Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Zhu, Ying

doi:10.1155/2021/5566647

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…Due to the lack of objective and reasonable judgment of the event, negative public opinion information is usually fragmented [ 10 ]. Such negative public opinion information is easy to cause network rumors, and the wanton dissemination of such negative public opinion information is the cause of many network violent events.…”

Section: Introductionmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs of people’s ideological changes and emotional trends. Aiming at the shortcomings of sentiment dictionaries or machine learning methods in sentiment analysis tasks, this paper builds a sentiment classification model based on deep learning methods. First, the current main text preprocessing methods are introduced, and then a sentiment classification model, BCBL, is proposed, combining BERT, CNN, and Bi LSTM. Compared with traditional models, BCBL can better complete text sentiment classification tasks on standard datasets. Next, in view of the problem that BCBL does not consider the distribution of vocabulary weights, an attention mechanism is introduced to improve BCBL, and then the BCBL-Att model is proposed. Set up multiple sets of comparative experiments again and find that the classification effect and overall performance of BCBL-Att on standard datasets are better than BCBL, indicating that BCBL-Att has more advantages in text sentiment classification tasks.

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Section: Introductionmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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“…Since there are fewer pet cats than pet dogs, most cats are kept in captivity at home, and cats are not suitable for using public pet toilets, so the public pet toilets we are talking about here are mainly provided for pet dogs [22]. Public pet toilets are also composed of direct users and potential users, the direct users are dogs, and potential users are breeders, cleaners and maintainers, and bystanders, as shown in Figure 4.…”

Section: Design Methodmentioning

confidence: 99%

Green Development and Self-Service Payment System of Intelligent Pet Public Toilet Based on Artificial Intelligence

Wang

Gong

Lyu

et al. 2022

Mobile Information Systems

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Controversy about pets such as dogs is also growing, and the public health controversy is the most obvious. In order to solve the public health problem of pets outdoors, meet the needs of pet family pets for outdoor hygiene, and make up for the lack of smart pet public toilets in the market and with the development of artificial intelligence technology, the authors propose that “pet toilets” have been put into use in many cities and the emergence of this new thing is mainly aimed at the substantial increase in the number of pets such as dogs in cities, hoping to better solve the problem of pet dogs urinating in public places in the community and provide a more convenient dog service measure for urban residents. Through a questionnaire survey of 100 random volunteers, it was found that 69 people believed that pet toilets were very necessary for society; therefore, from the perspective of artificial intelligence, the development of smart pet toilets and self-service payment systems has been focused on.

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“…Whereas communication latency provides an estimate of the time from when a query is sent from the origin server until the result is returned [176,226,227,228]. For missioncritical cases [229], DL models with low computational and communication latency are more favored.…”

Section: Latencymentioning

confidence: 99%

Enabling All In-Edge Deep Learning: A Literature Review

et al. 2023

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In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to its success is the proliferation of end devices that acted as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference is the main challenge. Usually, central cloud servers are used for the computation, but it opens up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers (a mesh of computing devices near end devices). Moreover, the confluence point of DL and edge has given rise to edge intelligence (EI). International Electrotechnical Commission (IEC) defines EI as the concept where the data is acquired, stored, and processed utilizing edge computing with DL and advanced networking capabilities. Broadly, EI has six levels of categories based on where the training and inference of DL take place, e.g., cloud server, edge server and end devices. This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important such as in mission-critical applications (e.g., health care). Besides, 5G/6G networks are envisioned to use all in-edge. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented. INDEX TERMS Artificial intelligence, all in-edge, deep learning, distributed systems, decentralized systems, edge intelligence I. INTRODUCTION T HE global community is increasingly becoming a datadriven environment in which end devices are generating vast quantities of data outside of the traditional data centers. International Telecommunication Union anticipates that global internet traffic per month will reach 607 Exabytes (EB) in 2025 and 5016 EB in 2030 [1]. This enormous amount of data has a positive impact on artificial intelligence (AI) applications. In particular, deep learning (DL) rely on the availability of large quantities of data for its d...

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Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm

Cited by 10 publications

References 24 publications

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Green Development and Self-Service Payment System of Intelligent Pet Public Toilet Based on Artificial Intelligence

Enabling All In-Edge Deep Learning: A Literature Review

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