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Sentiment Analysis is the process of figuring out the emotions from a piece of writing that whether it is positive, negative or neutral and is used to tell the speaker's attitude. The trend, today, is to consider the opinions of a variety of individuals around the globe before purchasing an item using micro-blogging data. Customers tend to go over a lot of reviews about a particular item before buying...
Cluster Analysis methods are very important, popular data summarization techniques applied in diverse environments. These techniques retrieve the hidden patterns in large datasets in the form of characterized patterns which can be interpreted further in different contexts. Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis...
Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace...
This paper presents a new data-driven classification pipeline for discriminating two groups of individuals based on the medical images of their brain. The algorithm combines deformation-based morphometry and penalised linear discriminant analysis with resampling. The method is based on sparse representation of the original brain images using deformation logarithms reflecting the differences in the...
The Zernike moments can achieve high accuracy and strong robustness for the classification and retrieval of images, but involve huge amount of computation caused by its complex definition. It has limited its exploitation in online real-time applications or big data processing. So researches on how to improve the computation speed of Zernike moments are carried out. One of the existing high-accuracy...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
Feature selection algorithm has a great influence on the accuracy of text categorization. The traditional information gain (IG) feature selection algorithm usually selects the features that rarely appear in the specified categories, but frequently appear in other categories. To overcome this drawback, on the basis of in-depth analysis of the related algorithms, an improved IG feature selection method...
On the problem of covert channel detection, the traditional detection algorithms exist specific covert channel blind area, or it is useful for some kind of covert channel detection but ignore other covert channels. In order to solve this problem, in this paper proposes network covert channel analysis method based on the density multilevel two segment clustering. Firstly, the problem of covert channel...
This paper introduces a novel sequential approach to user movement analysis and tracking for indoor positioning systems. The algorithm utilizes stored reference and measured received signal strength indication (RSSI) data to determine the most likely movement paths represented as sequences of rectangular zones. It is demonstrated that the application of the proposed approach results in an improvement...
Machine Leaning (ML) plays an important role in the electronic data management. It is always costly and difficult to manage the data manually without adopting ML or with ML using metadata. Many ML algorithms have been proposed to solve different data management issues, but the prediction of the confidential data and non- confidential data in a data file is still a challenging research gap. A file...
Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Increasing access to huge datasets and corresponding demands to analyze these data...
During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance...
The traffic identification is a preliminary and essential step for stable network service provision and efficient network resource management. While a number of identification methods have been introduced in literature, the payload signature-based identification method shows the highest performance in terms of accuracy, completeness, and practicality. However, the payload signature-based method's...
In data mining, a well known problem of “Curse of Dimensionality” occurs due to presence of large number of dimensions in a dataset. This problem leads to reduced accuracy of machine learning classifiers because of presence of many insignificant and irrelevant dimensions or features in the dataset. Data mining applications such as bioinformatics, risk management, forensics etc., generally involves...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
Text classification is the foundation and core of text mining. Naive Bayes is an effective method for text classification. This paper improves the accuracy of Naive Bayes classification using improved information gain, one of methods of feature extraction, by reducing the impact of low-frequency words. In this paper, we use a widely corpus of NLTK. According to the test results, The accuracy of the...
Three RFID reader based network deployment algorithms (grid-covering, diagonal and mixed) were evaluated in this paper. Experimental results show that the grid-covering method can be used to minimize hardware costs, but it leads to many indeterminate positions. The diagonal method can be used to solve the indeterminate problem, however increases the number of readers, especially in a large tracking...
The determination of Region-of-Interest can be used as a means of improving the performance of image retrieval, when used in image annotation as a step in the indexing of images collection. It also has the potential to support efficient video compression for real-time applications. However, existing Region-of-Interest detection methods are mostly unsuitable for managing large number of images and...
For most of data sets, there exist some redundant, irrelevant and even noise features. Usually, there are plenty of features in medical data sets and the correlation among features is strong. So, feature selection of medical data sets gets great concern in recent years. RELIEFF is one of the effective feature selection algorithms, but cannot remove redundant features. RS is a mathematical approach...
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