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Democracy, military expenditure and economic growth: A heterogeneous perspective

DEFENCE AND PEACE ECONOMICS 2023年第8期34卷 1039-1070页

作者： Wang, Xinyi Hou, Na Chen, Bo Cent Univ Finance & Econ Inst Def Econ & Management Beijing Peoples R China

Countries with different democratic levels tend to show various degrees of emphasis on military expenditure and a consensus can hardly be reached on the economic effect of democracy and military expenditure. By applying a panel vector autoregressive (PVAR) approach, this article examines the potential dynamic and endogenous relationships among democracy, military expenditure and economic growth of 126 countries from 1990 to 2020. Furthermore, the k-means clustering algorithm is employed to account for the heterogeneous interaction between democracy and military expenditure in different countries. The empirical results reveal that the strong positive impact of democracy and the negative effect of military spending on economic growth exists in the full sample and the cluster of countries with low democratic levels and a high military burden. For democratic countries with low military expenditure, there is a more significant and negative impact both of military expenditure on democracy and vice versa.

关键词： Democracy military expenditure economic growth panel vector autoregression k-means clustering algorithm

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The Classification System of Literary Works Based on k-means clustering

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JOURNAL OF INTERCONNECTION NETWORkS 2022年第SUPP01期22卷 2141001-2141001页

作者： Wei, Sanqiang Hou, Hongxia Sun, Hua Li, Wei Song, Wenxia Xingtai Univ Xingtai 054001 Hebei Peoples R China

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers;comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved k-means clustering algorithm (IkCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and k-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing algorithm and k-means algorithm, accuracy comparison of ALA and IkCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

关键词： Literary works k-means clustering algorithm text classification

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Binning Approach based on Classical clustering for Type 2 Diabetes Diagnosis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第3期11卷 630-637页

作者： Hai Thanh Nguyen Nhi Yen kim Phan Huong Hoang Luong Nga Hong Cao Hiep Xuan Huynh Can Tho Univ Coll Informat & Commun Technol Can Tho Vietnam FPT Univ Dept Informat Technol Can Tho Vietnam Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan

In recent years, numerous studies have been focusing on metagenomic data to improve the ability of human disease prediction. Although we face the complexity of disease, some proposed frameworks reveal promising performances in using metagenomic data to predict disease. Type 2 diabetes (T2D) diagnosis by metagenomic data is one of the challenging tasks compared to other diseases. The prediction performances for T2D usually reveal poor results which are around 65% in accuracy in state-of-the-art. In this study, we propose a method combining k-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction. We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by k-means allows to increase the performance in prediction accuracy reaching approximately or more than 70%.

关键词： Unsupervised binning k-means clustering algorithm metagenomics metagenome-based disease prediction Type 2 diabetes diagnosis

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Research on Communication Site Planning and Regional clustering Based on Optimal Multivariate Optimization Model

Research on Communication Site Planning and Regional Cluster...

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作者： Yanliang Chen Haoyan Pei Yan Pei Tongyan Li College of Communacation Engineering Chengdu University of Information Technology

In recent years,in the context of the continuous development of 5G networks,the construction of mobile communication mobile base stations is extremely important for the overall network environment construction in my *** sites in the objective function are planned multiple times through the immune genetic algorithm,and finally the optimal site is re-planned and the weak coverage points are clustered.

关键词： Optimal multivariate model simulated annealing algorithm immune genetic algorithm k-means clustering algorithm DBSCAN density clustering algorithm class algorithm

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Short-Term Load Forecasting for Campus Building with Small-Scale Loads by Types Using Artificial Neural Network

Short-Term Load Forecasting for Campus Building with Small-S...

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IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： Baek, Sung-Jun Yoon, Sung-Guk Soongsil Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781538682326

Nowadays, the large portion of total energy is consumed by buildings, so it is essential to use the energy efficiency in buildings. With the deployment of a smart meter in the building, a small-scale load data are now available such as lighting, general and the other loads. We first analyze the small-scale loads through the fixed k-means clustering algorithm. Based on the analysis of load characteristics, we propose five artificial neural network-based forecasting models for campus buildings. The five models use total load, temperature and small-scale loads as input data with different input combinations. The case studies show that using small-scale loads improves the forecasting performance.

关键词： artificial neural network campus building small-scale load smart meter k-means clustering algorithm short-term load forecasting

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Detecting Anomaly Using the Scene Modeling Based on Time Delayed Statistical Data

Detecting Anomaly Using the Scene Modeling Based on Time Del...

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IEEE International Conference on Information and Automation 2015

作者： Chen, Lixin Guo, Huiwen Wang, Min Chen, Yen-Lun Wu, Xinyu Feng, Wei Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen Peoples R China Chinese Univ Hong Kong Shatin Shatin Hong Kong Peoples R China

ISBN: (纸本)9781467391047

We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera view and acquire the crowd flow model by the k-means clustering algorithm. Secondly, we analyze the crowd flow model based on the time delayed statistical data between two camera views. And then we acquire the relative location among the entrances and exits in the different regions. Thirdly, we analyze the crowd transferring probabilistic model on the global scene based on the log-likelihood function and Dirichlet distribution to detect the crowd anomaly. We set up the empirical threshold value of probability P-e. If the probability of detected model is less than P-e, the detected model is marked as the crowd anomaly. Our approach is evaluated on the simulated data set and the real data set in far-field scenes. Experimental results show the anomaly detection is precise.

关键词： Crowd anomaly detection non-overlapping view k-means clustering algorithm log-likelihood function Dirichlet distribution

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A Method of Automatically Identifying Learners' help-seeking Behavior Classifications in Online Learning Environment 2

A Method of Automatically Identifying Learners' help-seeking...

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2nd International Conference on Data Science and Business Analytics (ICDSBA)

作者： Ye, Junmin Zhao, Lixian Luo, Daxiong Wang, Zhifeng Chen, Shu CCNU Sch Comp Wuhan Hubei Peoples R China CCNU Sch Educ Informat Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538684313

In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use k-means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the Naive Bayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.

关键词： learner's help-seeking behavior classification k-means clustering algorithm t-distributed Stochastic Neighbor Embedding algorithm Naive Bayesian classification algorithm

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A Hybrid Approach to Plan Itinerary for Tourists 5

A Hybrid Approach to Plan Itinerary for Tourists

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5th International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Awal, Md. Abdul Rabbi, Jakaria Hossain, Sk. Imran Hashem, M. M. A. Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781509012695

With the rapid development of information, communication and transportation systems, the number of tourists is increasing all over the world. Most of the tourists are facing problems when they visit unfamiliar places. That's why it is often difficult for an individual tourist to make an optimal sightseeing itinerary plan during a tight schedule. To help tourists in this situation, a hybrid approach has been proposed in this paper to recommend an optimal itinerary plan. In this approach, k-means clustering algorithm has been used to cluster various tourist spots based on the geographic location and preferences of the tourists. Then the individual cluster is treated as a travelling salesman problem. To find the optimal route in each cluster greedy and 2-opt algorithms are applied. To switch from one cluster to the next cluster, a route is established from the last spot of one cluster to the centroid of the next cluster. Thus, all the tourist spots will be visited efficiently.

关键词： Itinerary Planning k-means clustering algorithm Travelling Salesman Problem Greedy algorithm 2-opt algorithm

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Unsupervised Satellite Image Segmentation using a Bivariate Beta type-II Mixture Model

Unsupervised Satellite Image Segmentation using a Bivariate ...

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IEEE 9th International Conference on Computational Cybernetics (ICCC)

作者： Ben Arab, Taher Zribi, Mourad Masmoudi, Afif Lab Informat Signal & Image Cote Opale LISIC EA 4 ULCO50 Rue Ferdinand Buisson BP 719 F-62228 Calais France Univ Sfax Fac Sci Lab Probabilities Statist Sfax Tunisia

ISBN: (纸本)9781479900633;9781479900602

This paper presents an unsupervised satellite color image segmentation approach based on Bivariate Beta type-II. Such a method could be considered as original since it uses a. k-means clustering algorithm in order to initialize the image class number. Moreover, it exploits a Bivariate Beta type-II for statistical distributions applying it for each class. Satellite image exploitation requires the use of different approaches, especially those based on the unsupervised statistical segmentation principle. Such approaches necessitate the definition of several parameters such as image class number, class variables estimation and mixture distributions. The use of statistical image attributes has allowed us to get convincing results, provided that we ensure under the condition of having an initialization step with appropriate statistical distributions. Bivariate Beta type-II associated with a. k-means clustering algorithm and Expectation-Maximization (EM) algorithm could be adapted to such a problem. For each image class, Bivariate Beta typeII attributes a specific distribution type according to different parameters. Different adapted algorithms (namely. k-means clustering algorithm, EM algorithm and Bivariate Beta type-II algorithm) are then applied to the satellite image segmentation problem. The efficiency of those combined algorithms is validated with the Mean Squared Errors (MSE), Signal to Noise Ratio (SNR) and Maximum Distance (MD).

关键词： Bivariate Beta type-II Expectation Maximization algorithm k-means clustering algorithm Mean Squared Errors Unsupervised color image segmentation

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Two texture segmentation of document image using wavelet packet analysis

Two texture segmentation of document image using wavelet pac...

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9th International Conference on Advanced Communication Technology (ICACT 2007)

作者： Lee, Geum-Boon Odoyo, Wilfred O. Lee, Jae-Hoon Chung, Il-Yong Cho, Beom-Joon Chosun Univ Dept Comp Engn Kwangju South Korea

ISBN: (纸本)9788955191318

In this paper, we present a text segmentation method using wavelet packet analysis and k-means clustering algorithm. This approach assumes that the text and non-text regions are considered as two different texture regions. The text segmentation is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multiscale features, we compute the local energy and intensify the features before adapting the k-means clustering algorithm based on the unsupervised learning rule. The results show that our text segmentation method is effective for document images scanned from newspapers and journals.

关键词： wavelet packet analysis document image segmentation k-means clustering algorithm energy estimation

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