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In this paper, we propose a novel motion estimation algorithm for true motion estimation. After obtaining an initial motion vector map through hierarchical block matching algorithm with distance constraints, we classify the motion vectors based on residual energy map. With block and edge information, the motion vector map can get updated iteratively until it reaches the predefined condition. The experiment...
Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few years. In this paper, we have presented a Gait based human identification system using skeleton data acquired by using Microsoft Kinect sensor. The sensor acts as a digital eye which takes...
Inflammation of the conjunctiva and pain and discomfort in the inner surface of the eyelids is referred to as Conjunctivitis. It causes severe pain, burning sensation or in extreme cases blindness of the eye. Normally conjunctivitis is detected by eye specialist doctors and their limited number makes it difficult for everyone to reach them and get themselves diagnosed. This paper describes an automatic...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
With the rapid development of network information technology, the text is as a basic information carrier and begins to present exponential growth. The existing text classification methods haven't got information from the vast amounts of information resources timely and accurately. In order to solve the problem, the paper puts forward a new method about text categorization. It is a KNN algorithm based...
Pattern classification or clustering plays important role in a wide variety of applications in different areas like psychology and other social sciences, biology and medical sciences, pattern recognition and data mining. A lot of algorithms for supervised or unsupervised classification have been developed so far in order to achieve high classification accuracy with lower computational cost. However,...
Noise reduction is an active research area in hyperspectral image processing due to its importance in improving the quality of image for the subsequent applications. To improve the accuracy and efficiency of object recognition and classification using hyperspectral imagery (HSI), we propose a novel smoothing algorithm by coupling a Laplacian-based reaction term to a classical anisotropic diffusion...
Stack Overflow (SO) is a question and answers (Q&A) web platform on software development that is gaining in popularity. With increasing popularity often comes a very unwelcome side effect: A decrease in the average quality of a post. To keep Q&A websites like SO useful it is vital that this side effect is countered. Previous research proved to be reasonably successful in using properties...
Shape of breast Contours are prominent signs to determine malignancy in mammograms. A new algorithm for feature extraction is proposed based on polynomial regression on the signatures of benign and malignant contours. Two features mean absolute error and correlation coefficient were extracted for 57 mammograms of which 32 images were malignant contours and 25 images were benign contours. Three different...
This paper describes our efforts to apply various advanced supervised machine learning and natural language processing techniques, including Binomial Logistic Regression, Support Vector Machines, Neural Networks, Ensemble Techniques, and Latent Dirichlet Allocation (LDA), to the problem of detecting fraud in financial reporting documents available from the United States’ Security and Exchange Commission...
Data inaccuracy is an important problem in wireless sensor networks, since the accuracy is affected by harsh environments and malicious nodes. The reason for this data inaccuracy is the improper identification of outliers. To detect exact outliers in the wireless sensor networks, we propose the relative correlation based clustering (RCC) technique with high data accuracy and low computational overhead...
Research on feature selection techniques for identifying informative genes from high dimensional microarray datasets has received considerable attention. Numerous researchers have proposed various optimized solutions to reduce noises, redundancy in dataset and to enhance the accuracy and generalization of the classification model by applying many computational tools. High-dimensional microarray gene...
For classification of High Dimensional data, feature selection is the most important step for obtaining optimal result with respect to processing power required and time taken. Feature selection is a method by which the most relevant feature is selected from a set of features containing redundant and irrelevant features thereby reducing the load on the classification algorithm. This paper proposes...
This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated...
Gene selection is one of important research issues in analysis of gene expression data classification. Current methods try to reduce genes by means of statistical calculations and have used semantic similarity under gene ontology. In this article a technique has been presented based on which in addition to considering biological relation among genes, redundant genes by means of hierarchical clustering...
A New algorithm for classifying Direct Sequence Code Division Multiple Access (DS-CDMA) signals in additive white Gaussian noise (AGWN) is proposed based on the average likelihood (AL) function. The AL is express in terms of the code length and the number of active users. These parameters constitute the hypothesis under test. While the AL function is expressed in an analytical form, the implementation...
One of the most interesting topics in social network research is opinion formation. In this paper we have introduced a new dependent multi-dimensional opinion formation method. This method models agents with several dependent opinions so that modifying the agent's opinion about one issue can affect its opinion about another issue. Agents share their opinion with agents which have trusted them. A directed...
This paper presents a method for automatic classification of environmental sounds into three major categories, namely bird sounds, human speech and noise. Three various algorithms are used to classify the sounds individually and their efficiencies are compared. The classification algorithms are based on cyclostationary features, Mel Frequency Cepstral Coefficients (MFCC) and our proposed algorithm...
The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) [1] predicted nearly two...
The classification with instances which can be tagged with any of the 2L possible subsets from the predefined L labels is called multi-label classification. Multi-label classification is commonly applied in domains, such as multimedia, text, web and biological data analysis. The main challenge lying in multi-label classification is the dilemma of optimising label correlations over exponentially large...
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