the proceedings contain 70 papers. the special focus in this conference is on Soft Computing and patternrecognition. the topics include: Toward real-time high-frequency stock monitoring system using ***;sensitivity a...
ISBN:
(纸本)9783319606170
the proceedings contain 70 papers. the special focus in this conference is on Soft Computing and patternrecognition. the topics include: Toward real-time high-frequency stock monitoring system using ***;sensitivity analysis on effect of biomechanical factors for classifying vertebral deformities;soft feature based personal recognition;a new form of fuzzy reasoning tool to ensure both accuracy and readability;a novel edge based image steganography technique;RDE - reconstructed mutation strategy for differential evolution algorithm;predicting mobile application ratings using artificial neural network;dimensionality reduction of sift descriptor using vector decomposition for image classification;a comparative analysis of the different datamining tools by using supervised learning algorithms;a well organized phrase-based document clustering using ASCII values and adjacency list;reconstruction of 3-dimensional scenes using depth from defocus and artificial neural networks trained on fractals;performance comparison between apache hive and oracle SQL for big data analytics;design of wide beam hexagonal shaped circularly polarized patch antenna for WLAN application;an experimental comparison with time-series methods;advanced deep neural networks for patternrecognition;security enabled cluster head selection for wireless sensor network using improved firefly optimization;chaperoning the optimization of symmetric finFET circuits;solving machine part cell formation problem using genetic algorithm based evolutionary computing;DWT based source localization using microphone array;prevention of illegal content sharing in peer to peer systems;towards designing a framework for practical keystroke dynamics based authentication and exudates in detection and classification of diabetic retinopathy.
Variables selection is challenging task due mainly to huge search space. this study addresses the increasingly encountered challenge of variables selection. It addresses the application of machinelearning techniques ...
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Variables selection is challenging task due mainly to huge search space. this study addresses the increasingly encountered challenge of variables selection. It addresses the application of machinelearning techniques to the problem of variables selection. We detailed the various models of the variables selection and examined the basic steps that are used to select the cost-effective predictors. We also walked through the initial settings and all variables selection stages, including architecture configuration, strategy generation, learning, model induction, and scoring. Results from this study show that the cost and generalization were seen to improve significantly in terms of computing time and recognition accuracy when the proposed system is applied for medical diagnosis. Good comparisons with an experimental study demonstrate the multidisciplinary applications of our approach. 1877-0509 (C) 2017 the Authors. Published by Elsevier B.V.
Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. thus, it is necessary to optimize diagnostic met...
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Breast cancer (BC) is one of the leading causes of death in adult women worldwide and the best way to reduce mortality and improve prognosis is through early diagnosis. thus, it is necessary to optimize diagnostic methods;one option could be the automatic detection of patterns in 1D-II. In that respect, through recent analysis of unidimensional Immunoblot Images (1D-II), it was possible to distinguish between women with and without breast disease using as a discrimination criterion the presence of autoantibodies (bands) in their blood. However, the analysis of 1D-II is a difficult task even for an expert, generating great subjectivity and complexity in the process of interpretation. In the present study, a semi-automatic methodology for the bands' analysis contained in the 1D-II's was implemented and evaluated, the bands were extracted using digital image processing techniques. this was possible through the recognition of banding patterns represented as time series to distinguish between three classes: women with breast cancer (BC), women with benign breast pathology (BBP) and women without breast pathology (H). the classification was performed using the machinelearning algorithm k-nearest neighbors (KNN) with different parameters over the time series representation. the semi-automatic method here presented was able to reduce the time, complexity and subjectivity of the image analysis withthe performance metrics compared, obtaining similar percentages for both representations. Withthe traditional analysis, binary representation [Accuracy 72.8%, Precision 73.42% for three classes (BC, BBP and H) and Accuracy 90.91% Accuracy 92.55% Sensitivity 93.57% and Specificity 92.99% for two classes (BC and H)], versus Time series representation [Accuracy 66.4%, Precision 67.07% for three classes (BC, BBP and H) and Accuracy 86.36% Accuracy 87.31% Sensitivity 95.86% and Specificity 85.56% for two classes (BC and H)].
Human Computer Interaction has a significant impact in different fields of Information and Communication Technology. It is mainly due to the importance of interaction between human beings and the technologies they are...
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ISBN:
(纸本)9781785616525
Human Computer Interaction has a significant impact in different fields of Information and Communication Technology. It is mainly due to the importance of interaction between human beings and the technologies they are using. therefore, facial expression recognition has been widely used to enhance this interaction and make it more natural. Most of the proposed methods are based on images and even if they showed good performances, they do not match the real interaction model of people. Video is a rich source of information when considering the presence of temporal aspect. In this paper, we propose an approach that uses videos to recognize facial expressions and based on geometric features. We have tested it on a popular dataset and the carried experimentations showed promising results.
the proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of things (IoT). At the same time, new applications are creating challeng...
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ISBN:
(纸本)9783319419206;9783319419190
the proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. the gesture recognition model was built using the data collected from 52 unique subjects. the model was mapped onto Intel (R) Quark (TM) SE pattern Matching Engine, and field-tested using 8 additional subjects achieving 92% performance.
the proceedings contain 51 papers. the topics discussed include: Bayesian approach to the concept drift in the patternrecognition problems;transductive relational classification in the co-training paradigm;generalize...
ISBN:
(纸本)9783642315367
the proceedings contain 51 papers. the topics discussed include: Bayesian approach to the concept drift in the patternrecognition problems;transductive relational classification in the co-training paradigm;generalized nonlinear classification model based on cross-oriented choquet integral;reduction of distance computations in selection of pivot elements for balanced GHT structure;hot deck methods for imputing missing data: the effects of limiting donor usage;a new approach for association rule mining and bi-clustering using formal concept analysis;selecting classification algorithms with active testing;unsupervised grammar inference using the minimum description length principle;how many trees in a random forest?;constructing target concept in multiple instance learning using maximum partial entropy;and a new learning structure heuristic of Bayesian networks from data.
In this article we present a new and efficient algorithm to handle missing values in databases applied in datamining (DM). Missing values may harm the calculation of the clustering algorithm, and might lead to distor...
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ISBN:
(纸本)9783319210247;9783319210230
In this article we present a new and efficient algorithm to handle missing values in databases applied in datamining (DM). Missing values may harm the calculation of the clustering algorithm, and might lead to distorted results. therefore missing values must be treated before the DM. Commonly, methods to handle missing values are implemented as a separate process from the DM. this may cause a long runtime and may lead to redundant I/O accesses. As a result, the entire DM process may be inefficient. We present a new algorithm (km-Impute) which integrates clustering and imputation of missing values in a unified process. the algorithm was tested on real Red wine quality measures (from the UCI machinelearning Repository). km-Impute succeeded in imputing missing values and in building clusters as a unified integrated process. the structure and quality of clusters which were produced by km-Impute were similar to clusters of k-means. In addition, the clusters were analyzed by a wine expert. the clusters represented different types of Red wine quality. the success and the accuracy of the imputation were validated using another two datasets: White wine and Page blocks (from the UCI). the results were consistent withthe tests which were applied on Red wine: the ratio of success of imputation in all three datasets was similar. Although the complexity of km-Impute was the same as k-means, in practice it was more efficient when applying on middle sized databases: the runtime was significantly shorter than k-means and fewer iterations were required until convergence. km-Impute also performed much less I/O accesses in comparison to k-means.
machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective patternrecognition algorithm for the detection, identification, or quantification of various odors. ...
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machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective patternrecognition algorithm for the detection, identification, or quantification of various odors. data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy. (C) 2015 Wiley Periodicals, Inc.
Concept drift can be considered as a distribution mismatch problem where class distribution changes as a time passes. this problem is commonly found in classification task of datamining. Among the proposed solutions,...
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ISBN:
(纸本)9783319089799;9783319089782
Concept drift can be considered as a distribution mismatch problem where class distribution changes as a time passes. this problem is commonly found in classification task of datamining. Among the proposed solutions, the cost-based Class Distribution Estimation (CDE) shows the best performance in coping with difference in class distribution between train and test datasets. However there is still some problem, as CDE lost its performance when there is too much change in class distribution. In this paper, CDE-weight is proposed to reduce the impact of high change in class distribution. the idea is to use many models suitable with many class distributions along with dynamic weighting method that adjusts weight of each model according to its class distribution. Experimented results indicate that CDE-Weight methods are able to reduce the impact of misestimating and improve the classifier performance when train and test data are different.
machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective patternrecognition algorithm for the detection, identification, or quantification of various odors. ...
machine olfaction is an intelligent system that combines a cross-sensitivity chemical sensor array and an effective patternrecognition algorithm for the detection, identification, or quantification of various odors. data collected by the sensor array are the multivariate time series signals with a complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular nowadays. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy. (C) 2015 Wiley Periodicals, Inc.
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