Vocabulary grading is of great importance in Chinese vocabulary teaching. This paper starts with an analysis of the lexical attributes that affect lexical complexity, followed by an explanation of the extraction of le...
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Vocabulary grading is of great importance in Chinese vocabulary teaching. This paper starts with an analysis of the lexical attributes that affect lexical complexity, followed by an explanation of the extraction of lexical attribute information combined with the constructed word-formation knowledge base, the construction of mapping functions corresponding to lexical attributes, and the quantitative representation of the attributes that form the basis for vocabulary grading. Based on this, a machine learning classification algorithm is creatively applied to the Chinese vocabulary grading problem. Using the comparative analysis of vocabulary grading models based on common machine learning classification algorithms, the importance measurement analysis of Chinese vocabulary attributes based on different feature selection methods is performed, and a vocabulary grading model is constructed based on the machine learning classification algorithm and feature importance selection of different feature selection algorithms. A comparison of the experimental results demonstrated that the classification model based on the support vector machine (SVM) algorithm and top six attribute groups by the importance of feature selection received the best effect. To improve vocabulary grading, a variety of feature selection algorithms were used to fuse the importance of lexical attributes on average. Then an experiment was conducted for vocabulary grading combined with the Bagging + SVM integration algorithm and top six attribute groups by the importance of feature selection. The experimental results demonstrated that the combination scheme achieved a better effect.
Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clus...
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Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clouds, snow-covered lands, and bright buildings on remote-sensing images, they first implement an Ostu threshold to get high grey-level sub-regions, and then extract the descriptors of these sub-regions and put them into the softmax regression classifier. Regarding these methods, the authors conduct experiments using GF-1 remote-sensing images. The results demonstrate the effectiveness and excellency of their proposed method.
In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralo...
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In this article, we present a novel electronic nose fabrication process based on highly programmable anodic aluminum oxide (AAO) nanoarchitectonics and ultrasonic spray pyrolysis (USP) deposition. Featuring an ultralow manufacturing cost, the deposited material's morphology can be accurately controlled with fabricated general-purpose AAO template. Compared with nonstandard lithography-based template fabrication method, the need of complicated Bosch etching process and its associated complex process parameter tuning is eliminated. As a result, the cost-effective mass production of 3-D nanotemplate-based material and devices can be enabled. In addition, the target material's limited coverage and time efficiency issues widely existing in the previous deposition methods are well-addressed by our customized USP deposition, especially for the 3-D nanotemplate with large surface-to-volume ratio, leading to significantly improved gas-sensing performance. Moreover, the proposed fabrication recipe, together with the adopted gas recognition algorithms based on linear discriminant analysis (LDA), is validated based on the reported extensive measurement results for five gas biomarkers widely exploited for patients' exhaled gas-sensing and recognition applications. This shows great potential for the early disease diagnose of diabetes, breast cancer, acute lung injury, colon diseases, lung cancer, and so on.
The Domain Name System (DNS) is a critically fundamental element in the internet technology as it translates domain names into corresponding IP addresses. The DNS queries and responses are UDP (User Datagram Protocol)...
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ISBN:
(纸本)9781450347563
The Domain Name System (DNS) is a critically fundamental element in the internet technology as it translates domain names into corresponding IP addresses. The DNS queries and responses are UDP (User Datagram Protocol) based. DNS name servers are constantly facing threats of DNS amplification attacks. DNS amplification attack is one of the major Distributed Denial of Service (DDoS) attacks, in DNS. The DNS amplification attack victimized huge business and financial companies and organizations by giving disturbance to the customers. In this paper, a mechanism is proposed to detect such attacks coming from the compromised machines. We analysed DNS traffic packet comparatively based on the machine learning classification algorithms such as Decision Tree (TREE), Multi Layer Perceptron (MLP), Naive Bayes (NB) and Support Vector machine (SVM) to classify the DNS traffics into normal and abnormal. In this approach attribute selection algorithms such as Information Gain, Gain Ratio and Chi Square are used to achieve optimal feature subset. In the experimental result it shows that the Decision Tree achieved 99.3% accuracy. This model gives highest accuracy and performance as compared to other machinelearningalgorithms.
Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying numerous methodologies. Featur...
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Sentiment classification or sentiment analysis has been acknowledged as an open research domain. In recent years, an enormous research work is being performed in these fields by applying numerous methodologies. Feature generation and selection are consequent for text mining as the high dimensional feature set can affect the performance of sentiment analysis. This paper investigates the inability of the widely used feature selection method (IG, Chi Square, Gini Index) individually as well as their combined approach on four machine learning classification algorithm. The proposed methods are evaluated on three standard datasets viz. IMDb movie review, electronics and kitchen product review dataset. Initially, select the feature subsets from three different feature selection methods. Thereafter, statistical method UNION, INTERSECTION and revised UNION method are applied to merge these different feature subsets to obtain all top ranked including common selected features. Finally, train the classifier SMO, MNB, RF, and LR (logistic regression) with this feature vector for classification of the review data set. The performance of the algorithm is measured by evaluation methods such as precision, recall, F-measure and ROC curve. Experimental results show that the combined method achieved best accuracy of 92.31 with classifier SMO, which is encouraging and comparable to the related research.
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