As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of...
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With the rapid development of the Internet, it is increasingly necessary to mine and analyze the sentiment of comment data. The purpose of this paper is to use the deep learning feature construction method to develop ...
With the rapid development of the Internet, it is increasingly necessary to mine and analyze the sentiment of comment data. The purpose of this paper is to use the deep learning feature construction method to develop an effective mechanism to solve the learning problem under the imbalanced distribution of sentiment analysis of comment data, build an efficient model that can classify comments, and help the government correctly guide the people's sentimental inclinations. This paper adopts the method of combining keyword extraction and incremental discretization through this model, extracts keywords from the comment corpus, sets up a fuzzy matrix according to the weight of the keywords, and uses incremental K-in the discretization of the fuzzy matrix. The means algorithm extracts some additional structural features as auxiliary information to increase the prediction accuracy of the network. Through experiments, it is found that the comprehensive evaluation results obtained by this method on the comment data set are only 0.02% different from the comprehensive scores of other papers, indicating that the results obtained by the model are extremely accurate. Moreover, the model is adjusted with hyperparameters, and it is compared with the text sentiment analysis model proposed in some articles in recent years. Experiments show that the review data sentiment analysis model constructed with deep learning features has a good classification effect and a wide range of applicability.
At present, unbalanced economic development and great regional difference exists in our economy, so it is necessary to study the regional difference of economy. However, financial stock market determines the distribut...
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ISBN:
(纸本)9781510835429
At present, unbalanced economic development and great regional difference exists in our economy, so it is necessary to study the regional difference of economy. However, financial stock market determines the distribution situation of private capital in regional and inter-industry. This paper has made visualized research on the data of listed companies based on Web GL technology and attained the distribution situation of Chinese capital in different regions and different industries through visual observation and comparative analysis. The tool proposed in this paper plays a guidance role for the country to make economic strategies and decide the direction of individual investment.
Information security has become interdependent, global and critical - it has become cybersecurity. In this complex environment, legal consideration and economic incentives are as integral to ensuring the security of i...
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Information security has become interdependent, global and critical - it has become cybersecurity. In this complex environment, legal consideration and economic incentives are as integral to ensuring the security of information systems as the technological realization. In this paper, we argue that comprehensive cybersecurity requires that these three disciplines are considered together. To this end, we propose a legal analysis framework, which can can be used to study legal and economic requirements for cybersecurity in relation to technological realities. The framework yields concrete recommendations, which complex system and critical infrastructure stakeholders can utilize to improve security within their networks. The analysis framework aims to offer key stakeholders a better understanding of the legal and economic requirements for cybersecurity and provide them with recommendations that are in line with modern cybersecurity strategies, including the enhancement of cooperation and collaboration capabilities and the implementation of other state-of-the-art security mechanisms.
Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntact...
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Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
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