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automatic classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

Computers, Materials & Continua 2022年第1期70卷 1799-1814页

作者： Haithem Ben Chikha Ahmad Almadhor Jouf University College of Computer and Information SciencesComputer Engineering and Networks DepartmentSakaka72388Kingdom of Saudi Arabia

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single *** this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m *** purpose consists of classifying pairs of users modulations from superimposed *** achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB *** use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations *** simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian *** also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)*** results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 *** addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

关键词： automatic classification MIMO two-way cognitive relay network Nakagami-m channels superimposed modulations 5G

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automatic classification of Swedish Metadata Using Dewey Decimal classification:A Comparison of Approaches

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Journal of Data and Information Science 2020年第1期5卷 18-38页

作者： Koraljka Golub Johan Hagelback Anders Ardo Department of Cultural Sciences Faculty of Arts and HumanitiesLinnaeus UniversityVaxjoSweden Department of Computer Science and Media Technology Faculty of TechnologyLinnaeus UniversityKalmarSweden Department of Electrical and Information Technology Lund UniversityLundSweden

Purpose:With more and more digital collections of various information resources becoming available,also increasing is the challenge of assigning subject index terms and classes from quality knowledge organization *** the ultimate purpose is to understand the value of automatically produced Dewey Decimal classification(DDC)classes for Swedish digital collections,the paper aims to evaluate the performance of six machine learning algorithms as well as a string-matching algorithm based on characteristics of ***/methodology/approach:State-of-the-art machine learning algorithms require at least 1,000 training examples per *** complete data set at the time of research involved 143,838 records which had to be reduced to top three hierarchical levels of DDC in order to provide sufficient training data(totaling 802 classes in the training and testing sample,out of 14,413 classes at all levels).Findings:Evaluation shows that Support Vector Machine with linear kernel outperforms other machine learning algorithms as well as the string-matching algorithm on average;the string-matching algorithm outperforms machine learning for specific classes when characteristics of DDC are most suitable for the *** embeddings combined with different types of neural networks(simple linear network,standard neural network,1 D convolutional neural network,and recurrent neural network)produced worse results than Support Vector Machine,but reach close results,with the benefit of a smaller representation *** of features in machine learning shows that using keywords or combining titles and keywords gives better results than using only titles as *** only marginally improves the *** stop-words reduced accuracy in most cases,while removing less frequent words increased it *** greatest impact is produced by the number of training examples:81.90%accuracy on the training set is achieved when at least 1,000 records per class are available

关键词： LIBRIS Dewey Decimal classification automatic classification Machine learning Support Vector Machine Multinomial Naive Bayes Simple linear network Standard neural network 1D convolutional neural network Recurrent neural network Word embeddings String matching

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automatic classification of Chestnuts Based on Decision Fusion

Automatic Classification of Chestnuts Based on Decision Fusi...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yanjun Jiang School of Computer Science Sichuan University

Image acquisition system of chestnuts has been designed in this study. After the preprocessing on the images, including image de-noising, image enhancement, image segmentation, edge extraction and edge linking, the parameters of the shape features of chestnuts such as the areas, the perimeter, the circularity, the eccentricity and the degree of circularity have been extracted. This study proposes a decision fusion strategy using L-M neural network for automatic classification of chestnuts. The simulation results present that the classification accuracy has been improved using the method based on decision fusion compared with the traditional methods.

关键词： decision fusion image processing L-M neural network automatic classification chestnut

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automatic TEXT classification BASED ON ROUGH SET AND IMPROVED QUICK-REDUCE ALGORITHM

AUTOMATIC TEXT CLASSIFICATION BASED ON ROUGH SET AND IMPROVE...

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第七届国际信号处理会议

作者： JIANG Minghu1, DENG Beixing2, SHENG Xiaowei1, TANG Xiaofang3, RUAN Qiuqi3, YUAN Baozong3 1Lab of Computational Linguistics, Dept. of Chinese Language, Tsinghua University, Beijing, 100084, China 2 Department of Electronics Engineering, Tsinghua University, Beijing 100084, China 3Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China

This paper proposes a fast dimensionality reduction algorithm for automatic text classifications (TC), which introduces Rough Set theory that can greatly reduce the document vector dimensions by th

关键词： automatic classification rough set decision table reduction algorithm

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Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1140-1152页

作者： Zhenhao Xu Wen Ma Peng Lin Yilei Hua Geotechnical and Structural Engineering Research Center Shandong UniversityJinan250061China School of Qilu Transportation Shandong UniversityJinan250061China Institute of Robotics and Intelligent Systems Wuhan University of Science and TechnologyWuhan430000China

An intelligent lithology identification method is proposed based on deep learning of the rock microscopic *** on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were *** shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be *** the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative *** expanded a self-built dataset by using transfer learning and data ***,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model *** results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing ***,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology *** proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.

关键词： Deep learning Rock microscopic images automatic classification Lithology identification

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automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

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Journal of Geographical Sciences 2017年第11期27卷 1413-1427页

作者： WANG Jiao CHENG Weiming ZHOU Chenghu ZHENG Xinqi School of Information Engineering China University of Geosciences Beijing 100083 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing （ISO） cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients （K） as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.

关键词： automatic classification geomorphometric parameters ISO cluster lunar iandforms DEM

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automatically identifying the motivations of Facebook mention to scholarly outputs based on Light GBM

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Data Science and Informetrics 2022年第4期2卷 120-134页

作者： Houqiang Yu Wei Zhang Yue Wang Yinghua Xie School of Information Management Sun Yat-sen UniversityGuangzhouChina School of Economics&Management Nanjing University of Science and TechnologyNanjingChina

[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual *** particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was *** comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature *** classification precision with only the post text features was *** combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’*** combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was *** conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.

关键词： Facebook mention Facebook mention motivation automatic classification Light GBM

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UNSUPERVISED INCREMENTAL ACQUISITION OF A THEMATIC CORPUS FROM THE WEB

UNSUPERVISED INCREMENTAL ACQUISITION OF A THEMATIC CORPUS FR...

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2003 International Conference on Natural Language Processing and Knowledge Engineering

作者： Florence Duclaye Francois Yvon Olivier Collin France Télécom R&D 2 avenue P.Marzin22307 Lannion CedexFrance

＜正＞This paper presents a nearly unsupervised learning methodology for automatically acquiring a thematic corpus from the *** on a bootstrapping mechanism,our system starts with one single linguistic expression of a given target semantic *** then samples the Web so as to progressively accumulate a corpus of potential examples of the same *** steps alternate with filtering steps,making it possible to keep the corpus thematically *** corpus is finally analysed to search for potential paraphrases of the initial expression of the semantic relationship. These paraphrases will eventually be used to improve our question-answering *** paper focuses on the learning aspect of the system and reports experimental results regarding the effectiveness of our filtering strategy.

关键词： Paraphrases Synonyms Machine learning Web automatic classification EM

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