PecidRL: Petition expectation correction and identification based on deep reinforcement learning

Liu, Ying; Fang, Wensi; Sun, Hang; Liu, Xiangyu; Du, Wei; Liu, Yijun; Li, Qianqian

doi:10.1016/j.ipm.2023.103285

Cited by 4 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paper ID Deliberative [42,48,55,61,64,65,69,71,79,81,86] Democratic [41,44,50,54,65,81,85] Civic Engagement [41,44,47,[49][50][51][53][54][55]58,60,63,65,66,[68][69][70]74,76,[78][79][80][81][82][83]85,86]…”

Section: Effectsmentioning

confidence: 99%

“…In some studies, these evaluations are about "Openness and Transparency" that the implementation of a new technology [77] or a new platform [60] offers while others document problems that are caused by the lack of Openness and Transparency in existing platforms [79]. [41,60,65,76] As for "quantity", the literature almost never mentions a particularly high increase in participation. Divergent outcomes follow the implementation of digital platforms for policymaking.…”

Section: Eparticipation Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Artificial Intelligence in eParticipation: Mapping Current Research

Vasilakopoulos,

Tavantzis,

Promikyridis

et al. 2024

Future Internet

View full text Add to dashboard Cite

Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.

show abstract

Section: Effectsmentioning

confidence: 99%

Section: Eparticipation Evaluationmentioning

confidence: 99%

The Use of Artificial Intelligence in eParticipation: Mapping Current Research

Vasilakopoulos,

Tavantzis,

Promikyridis

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…DL is a tool to sharpen the action produced by the agent, with random probability conditions [33]. In previous research, in the context of DRL, many used traditional supervised learning models such as SVM or random forest [34][35][36], thus the use of DL in this study is still relevant in the context of the updated method being implemented. The DL implementation in this study uses an artificial neural network (ANN), which is defined by five layers, consisting of one layer as the input layer, three layers as hidden layers, and one layer as the output layer.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL)

2023

View full text Add to dashboard Cite

The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system.

show abstract