The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method base...
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The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion naive bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical naive bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.
In order to overcome the problems of traditional online classroom teaching quality evaluation methods, such as low accuracy of quality evaluation and poor effect of classroom teaching quality improvement, this paper p...
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In order to overcome the problems of traditional online classroom teaching quality evaluation methods, such as low accuracy of quality evaluation and poor effect of classroom teaching quality improvement, this paper proposes an online classroom teaching quality evaluation method based on deep data mining. Fuzzy comprehensive evaluation method is used to quantify the evaluation index of online classroom teaching quality. The evaluation matrix is constructed to calculate the weight of classroom teaching quality evaluation index. The online classroom teaching quality evaluation indicators are classified by naive bayes classification algorithm. With the help of deep data mining algorithm, this paper evaluates the post classification evaluation index, constructs the online classroom teaching quality evaluation model, and completes the online classroom teaching quality evaluation. The experimental results show that the accuracy of the proposed method is about 0.9, and it can effectively improve the quality of online classroom teaching.
The college information chatbot system is enhanced using the naive bayes classification algorithm that analyzes user's queries and messages. This system responds appropriately to the queries that are posed by the ...
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ISBN:
(纸本)9781728185019
The college information chatbot system is enhanced using the naive bayes classification algorithm that analyzes user's queries and messages. This system responds appropriately to the queries that are posed by the user using in-built Artificial Intelligence (Al) with an effective Graphical User Interface (GUI) tool. The queries posed by the user were analyzed by the chatbot using a cognitive service named Language Understanding Intelligent System (LUIS), which is designed by Microsoft. It is integrated with the Skype application which can be downloaded and installed from the play store on the user's smartphone.
作者：
Li, YelinBu, HuiLi, JiahongWu, JunjieBeihang Univ
Sch Econ & Management Dept Informat Syst Beijing 100191 Peoples R China Beihang Univ
Sch Econ & Management Dept Finance Beijing 100191 Peoples R China JD Digits
Beijing 100176 Peoples R China Beihang Univ
Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China
Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extract...
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Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of *** in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classificationalgorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and naivebayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Aim of this paper to discover the knowledge in effort to identify some of most accumulated information. The problem solving schema such as logical reasoning, Numerical ability and personality task questions are framed...
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Aim of this paper to discover the knowledge in effort to identify some of most accumulated information. The problem solving schema such as logical reasoning, Numerical ability and personality task questions are framed by the category of cognitive skills which is analyzed by predicting method in data mining techniques. In this paper focuses data mining techniques to evaluate the student skills based on problem solving resources which can be analysed by naivebayesclassification, FP tree Growth association and Kmeans (kernel) clustering techniques, experimented in rapid miner. (C) 2015 The Authors. Published by Elsevier B.V.
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