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Journal articles on the topic 'Model based classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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2

Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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3

Geman, D., and B. Jedynak. "Model-based classification trees." IEEE Transactions on Information Theory 47, no. 3 (2001): 1075–82. http://dx.doi.org/10.1109/18.915664.

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4

Tran, Chi-Kien. "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model." International Journal of Machine Learning and Computing 9, no. 3 (2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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5

Zong, Xinlu, Chunzhi Wang, and Hui Xu. "Density-based Adaptive Wavelet Kernel SVM Model for P2P Traffic Classification." International Journal of Future Generation Communication and Networking 6, no. 6 (2013): 25–36. http://dx.doi.org/10.14257/ijfgcn.2013.6.6.04.

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6

Casselgren, Johan, Mikael Sjödahl, and James P. LeBlanc. "Model-based winter road classification." International Journal of Vehicle Systems Modelling and Testing 7, no. 3 (2012): 268. http://dx.doi.org/10.1504/ijvsmt.2012.048941.

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7

Wasi, Shaukat, Syed Imran Jami, and Zubair Ahmed Shaikh. "Context-based email classification model." Expert Systems 33, no. 2 (2015): 129–44. http://dx.doi.org/10.1111/exsy.12136.

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8

Rice, Feng, Tristrom Cooke, and Danny Gibbins. "Model based ISAR ship classification." Digital Signal Processing 16, no. 5 (2006): 628–37. http://dx.doi.org/10.1016/j.dsp.2006.01.007.

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9

Maier, U., and A. Löffler. "Course Development Based on a Model for the Classification of Learning Progress." International Journal of Social Science and Humanity 5, no. 9 (2015): 735–42. http://dx.doi.org/10.7763/ijssh.2015.v5.549.

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10

McNicholas, Paul D. "Model-based classification using latent Gaussian mixture models." Journal of Statistical Planning and Inference 140, no. 5 (2010): 1175–81. http://dx.doi.org/10.1016/j.jspi.2009.11.006.

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11

张, 俊达. "Volume Data Classification Visualization Based on Probabilistic Classification Model." Computer Science and Application 09, no. 11 (2019): 1986–92. http://dx.doi.org/10.12677/csa.2019.911223.

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12

ZHANG, Zhen, and Xue-gang HU. "Classification model based on mutual information." Journal of Computer Applications 31, no. 6 (2012): 1678–80. http://dx.doi.org/10.3724/sp.j.1087.2011.01678.

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13

Hung-Chih Chiang, R. L. Moses, and L. C. Potter. "Model-based classification of radar images." IEEE Transactions on Information Theory 46, no. 5 (2000): 1842–54. http://dx.doi.org/10.1109/18.857795.

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14

Kotsiantis, S. B. "Rotation-based model trees for classification." International Journal of Data Analysis Techniques and Strategies 2, no. 1 (2010): 22. http://dx.doi.org/10.1504/ijdats.2010.030009.

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15

Newman, T. "Model-Based Classification of Quadric Surfaces." Computer Vision and Image Understanding 58, no. 2 (1993): 235–49. http://dx.doi.org/10.1006/cviu.1993.1042.

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16

Newman, T. S., P. J. Flynn, and A. K. Jain. "Model-Based Classification of Quadric Surfaces." CVGIP: Image Understanding 58, no. 2 (1993): 235–49. http://dx.doi.org/10.1006/ciun.1993.1040.

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17

Xu, Binghui, Sizhe Ding, and Yan Zhang. "Image classification model based on GAT." Journal of Physics: Conference Series 1570 (June 2020): 012082. http://dx.doi.org/10.1088/1742-6596/1570/1/012082.

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18

Öner, Yüksel, Fikriye Kabakçı, Burçin Öner, and Mehmet Gürcan. "A REMARK ON L2 DISTANCE FUNCTION AND NON-IDENTIFIABILITY PROBLEM OF FINITE MIXTURE DISTRIBUTION MODELS IN MODEL-BASED CLASSIFICATION." E-journal of New World Sciences Academy 14, no. 4 (2019): 139–46. http://dx.doi.org/10.12739/nwsa.2019.14.4.2a0176.

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19

Choi, Gihyeon, Youngjin Jang, Harksoo Kim, and Kwanwoo Kim. "Deep Learning-based Text Classification Model for Poisonous Clauses Classification." Journal of KIISE 47, no. 11 (2020): 1054–60. http://dx.doi.org/10.5626/jok.2020.47.11.1054.

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20

Murugan, S., and Dr M. Jeyakarthic. "Optimal Deep Neural Network based Classification Model for Intrusion Detection in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1374–87. http://dx.doi.org/10.5373/jardcs/v11sp10/20192983.

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21

Zhaoye, Xing, Liu Xiaoqun, and Sun Peijie. "Hybrid Chinese text classification model based on pretraining model." Journal of Physics: Conference Series 1961, no. 1 (2021): 012002. http://dx.doi.org/10.1088/1742-6596/1961/1/012002.

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22

Aich, Satyabrata, Sabyasachi Chakraborty, and Hee-Cheol Kim. "Convolutional neural network-based model for web-based text classification." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5785. http://dx.doi.org/10.11591/ijece.v9i6.pp5785-5191.

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<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different to

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23

Xing, Ling, Wei Zhao, and Rong Fu. "A Cloud Support Vector Machine Model Based on Image Semantics." Applied Mechanics and Materials 411-414 (September 2013): 1170–73. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1170.

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In allusion to randomness and fuzziness of digital image semantic, we propose a new semantic representation of digital image based on cloud model and construct a semantic vector space. In this space, semantic classifications of digital images are completed by calculating the semantic class certainty degree (SCCD). In addition, we propose cloud support vector machine based on image semantics (CSVM-IS) model. Experimental results show that CSVM-IS can accomplish target classification and has good classification accuracy.

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24

Choi, Sun-Wook, and Chong Ho Lee. "Hypergraph model based Scene Image Classification Method." Journal of Korean Institute of Intelligent Systems 24, no. 2 (2014): 166–72. http://dx.doi.org/10.5391/jkiis.2014.24.2.166.

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25

HU, Ting, Yong WANG, and Xiao-ling TAO. "Network traffic classification based on hybrid model." Journal of Computer Applications 30, no. 10 (2010): 2653–55. http://dx.doi.org/10.3724/sp.j.1087.2010.02653.

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26

ZHANG, Qi-Fei, Wei LIU, Bao-Lin SUN, Chao GUI, and Bing YAN. "Collision Classification Model-Based Collision Resolution Algorithm." Journal of Software 21, no. 3 (2010): 548–63. http://dx.doi.org/10.3724/sp.j.1001.2010.03452.

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27

LI, Wen-Bo, Le SUN, and Da-Kun ZHANG. "Text Classification Based on Labeled-LDA Model." Chinese Journal of Computers 31, no. 4 (2009): 620–27. http://dx.doi.org/10.3724/sp.j.1016.2008.00620.

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28

Kang, Nan Nan, Xiao Fang Wang, and Rong Rong Zhang. "Image Classification Based on Color Topic Model." Applied Mechanics and Materials 556-562 (May 2014): 4770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4770.

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This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evalua

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29

Mokhtar, Umi Asma’, and Zawiyah M. Yusof. "Function-Based Classification: Model Development and Validation." Open Journal of Social Sciences 03, no. 03 (2015): 211–14. http://dx.doi.org/10.4236/jss.2015.33031.

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30

Liang, Jiguang, Ping Liu, Jianlong Tan, and Shuo Bai. "Sentiment Classification Based on AS-LDA Model." Procedia Computer Science 31 (2014): 511–16. http://dx.doi.org/10.1016/j.procs.2014.05.296.

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31

Long, Zhili, Ronghua He, Xiaobing Zhang, and Yuxiang He. "Ultrasound Environment Classification Based on Fuzzy Model." IOP Conference Series: Materials Science and Engineering 428 (October 1, 2018): 012046. http://dx.doi.org/10.1088/1757-899x/428/1/012046.

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32

Qiao, Yaqin, Xuezhen Dai, and Zaibin Chang. "Classification Model based on Choquet Integral Discriminant." Journal of Physics: Conference Series 1693 (December 2020): 012078. http://dx.doi.org/10.1088/1742-6596/1693/1/012078.

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33

Qiao, Yulong, and Lixiang Weng. "Hidden Markov Model Based Dynamic Texture Classification." IEEE Signal Processing Letters 22, no. 4 (2015): 509–12. http://dx.doi.org/10.1109/lsp.2014.2362613.

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34

Pan, Fei, Yingchun Yuan, and Yufei Song. "Students’ Classification Model Based on Stacking Algorithm." Journal of Physics: Conference Series 1486 (April 2020): 032020. http://dx.doi.org/10.1088/1742-6596/1486/3/032020.

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35

Nasr, Abdurrahman A. "ABCD: agent based model for document classification." International Journal of Data Mining, Modelling and Management 10, no. 3 (2018): 250. http://dx.doi.org/10.1504/ijdmmm.2018.093878.

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36

Nasr, Abdurrahman A. "ABCD: agent based model for document classification." International Journal of Data Mining, Modelling and Management 10, no. 3 (2018): 250. http://dx.doi.org/10.1504/ijdmmm.2018.10015045.

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37

KHOSHGOFTAAR, TAGHI M., LOFTON A. BULLARD, and KEHAN GAO. "A RULE-BASED SOFTWARE QUALITY CLASSIFICATION MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (2008): 247–59. http://dx.doi.org/10.1142/s0218539308003064.

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A rule-based classification model is presented to identify high-risk software modules. It utilizes the power of rough set theory to reduce the number of attributes, and the equal frequency binning algorithm to partition the values of the attributes. As a result, a set of conjuncted Boolean predicates are formed. The model is inherently influenced by the practical needs of the system being modeled, thus allowing the analyst to determine which rules are to be used for classifying the fault-prone and not fault-prone modules. The proposed model also enables the analyst to control the number of rul

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38

Parsons, Jeffrey. "An Information Model Based on Classification Theory." Management Science 42, no. 10 (1996): 1437–53. http://dx.doi.org/10.1287/mnsc.42.10.1437.

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39

Zhong, Ziliang, Muhang Zheng, Huafeng Mai, Jianan Zhao, and Xinyi Liu. "Cancer image classification based on DenseNet model." Journal of Physics: Conference Series 1651 (November 2020): 012143. http://dx.doi.org/10.1088/1742-6596/1651/1/012143.

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40

Chiou, C. P. "Model-based ultrasonic flaw classification and sizing." NDT & E International 25, no. 1 (1992): 42. http://dx.doi.org/10.1016/0963-8695(92)90060-t.

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41

Schmerr, L. W., C. P. Chiou, and S. M. Nugen. "Model-based ultrasonic flaw classification and sizing." NDT & E International 25, no. 1 (1992): 43. http://dx.doi.org/10.1016/0963-8695(92)90072-o.

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42

Alewijnse, Sander P. A., Kevin Buchin, Maike Buchin, Stef Sijben, and Michel A. Westenberg. "Model-Based Segmentation and Classification of Trajectories." Algorithmica 80, no. 8 (2017): 2422–52. http://dx.doi.org/10.1007/s00453-017-0329-x.

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43

Li, Dong, and Yunhua Zhang. "Adaptive Model-Based Classification of PolSAR Data." IEEE Transactions on Geoscience and Remote Sensing 56, no. 12 (2018): 6940–55. http://dx.doi.org/10.1109/tgrs.2018.2845944.

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44

Lee, Yerin, Soyoung Lim, and Il-Youp Kwak. "CNN-Based Acoustic Scene Classification System." Electronics 10, no. 4 (2021): 371. http://dx.doi.org/10.3390/electronics10040371.

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Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcom

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45

Vrbik, Irene, and Paul D. McNicholas. "Parsimonious skew mixture models for model-based clustering and classification." Computational Statistics & Data Analysis 71 (March 2014): 196–210. http://dx.doi.org/10.1016/j.csda.2013.07.008.

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46

Zhang, Jiahuan, Hongjun Song, and Binbin Zhou. "SAR Target Classification Based on Deep Forest Model." Remote Sensing 12, no. 1 (2020): 128. http://dx.doi.org/10.3390/rs12010128.

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Synthetic aperture radar (SAR) has become one of the most important means of information acquisition in today’s society and shows great potential in many fields. Target identification and classification of SAR images are also the focus of research. With the vigorous development of deep learning, many researchers apply this method to SAR target classification to obtain a more automatic process and more accurate results. In this paper, a novel deep forest model constructed by multi-grained cascade forest (gcForest), which is different from the traditional neural network (NN) model, is employed t

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47

HUI, Bei, and Yue WU. "Anti-spam model based on semi-Naive Bayesian classification model." Journal of Computer Applications 29, no. 3 (2009): 903–4. http://dx.doi.org/10.3724/sp.j.1087.2009.00903.

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48

Karungaru, Stephen, Lyu Dongyang, and Kenji Terada. "Vehicle Detection and Type Classification Based on CNN-SVM." International Journal of Machine Learning and Computing 11, no. 4 (2021): 304–10. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1052.

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In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves

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49

Xing, Ling, Wei Zhao, and Rong Fu. "A Semantic Classification Method of Digital Image Based on Cloud Model." Applied Mechanics and Materials 321-324 (June 2013): 1011–16. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1011.

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In view of randomness and fuzziness of digital image semantics, a new semantic representation of digital image based on cloud model is proposed and a semantic vector space is constructed. In the space, semantic classifications of digital images are completed by calculating the semantic class certainty degree (SCCD). In addition, we propose cloud support vector machine based on image semantics (CSVM-IS) model, which can effectively utilize the knowledge of SCCD. This method can effectively classify the multi-semantic information and eliminate the rejection of the classification samples. Experim

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50

Zhou, Ji Lai, Ming Quan Zhou, and Guo Hua Geng. "3D Model Retrieval Based on Distance Classification Histogram." Applied Mechanics and Materials 733 (February 2015): 931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.733.931.

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This paper presents a new algorithm to retrieve 3D model on distance classification histogram. First, we select the certain number of random points on the model surface and compute the distance between two random points. Secondly, we sort the distance into two types which is based on the different geometry properties of these distance and construct the distance classification histogram. Finally, we measure the similarity of 3D models by comparing distance classification histogram. The experimental results on PSB show that our method has a good performance in precision and computational complic

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