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Enhancing topic clustering for Arabic security news based on k-means and topic modelling

IET NETWORKS 2021年第6期10卷 278-294页

作者： Alharbi, Adel R. Hijji, Mohammad Aljaedi, Amer Univ Tabuk Coll Comp & Informat Technol Tabuk Saudi Arabia

The internet has become one of the main sources of news spread as it unleashed the information dissemination space, where the news websites express opinions on entities while also reporting on recent or unusual security risks. Recently, many research studies have focused on sentimental reflection on the views and impressions of people utilising natural language processing and analytical linguistics. Therefore, we have collected corpus from popular Arabic websites that publish articles related to recent security issues, and we provide light weight preprocessing techniques where data is term matrix is transformed. We also present an intensive lexical-driven data analysis with visualised data views, as our topic modelling technique can effectively extract significant topics from all the collected text from different websites. Our experiments validate the k-means clustering algorithm with and without the latent Dirichlet allocation topic modelling method, and we adopted various validation techniques to measure the topic clustering internally and externally. As shown in the experiments' results, our proposed combined method has a high round index rate of 87.2%, with a large number of topics and clusters.

关键词： people utilising natural language processing analytical linguistics popular Arabic websites news spread different websites data analysis clusters recent security risks validation techniques recent security issues computational linguistics collected text Other topics in statistics Natural language processing Arabic security news pattern clustering latent Dirichlet allocation topic modelling method visualised data views Web sites Internet topic modelling technique impressions unusual security risks sentimental reflection clustering algorithm term matrix topic clustering news websites natural language processing Information networks text analysis information dissemination space significant topics k-means

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Research on the Machine Learning Platform Design Scheme Based on the Spark Platform 1st

Research on the Machine Learning Platform Design Scheme Base...

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World Conference on Intelligent and 3-D Technologies (WCI3DT)

作者： Zhong, Jianxu Yu, Shaofeng Xi, Lingzhi Ma, Yining CSG Power Generat Co Informat Commun Branch Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789811971860;9789811971846;9789811971839

In the continuous innovation of computer technology, artificial intelligence technology has played a positive role in industrial production and daily life. Especially with the advent of the era of big data, cloud computing and Internet of Things technology has attracted people's attention. Using Spark to build a machine learning platform has become the main direction for guests and scholars to explore and analyze. In the study of practical construction, the machine learning platform with Spark as the core should be built with a clear grasp of distributed cluster technology and cloud computing, so as to flexibly use existing computing resources and improve the processing efficiency of practical data. At the same time, experimental comparative analysis should be carried out under the conditions of one-click environment and Hadoop, which proves that the hybrid architecture machine learning platform constructed in this paper has strong application performance.

关键词： Spark platform Machine learning Linear regression Platform measurement clustering algorithm

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Planning for Network Expansion Based on Prim algorithm and Reinforcement Learning

Planning for Network Expansion Based on Prim Algorithm and R...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Dong, Fude Li, Zilv Xu, Yuantu Zhu, Deqiang Huang, Rongjie Zou, Haobin Wu, Zelin Wang, Xinghua Guangdong Power Grid Co Ltd Qingyuan Power Supply Bur Qingyuan Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China

ISBN: (纸本)9798350339345

Existing methods for expanding distribution network grids lack flexibility in handling multiple scenarios, particularly in the expansion planning of photovoltaic(PV)-enabled distribution networks where the problem of adding load points is mainly addressed based on empirical knowledge without considering the economic and reliability aspects throughout the entire planning process. To address this issue, this paper proposes a planning method that combines Prim algorithm and reinforcement learning (RL). Firstly, historical data of PV and load characteristics are extracted, and multiple typical PV and corresponding load scenarios are generated using clustering algorithms as planning scenarios. Secondly, the Prim algorithm is introduced to improve the action rules and environment of RL, and the mathematical model of planning is transformed into a corresponding reward function. By using RL, the optimal planning path can be found, which enhances the overall operational efficiency of the network and ensures the power supply of the new load points, providing a reference for future operation planning scenarios. Finally, calculations and data analysis based on IEEE standard cases verify the scientific and efficient nature of the proposed method.

关键词： distribution network planning Prim algorithm reinforcement learning clustering algorithm

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A Flexible Load Management Method for Digital Power Grid Based on Cluster Analysis 2

A Flexible Load Management Method for Digital Power Grid Bas...

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2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

作者： Zhou, Ding State Grid Chongqing Yongchuan Power Supply Co Chongqing Peoples R China

ISBN: (纸本)9798350303896

Flexible load, as an important schedulable resource, can greatly improve the balance of power network flow through refined control. Under the background of the current digital power grid, the Traditional mathematics mathematical model fitting method is not enough to achieve the analysis of nonlinear high-dimensional load data. In this regard, this article proposes a load forecasting model that combines an improved clustering algorithm with a short-term and short-term memory network. Firstly, for flexible loads, data processing is implemented using a multi core fuzzy C clustering algorithm, dynamically adjusting the training parameters of the model, and improving the feature description ability. Furthermore, the optimized long and short term memory network is used to train and learn the preprocessed sample dataset, capturing the bidirectional dependency information of the input data, effectively improving the predictive model's ability to fit the characteristics of load sample data. The experimental results prove that the proposed flexible load forecasting model achieves precise fitting of the implementation situation and can achieve situational awareness and load control for the power grid.

关键词： Digital power grid flexible load load forecasting sum function clustering algorithm long-term and short-term memory network

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Exploring success factors of tourism performing arts by analyses of online reviews

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JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY 2023年第1期14卷 37-52页

作者： Cui, Yuan Kim, Seungwoon Feng, Shi Jeonbuk Natl Univ Dept Business Adm Jeonju South Korea Northeastern Univ Dept Comp Sci Shenyang Peoples R China

PurposeThis study aims to explore the success factors of tourism performing arts (TPA) programs by analyzing a large data set of online reviews. Design/methodology/approachA total of 195,230 reviews from *** were collected and preprocessed. A deep learning method was leveraged to estimate the similarity between words. Then, regression analysis was conducted to determine success factors. FindingsThis study extracted four positive and two negative factors affecting tourist satisfaction with tourism performance arts. The results demonstrate that the tourists paid the most attention to the traditional Chinese cultural aspects, audiovisual effects and the actors' performing enthusiasm. Research limitations/implicationsDespite this study's large data set, the focused was only on Chinese reviews. It would be useful and interesting to compare the success factors of tourism performance arts programs offered in different countries. Practical implicationsThe study findings can contribute to the development of TPA programs to attract tourists to travel destinations. Originality/valueThis study demonstrates that analyzing online reviews of TPA through text mining technology is an effective method of understanding tourist satisfaction.

关键词： Tourism performing arts Success factor Tourist satisfaction Text mining Tourism online review clustering algorithm ??? ???? ???? ????? ??? ?????? ????

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An improved K-prototype-based cluster algorithm for mixed educational data mining

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2022年第5期15卷 448-456页

作者： Wang, Yuan Yang, Liping Wu, Jun Chen, Shaomiao Beijing Univ Chem Technol Coll Humanities & Law Beijing 100029 Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

Educational data mining is one of the important ways to improve the quality of education. clustering is a classic data mining method. It is often applied to single-attribute data. Educational data which contains multiple types of attributes. This brings great challenges to the dissimilarity measurement of clustering algorithms. In this paper, we first collect a large amount of student education data with 18 different types of attributes. Then, based on the K-prototype algorithm framework, we improve the dissimilarity measurement method so that it can be applied to the mixed attribute characteristics of education data. Finally, a Kruskal-Wallis test is used to determine the significance of clustering properties. Experimental results show that the proposed method can obtain higher quality clustering than the state-of-the-art clustering methods.

关键词： clustering algorithm educational data mining EDM mixed attribute learning analysis campus big data

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Analysing the variation of metadiscourse verb patterns in English academic papers from within and between disciplines

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 107-118页

作者： Li, Yinbo Hunan City Univ Int Educ Coll Yiyang 413000 Peoples R China

English is a universal language and is widely used. In order to deepen the cognition of language, this paper proposes to analyse the variation of metadiscourse verb patterns in English academic papers from within and between disciplines. By comparing the metadiscourse chunks used in various disciplines, it is concluded that there are obvious differences in the verb types of the subject papers. Analysing various identification algorithms of verb types, it is obtained that the combination of the word movement distance (WMD) model and the R&L density peak clustering algorithm is the best. Using the R&L density peak clustering, identification parameters are easy to determine, and when combined with the WMD model to calculate the similarity of words, it improve the accuracy of verb pattern clustering. By comparison, it is proved that the accuracy of the algorithm combining the WMD model and R&L density peak clustering reaches 30.06%, and the effect of identifying verb pattern variation is the best.

关键词： metadiscourse clustering algorithm WMD model English academic papers

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RETRACTED: Audio-Visual Automatic Speech Recognition Towards Education for Disabilities (Retracted Article)

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JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2023年第9期53卷 3581-3594页

作者： Debnath, Saswati Roy, Pinki Namasudra, Suyel Crespo, Ruben Gonzalez Alliance Univ Dept Comp Sci & Engn Bangalore Karnataka India Natl Inst Technol Dept Comp Sci & Engn Silchar Assam India Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Univ Int La Rioja Logrono Spain

Education is a fundamental right that enriches everyone's life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition.

关键词： AV-ASR LBP-TOP GLCM MFCC clustering algorithm Supervised learning

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Weld Defect Location Method of U-Shaped Crane Boom Based on Helical Guided Waves 10th

Weld Defect Location Method of U-Shaped Crane Boom Based on ...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Lu, Zhaojing Liu, Zenghua Jiang, Wenshuo Chen, Honglei Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9783031073229;9783031073212

The boom is the key load-bearing component of the crane, and its health seriously threatens the service performance of the crane. In order to ensure the safe production of cranes, nondestructive testing (NDT) and structural health monitoring (SHM) of boom become more and more urgent and important. In this paper, the intelligent defect location algorithm based on helical guided waves is applied to the weld defect detection of U-shaped boom. Intelligent defect location algorithm is an imaging algorithm that combines ellipse imaging principle, evolutionary algorithm and K-means clustering algorithm. This paper verifies the effectiveness of this algorithm for weld defect location of U-shaped boom through experimental research. Firstly, the propagation regularity of helical guided waves in the U-shaped boom structure is studied. In addition, the elliptical imaging algorithm is used to image and analyze the weld defects in the U-shaped boom, and the location of the defects is preliminarily determined. The defect location analysis of weld defects is carried out by using the intelligent defect location algorithm. By comparing the imaging results of the two methods, it is found that the intelligent defect location algorithm has higher resolution and can effectively improve the defect location accuracy. This algorithm provides a tool for health monitoring of special-shaped structures based on guided waves.

关键词： Structural health monitoring U-shaped crane boom Helical guided waves Evolutionary algorithm clustering algorithm

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Intelligent Pricing and Replenishment Strategy for Vegetables Based on ARIMA Prediction and BP Neural Networks

Intelligent Pricing and Replenishment Strategy for Vegetable...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Yang, Quyixiao Liu, Enyu Qiao, Yue Dalian University of Technology Dalian China

ISBN: (纸本)9798350309614

With the improvement of people's living standard, people pay more and more attention to nutritional balance, which brings huge commercial profits, because vegetable goods can only be sold on the same day, taking into account the special characteristics of vegetable goods, the use of past sales data for data mining, to help store managers to make the best decision from the single product, category, replenishment volume, pricing. Firstly, using the idea of clustering, the single items are clustered, and Spearman correlation analysis model is established for correlation analysis, then, the weekly sales volume, sales unit price, wholesale price are counted, and the cost-plus pricing relationship of each category is back-calculated daily through the cost-plus pricing formula, and the total amount of replenishment of the vegetable category for a week and pricing strategy are predicted using the BP neural network prediction model forecasting, and the total amount of replenishment of the vegetable category for a week is predicted. Finally, the ARIMA time series prediction model is built to solve the pricing reference strategy for a single item on a single day and determine the replenishment quantity of a single item on a single day by considering the unit price of the vegetable category, the wholesale unit price, and the net profit from the sale of the vegetable category. The proposed model has excellent forecasting ability and the results are evaluated more objectively. © 2023 IEEE.

关键词： BP Neural Network Prediction Models clustering algorithm Intelligent Decision Making for Goods Polynomial Regression Spearman Correlation

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