Security topics related microblogs search based on deep convolutional neural networks

Zhou, Nan; Du, Junping; Xue, Zhe; Liang, Meiyu; Xu, Yao; Cui, Wanqiu

doi:10.1016/j.neucom.2018.09.105

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Histogram equalization is one of the very important algorithms in image enhancement. It uses basic knowledge of probability theory to perform gray point operations for the purpose of histogram transformation [6][7][8]. e essence of the image histogram equalization algorithm is to selectively enhance the low-frequency information and suppress the high-frequency information of the image.…”

Section: Night Image Improvement Algorithm Based On Histogrammentioning

confidence: 99%

“…In contrast, the boundaries between image regions that normally occupy fewer pixels contain important structural information, which may cause the loss of image details. In addition, various types of image noise are inevitably present in the image, and then the image noise will be amplified accordingly [7,9]. erefore, the traditional histogram equalization algorithm has the disadvantages of image detail information loss and noise amplification.…”

Section: Night Image Improvement Algorithm Based On Histogrammentioning

confidence: 99%

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Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

Fan

2021

Scientific Programming

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The rapid development of the tourism industry and the Internet era has led to an increasingly severe problem of travel information overload, and travel recommendation methods are essential to solving the information overload problem. Traditional recommendation algorithms only target common travel scenarios during the daytime, combining the ratings and necessary attributes between users and items to calculate similarity for a recommendation. Still, the research on night travel recommendations is one of the few scenarios that needs to be explored urgently. This paper, based on histogram equalization, achieves better experimental results on image enhancement, combines convolutional neural network technology with night image and text comment feature extraction technology, and evaluates the resulting error with mean absolute error (MAE). This paper presents the first night travel recommendation system. It compares it with the traditional collaborative filtering method, and the model proposed in this paper can effectively reduce the prediction error.

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Section: Night Image Improvement Algorithm Based On Histogrammentioning

confidence: 99%

Section: Night Image Improvement Algorithm Based On Histogrammentioning

confidence: 99%

Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

Fan

2021

Scientific Programming

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“…Almost all of them are employed in some capacity regularly. The past content consumption is used to make product recommendations based on interest models [ 7 ]. The system only generates a small number of recommendations, a significant shortcoming.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Recommendation Method for Tourist Attractions Based on Deep Learning

Yang

2022

Computational Intelligence and Neuroscience

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Tourists are the people who can be seen all over the world. Therefore, this has increased the demand for product supply in tourist locations. Technological development would be the only solution to solve those issues related to the demand and supply of the products. Furthermore, a tourist needs enough information about the country he wishes to travel. Some specific data required include hotels, destinations, malls, and tourist places, and they are needed before they land on the tourist country. The data collection can be achieved by applying trending technologies such as deep learning algorithms and some intelligent systems. Furthermore, the tourist may collect information about the locations through feasible devices such as laptops and mobile phones. Among the varying devices and technologies, the most preferred and convenient device should be carried wherever they travel with ease. For example, cellular phones may be considered the easiest modem to carry and use with this specification. In this perspective, the lightweight deep learning model will make significant technology access to resources. Typically, deep learning models are designed with the prospect of extracting the features, potentially to create easy tool access within the mobile accessing service. Visual Bayesian Personalized Ranking (VBPR) Algorithm using DL is implemented for initiating the recommendation system for tourist attractions in any given location. The proposed model was compared with various existing algorithms, and it was found that the proposed system had delivered 98.56% of accurate recommendations for tourist travelers.

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A semi-supervised semantic-enhanced framework for scientific literature retrieval

Xue

et al. 2021

Neurocomputing

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Security topics related microblogs search based on deep convolutional neural networks

Cited by 6 publications

References 27 publications

Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

An Intelligent Recommendation Method for Tourist Attractions Based on Deep Learning

A semi-supervised semantic-enhanced framework for scientific literature retrieval

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