neural networks
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Time series prediction using artificial neural networks

Ciencias marinas, 2002-03, Vol.28 (1) [Peer Reviewed Journal]

ISSN: 0185-3880 ;EISSN: 2395-9053 ;DOI: 10.7773/cm.v28i1.205

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