If a woman is pregnant, it is important for both her and her doctor/clinician to be aware if there are problems with the developing fetus. There are currently ways to discover problems using both noninvasive and invas...
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ISBN:
(纸本)9781467325882
If a woman is pregnant, it is important for both her and her doctor/clinician to be aware if there are problems with the developing fetus. There are currently ways to discover problems using both noninvasive and invasive techniques. The University of Arkansas for Medical Sciences (UAMS) has recently developed a noninvasive system called the Squid Array for Reproductive Assessment (SARA) that can be used to gather fetal heartbeat data. This raw data, however, must then be analyzed by a human being to determine if there is a problem with a given fetus. In this paper, we propose a method to enable a computer to determine if a fetus is in a healthy or unhealthy state by the employment of a technique that will allow for rapid analysis using datamining.
Network security has become an important issue due to the evolution of internet. It brings people not only together but also provides huge potential threats. Intrusion detection technique is considered as the immense ...
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ISBN:
(纸本)9781467356930
Network security has become an important issue due to the evolution of internet. It brings people not only together but also provides huge potential threats. Intrusion detection technique is considered as the immense method to deploy networks security behind firewalls. An intrusion is defined as a violation of security policy of the system. Intrusion detection systems are developed to detect those violations. Due to the effective data analysis method, datamining is introduced into IDS. This paper brings an idea of applying datamining algorithms to intrusion detection database. Performance of various rule and function based classifiers like Part, Ridor, NNge, DTNB, JRip, Conjunctive Rule, One R, Zero R, Decision Table, RBF, Multi Layer Perception and SMO algorithms are compared and result shows that SMOciassification algorithm performs well in terms of accuracy, specificity and sensitivity. The performance of the model is measured using 10-fold cross validation.
The proceedings contain 69 papers. The topics discussed include: an ontology-based topical crawling algorithm for accessing deep web content;image mining: a new approach for datamining based on texture;decision makin...
ISBN:
(纸本)9780769548722
The proceedings contain 69 papers. The topics discussed include: an ontology-based topical crawling algorithm for accessing deep web content;image mining: a new approach for datamining based on texture;decision making through multi rule algorithm: an extension to 1 rule algorithm;user centric retrieval of learning objects in LMS;web pre-fetching at proxy server using sequential datamining;privacy preserving datamining techniques: current scenario and future prospects;an approach to adaptive locality based maintenance of correlated data;voltage mode single OTRA based biquadratic filters;error estimation using correlator for OFDM based wireless communication system;raditation pattern & performance analysis of rectangular microstrip feed patch antenna using EM solution IE3D;systematic study of binocular depth finding using two web cameras;and reduced variable neighborhood search for page number minimization problem.
We present approaches for gesture classification and gesture segmentation by using machinelearning on the Kinect sensor's data stream. Our work involved three phases. Firstly we developed gesture classification f...
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ISBN:
(纸本)9781467318259;9781467318273
We present approaches for gesture classification and gesture segmentation by using machinelearning on the Kinect sensor's data stream. Our work involved three phases. Firstly we developed gesture classification from a known vocabulary of gestures in an edited data stream. Secondly we extended those techniques to detect and classify a gesture in an unedited stream which also captures random movements. Thirdly, we apply rules to filter out movements that were not intentional gestures and yet resembled certain gestures in our vocabulary.
Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives beforepur...
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ISBN:
(纸本)9781467355308;9781467355292
Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives beforepurchasing an item, but today the trend is to identify the opinions of a variety ofindividuals around the globe using microbloggingdata. This paper discusses an approach where a publicised stream of tweets from the Twitter microblogging site are preprocessed and classified based on their emotional content as positive, negative and irrelevant;and analyses the performance of various classifying algorithms based on their precision and recall in such cases. Further, the paper exemplifies the applications of this research and its limitations.
Mutual Information estimation is an important task for many datamining and machinelearning applications. In particular, many feature selection algorithms make use of the mutual information criterion and could thus b...
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ISBN:
(纸本)9789898425980
Mutual Information estimation is an important task for many datamining and machinelearning applications. In particular, many feature selection algorithms make use of the mutual information criterion and could thus benefit greatly from a reliable way to estimate this criterion. More precisely, the multivariate mutual information (computed between multivariate random variables) can naturally be combined with very popular search procedure such as the greedy forward to build a subset of the most relevant features. Estimating the mutual information (especially through density functions estimations) between high-dimensional variables is however a hard task in practice, due to the limited number of available data points for real-world problems. This paper compares different popular mutual information estimators and shows how a nearest neighbors-based estimator largely outperforms its competitors when used with high-dimensional data.
Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, dc-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-ba...
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ISBN:
(纸本)9783642254369;9783642254376
Kernel Principal Component Analysis (KPCA) is a widely used technique in the dimension reduction, dc-noising and discovering nonlinear intrinsic dimensions of data set. In this paper we describe a reweighing kernel-based classification method for improving recognition problem. Firstly, we map the training samples to the feature space by non-linear transformation, and then perform principal component analysis(PCA) using the selected kernel function in the feature space. and get the linear representation of testing samples in the feature space. Secondly, by using the idea of reweighting, we select the similarity between testing sample and each training sample as the weight of reweightine, then take the final weight as the criteria of classification. The experimental results demonstrate that our method is more accurate than Support Vector machine (SVM) classification method and Linear Discriminant Analysis (LDA) classification. In addition, the number of training samples that our method need is much smaller than some other methods.
machine to machine (M2M) communication has been gaining momentum in recent years as a key enabling technology for a wide range of applications including smart grid, e-health, home/industrial automation, and smart citi...
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ISBN:
(纸本)9781467325950;9781467325967
machine to machine (M2M) communication has been gaining momentum in recent years as a key enabling technology for a wide range of applications including smart grid, e-health, home/industrial automation, and smart cities. However, with the current communication systems mainly optimized for human to human communications, there are important capabilities that need to be developed in M2M systems in order to fully realize the new smart services enabled by M2M. In this paper, we provide an overview of M2M and its applications to smart grid. In particular, we discuss technical areas where datamining and machinelearning can play an important role in realizing various smart functionalities in the future power grid. As a case study, we also present a novel phase identification technique in smart grid based on smart meter data. Preliminary results have demonstrated the effectiveness of the proposed algorithm.
In this paper we present a novel approach to the problem of understanding, monitoring, and controlling the machining process of composites materials. The approach is called Logical Analysis of data (LAD). It is based ...
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The fault signature can be revealed by vibration analysis in machine fault detection and diagnosis. It is difficult to evaluate the status of machine for that non-stationary and non-linear vibrations are often caused ...
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