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Deep Learning-Based Intelligent Robot in Sentencing

This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in tria...

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Autor principal: Chen, Xuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9341297/
https://www.ncbi.nlm.nih.gov/pubmed/35923731
http://dx.doi.org/10.3389/fpsyg.2022.901796
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author Chen, Xuan
author_facet Chen, Xuan
author_sort Chen, Xuan
collection PubMed
description This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist in the sentencing of cases. Finally, an example is adopted to illustrate the feasibility of the intelligent robot under deep learning in legal sentencing. The results show that the general final trial periods for cases of traffic accidents, copyright information, trademark infringement, copyright protection, and theft are 1,049, 796, 663, 847, and 201 days, respectively; while the final trial period under artificial intelligence evaluation based on the restricted Boltzmann deep learning model is 458, 387, 376, 438, and 247 days, respectively. The accuracy of trials is above 92%, showing a high application value. It can be observed that expect theft cases, the final trial period for others cases has been effectively reduced. The intelligent robot assistance under the restricted Boltzmann deep learning model can shorten the trial period of cases. The deep learning intelligent robot has a certain auxiliary role in legal sentencing, and this outcome provides a theoretical basis for the research of artificial intelligence technology in legal sentencing.
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spelling pubmed-93412972022-08-02 Deep Learning-Based Intelligent Robot in Sentencing Chen, Xuan Front Psychol Psychology This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist in the sentencing of cases. Finally, an example is adopted to illustrate the feasibility of the intelligent robot under deep learning in legal sentencing. The results show that the general final trial periods for cases of traffic accidents, copyright information, trademark infringement, copyright protection, and theft are 1,049, 796, 663, 847, and 201 days, respectively; while the final trial period under artificial intelligence evaluation based on the restricted Boltzmann deep learning model is 458, 387, 376, 438, and 247 days, respectively. The accuracy of trials is above 92%, showing a high application value. It can be observed that expect theft cases, the final trial period for others cases has been effectively reduced. The intelligent robot assistance under the restricted Boltzmann deep learning model can shorten the trial period of cases. The deep learning intelligent robot has a certain auxiliary role in legal sentencing, and this outcome provides a theoretical basis for the research of artificial intelligence technology in legal sentencing. Frontiers Media S.A. 2022-07-18 /pmc/articles/PMC9341297/ /pubmed/35923731 http://dx.doi.org/10.3389/fpsyg.2022.901796 Text en Copyright © 2022 Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Chen, Xuan
Deep Learning-Based Intelligent Robot in Sentencing
title Deep Learning-Based Intelligent Robot in Sentencing
title_full Deep Learning-Based Intelligent Robot in Sentencing
title_fullStr Deep Learning-Based Intelligent Robot in Sentencing
title_full_unstemmed Deep Learning-Based Intelligent Robot in Sentencing
title_short Deep Learning-Based Intelligent Robot in Sentencing
title_sort deep learning-based intelligent robot in sentencing
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9341297/
https://www.ncbi.nlm.nih.gov/pubmed/35923731
http://dx.doi.org/10.3389/fpsyg.2022.901796
work_keys_str_mv AT chenxuan deeplearningbasedintelligentrobotinsentencing