Deep Learning Based Smart Survilance Robot

Ganesan, Vithya; Das, Smritilekha; Kundu, Tamal; Naren, J.; Bushra, S. Nikkath

doi:10.4108/eai.16-5-2020.2304191

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…With the continuous advancement of artificial intelligence technology, more and more deep learning models are deployed on agents to solve problems in different fields, such as visual recognition (Ruiz-del-Solar et al, 2018;Gu et al, 2021), behavior supervision (Quan et al, 2018;Ganesan et al, 2021;Jia et al, 2021), and artificial assistance (Xiao et al, 2020), etc. Deep learning is defined as a scientific field involving complex functions (for example, non-linear dynamics) to train a multilayer neural network, embedding the data from the original, high-dimensional, multimodal state to the understandable state of the agent system (Goodfellow et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

et al. 2021

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With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

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