An Unsupervised Sentiment Classification Method Based on Multi-Level Fuzzy Computing and Multi-Criteria Fusion

Wang, Bingkun; He, Wei; Yang, Zhen; Xiong, Shufeng

doi:10.1109/access.2020.3014849

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…The fuzzy inference mechanism is used to obtain the fuzzy inference result. The fuzzy inference mechanism transforms the membership degrees of clear inputs into rule results, and then the rule results are combined by the fuzzy logic operations to form the final fuzzy inference result [23]. The common fuzzy inference mechanisms include the Mamdani minimum and Larsen product [24].…”

Section: Fuzzy Inference Modelmentioning

confidence: 99%

A focused crawler based on semantic disambiguation vector space model

Liu

et al. 2022

Complex Intell. Syst.

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The focused crawler grabs continuously web pages related to the given topic according to priorities of unvisited hyperlinks. In many previous studies, the focused crawlers predict priorities of unvisited hyperlinks based on the text similarity models. However, the representation terms of the web page ignore the phenomenon of polysemy, and the topic similarity of the text cannot combine the cosine similarity and the semantic similarity effectively. To address these problems, this paper proposes a focused crawler based on semantic disambiguation vector space model (SDVSM). The SDVSM method combines the semantic disambiguation graph (SDG) and the semantic vector space model (SVSM). The SDG is used to remove the ambiguation terms irrelevant to the given topic from representation terms of retrieved web pages. The SVSM is used to calculate the topic similarity of the text by constructing text and topic semantic vectors based on TF × IDF weights of terms and semantic similarities between terms. The experiment results indicate that the SDVSM method can improve the performance of the focused crawler by comparing different evaluation indicators for four focused crawlers. In conclusion, the proposed method can make the focused crawler grab the higher quality and more quantity web pages related to the given topic from the Internet.

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Section: Fuzzy Inference Modelmentioning

confidence: 99%

A focused crawler based on semantic disambiguation vector space model

Liu

et al. 2022

Complex Intell. Syst.

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“…Human pose detection, a current research hotspot in computer vision, has a wide range of applications in life, such as in video surveillance to ensure security in the public domain and in human-machine interaction to enhance the fluency between humans and machines [1] . Human pose detection is an algorithmic process that uses convolutional neural networks to detect key nodes of the human body in pictures or videos and then connects these key points.…”

Section: Introductionmentioning

confidence: 99%

A data fusion-based method for detecting difficult yoga postures

Wang¹

2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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The current traditional human pose detection methods mainly acquire the pose data collected by sensors and realize the pose discrimination by edge computing decision unit, which leads to poor detection effect due to the lack of fusion of sensor data. In this regard, a high difficulty yoga body pose detection method based on data fusion is proposed. Complementary filtering, Kalman filtering and adaptive Kalman filtering methods are used to fuse sensor data for pose prediction, and a skeleton extraction model is constructed to build a multilayer perceptron network architecture to achieve the detection of difficult yoga asana poses. In the experiments, the proposed human posture detection method is validated. The analysis of the experimental results shows that the proposed method has a high comprehensive evaluation index and excellent detection performance when used for the detection of difficult yoga asana postures.

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“…Liu and Tsai conducted deep study and analysis on intelligent identification and instruction of English vague text through collateral projection and area extension [6]. Wang et al proposed an unsupervised emotion classification means on the strength of multistage vague computation and multinorm integration [7]. Sunny et al created a set of fuzzy computing software quality models [8].…”

Section: Introductionmentioning

confidence: 99%

The Intervention of Music Education on Students’ Mental Health Based on Fuzzy Computing

Wang

2022

Mathematical Problems in Engineering

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Music is an important way for people to express and communicate their thoughts and feelings. It not only has the functions of cultivating sentiment, developing intelligence, and promoting personality development but also has an energetic function on the spiritual fitness of pupils. As an important part of quality education in schools, music education has become a pioneer in the implementation of psychological intervention for students. Music education can cultivate sentiment and lively and optimistic mood by emotional images conveyed through various music and has a special role that cannot be replaced by other disciplines. However, the current music education in schools only focuses on the dissemination of music knowledge and there is no research on which type of music can effectively interfere with students’ mental health. In order to be able to choose music that is effective for students’ mental health intervention in music education, this paper will study the intervention research of music education on students’ mental health based on fuzzy computing. This paper extracts the musical features such as average pitch, average pitch intensity, melody direction, pitch stability value, rhythm intensity, and beat, uses fuzzy computing to classify music, determines which types of music can improve students’ mental health, and uses experiments to verify the validity of this research. The consequences of the research show that choosing effective music to intervene in students’ mental health can greatly improve students’ mental health problems. The scores of students’ psychological status after the intervention are 0.73 times of those before the intervention. It demonstrates the validity of the study.

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An Unsupervised Sentiment Classification Method Based on Multi-Level Fuzzy Computing and Multi-Criteria Fusion

Cited by 8 publications

References 35 publications

A focused crawler based on semantic disambiguation vector space model

A focused crawler based on semantic disambiguation vector space model

A data fusion-based method for detecting difficult yoga postures

The Intervention of Music Education on Students’ Mental Health Based on Fuzzy Computing

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