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International Conference on Control Communication & Computing India (ICCC)

作者： Saran, K. B. Sreelekha, G. Coll Engn Vadakara Dept Elect & Commun Engn Calicut Kerala India Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode Kerala India

ISBN: (纸本)9781467373494

Vehicle detection and classification is the most important and challenging stage of traffic surveillance using computer vision techniques. The videos captured using the closed circuit television (CCTV) cameras placed in roadsides or driveways are used for the surveillance. The surveillance system includes detection of moving vehicles, counting the number of vehicles and the classification of the detected vehicles. The main challenge of the computer vision technique is the real time applicability of the algorithms used. In this work a vehicle detection and classification algorithm which works in real time is proposed. The detection is carried out by the method of background subtraction where the background is modeled using the mixture of Gaussians and the detected vehicles are classified using the Artificial Neural Network (ANN) with a new set of features, Histograms of Oriented Gradients (HOG) and geometric measures of the vehicles. Experimental results shows that the proposed method with the new combination of features as training parameters for ANN give better result as compared to other popular algorithms.

关键词： Vehicle detection images computer vision vehicle new combination Artificial neural networks Drive ways traffic monitoring Gauss sorting algorithm video monitoring SURVEILLANCE SYSTEMS Roadside

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Feature Reduction Based on Genetic algorithm and Hybrid Model for Opinion Mining

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SCIENTIFIC PROGRAMMING 2015年第1期2015卷 15-15页

作者： Kalaivani, P. Shunmuganathan, K. L. Sathyabama Univ St Josephs Coll Engn Dept Comp Sci & Engn Madras 600119 Tamil Nadu India RMK Engn Coll Dept Comp Sci & Engn Madras Tamil Nadu India

With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naive Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar's test to compare the level of significance of the classifiers.

关键词： Hybrid modes sorting algorithm Support Vector Network Dataset opinion Cinema User preferences mining information gain Genetic algorithm (GA) Classifiers

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LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHETYPAL DICTIONARIES

LANDCOVER CLASSIFICATION WITH SELF-TAUGHT LEARNING ON ARCHET...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Ribana Roscher Christoph Romer Bjorn Waske Lutz Plumer Division of Remote Sensing and Geoinformatics Institute of Geographical Sciences Freie Universitat Berlin Department of Geoinformation Institute of Geodesy and Geoinformation University of Bonn

ISBN: (纸本)9781479979301

This paper introduces archetypal dictionaries for a self-taught learning framework for the application of landcover classification. Self-taught learning, an unsupervised representation learning method, is exploited to learn low-dimensional and discriminative higher-level features, which are used as input into a classification algorithm. Experiments are conducted using a multi-spectral Landsat 5 TM image of a study area in the north of Novo Progresso located in South America. Our results confirm that self-taught learning with archetypal dictionaries provide features, which can be used as input into a linear logistic regression classifier. The obtained classification accuracies are comparable to kernel-based classifier using the original features.

关键词： Self-taught learning Archetypal analysis Landcover classification Dictionaries as Topic Classifiers Learning Logistic Regression South America sorting algorithm Learning methods MULTISPECTRAL

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Improving Medical Diagnosis Reliability Using Boosted C5.0 Decision Tree empowered by Particle Swarm Optimization

Improving Medical Diagnosis Reliability Using Boosted C5.0 D...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Elnaz Pashaei Mustafa Ozen Nizamettin Aydin the Department of Computer Engineering Yildiz Technical University Istanbul Turkey Istanbul University Istanbul Turkey Department of Computer Engineering Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781424492695

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

关键词： PSO UCI CART parametric subharmonic oscillator Particle swarm optimization Decision Trees Classification Methods MICROCURIES useful area cart sorting algorithm Medical diagnosis Dataset Disease diagnosis

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Influence of ultrasound despeckling on the liver fibrosis classification

Influence of ultrasound despeckling on the liver fibrosis cl...

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International Conference on Image Processing Theory, Tools and Applications

作者： Alexander Khvostikov Andrey Krylov Julius Kamalov Alina Megroyan Lomonosov Moscow State University Department of Computational Mathematics and Cybernetics Petrovsky National Research Center of Surgery Laboratory of Ultrasonic Diagnosis

ISBN: (纸本)9781479986385

An analysis of speckle filtering influence on B-mode ultrasound image texture-based determination of the liver fibrosis stage has been performed. We developed a comprehensive method for liver texture analysis based on 10-20 textural characteristics. These characteristics were found as most informative from 1390 textural features calculated using Laws' masks, co-occurrence matrix, gray level run-length matrix, wavelets and statistical characteristics of the images. We used Siemens ACUSON S2000 ultrasound images of liver cuts along the right midclavicular line for more than 50 patients for fibrosis classification using the METAVIR score. The classification was performed using Multi-layer Perceptron, Random Forests and KNN classifiers with data balancing using SMOTE algorithm. The ultrasound despeckling was performed using SRAD algorithm with an entropy-based stopping criterion. It was found that speckle filtering procedure enhances the classification and increases AUROC value by 5%.

关键词： Texture analysis B-mode ultrasound images Speckle filtering Anisotropic diffusion Liver fibrosis Classification algorithms Liver Fibrosis Speckle Filtering texture analysis sorting algorithm Anisotropic magnetoresistance Ultrasonic Waves

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Interferogram-Based Breast Tumor Classification Using Microwave-Induced Thermoacoustic Imaging

Interferogram-Based Breast Tumor Classification Using Microw...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Hao Nan Benyamin Allahgholizadeh Haghi Amin Arbabian Department of Electrical Engineering Stanford University Stanford CA 94305 USA Department of Electrical Engineering and Department of Mathematics & Computer Science Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424492695

Microwave-induced thermoacoustic (TA) imaging combines the dielectric/conductivity contrast in the microwave range with the high resolution of ultrasound imaging. Lack of ionizing radiation exposure in TA imaging makes this technique suitable for frequent screening applications, as with breast cancer screening. In this paper we demonstrate breast tumor classification based on TA imaging. The sensitivity of the signal-based classification algorithm to errors in the estimation of tumor locations is investigated. To reduce this sensitivity, we propose to use the interferogram of received pressure waves as the feature basis used for classification, and demonstrate the robustness based on a finite-difference time-domain (FDTD) simulation framework.

关键词： microwave range resolution ultrasound microwave range TEST AUTHORIZATIONS TA tantalum Ultrasonic Waves Resolving power Breast Neoplasms ionizing radiation exposure Imaging interference pattern sorting algorithm Cancer Screening Error analysis

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Multimedia based instructional development: Bubble sort visualization

Multimedia based instructional development: Bubble sort visu...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Hadi Sutopo Faculty of Computer Science and Communication Science Kalbis Institute Indonesia

ISBN: (纸本)9781479983544

This paper is intended to develop a bubble sort algorithm visualization for an algorithm and Programming course in computer education. This learning model should encourage student's ability to learn the variety of sorting, such as selection sort, merge sort. etc. This research consists of 6 steps in visualization development such as need assessment, front-end analysis, design, development, implementation, and evaluation. During the testing step, the application is run and checked to confirm that it performs exactly what the author has intended and the students can learn bubble sorting algorithm by studying the visualization. Research findings indicated that the students increased their motivation and ability to create sorting visualitation application. Based on the data of process and product competencies taken from the evaluation, 85 per cent of the students were competent in sorting visualization programming and there were 15 per cent of the students were not competent.

关键词： algorithm Bubble sort Visualization Learning Visualization Students sorting programs MULTI-MEDIA SYSTEM algorithms Learning sorting algorithm bubbles grade computer-assisted instruction

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Learning Label Specific Features for Multi-Label Classification

Learning Label Specific Features for Multi-Label Classificat...

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IEEE International Conference on Data Mining

作者： Jun Huang Guorong Li Qingming Huang Xindong Wu School of Computer and Control Engineering University of Chinese Academy of Sciences Department of Computer Science University of Vermont

ISBN: (纸本)9781467395052

Binary relevance (BR) is a well-known framework for multi-label classification. It decomposes multi-label classification into binary (one-vs-rest) classification subproblems, one for each label. The BR approach is a simple and straightforward way for multi-label classification, but it still has several drawbacks. First, it does not consider label correlations. Second, each binary classifier may suffer from the issue of class-imbalance. Third, it can become computationally unaffordable for data sets with many labels. Several remedies have been proposed to solve these problems by exploiting label correlations between labels and performing label space dimension reduction. Meanwhile, inconsistency, another potential drawback of BR, is often ignored by researchers when they construct multi-label classification models. Inconsistency refers to the phenomenon that if an example belongs to more than one class label, then during the binary training stage, it can be considered as both positive and negative example simultaneously. This will mislead binary classifiers to learn suboptimal decision boundaries. In this paper, we seek to solve this problem by learning label specific features for each label. We assume that each label is only associated with a subset of features from the original feature set, and any two strongly correlated class labels can share more features with each other than two uncorrelated or weakly correlated ones. The proposed method can be applied as a feature selection method for multi-label learning and a general strategy to improve multi-label classification algorithms comprising a number of binary classifiers. Comparison with the state-of-the-art approaches manifests competitive performance of our proposed method.

关键词： binary sorter bromine Binary sorting algorithm Inconsistency

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Robust Face Detection with Eyes Occluded by the Shadow from Dazzling Avoidance System

Robust Face Detection with Eyes Occluded by the Shadow from ...

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International IEEE Conference on Intelligent Transportation Systems

作者： Xiangpeng Liu Axel Graeser Inst. of Autom. Univ. of Bremen Bremen Germany

ISBN: (纸本)9781467365970

Some novel vision-based dazzling avoidance systems, such as the ShadeVision system, can intelligently cast a shadow on driver's eye region to protect the eyes from the dazzling effect that are caused by the strong external light sources. However, during this protective state (eyes covered by shadow), the system still needs to detect the driver's face to realize further monitoring, e.g., the intention of the driver as well as the dazzling effect from another light source. The protective shadow will make it difficult to apply the state-of-the-art algorithms to detect the driver's face, as the eye information is of great importance to vision-based face detection. This paper presents a series of robust algorithms for the face detection with protective shadow cast on driver's eyes, among which the Partially Masked Training, the Consecutive Sub-block Training and the Overlapped Sub-block Training are proposed for the first time for this kind of task. The on-road experimental results verify the effectiveness of the designed algorithms.

关键词： computer vision face recognition learning (artificial intelligence) object detection traffic engineering computing ShadeVision system consecutive sub-block training dazzling effect eye protection light source overlapped sub-block training partially masked training protective shadow robust face detection vision-based dazzling avoidance system vision-based face detection Classification algorithms Face Face detection Nose Skin Training Vehicles dazzling avoidance system eyes occluded by shadow face detection overlapped training Facial Recognition Eye protection shadows LIGHT SOURCES sorting algorithm Eye Object detection Drivers Face Face Nose vehicle images computer vision Functional training

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Research on Object Self-Organizing Iterative Cluster algorithm of Emitter Identification

Research on Object Self-Organizing Iterative Cluster Algorit...

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Xiaoxuan Wang Lianwang Diao Xin Xu Sci. & Technol. on Inf. Syst. Eng. Lab. China Electronics Technology Group Corp 28th Research Institute Nanjing Jiangsu CN

ISBN: (纸本)9781479986477

A novel sorting algorithm based on clustering is proposed to resolve problems such as low sorting precision and even failure which are caused by conventional radar signal sorting algorithm applied in present high-density signal environment. According to the characters of electromagnetic parameters, the advantages and disadvantages of the traditional clustering algorithms in data mining have been discussed in this paper. By using traditional clustering algorithm, users have to specify in advance how many clusters are being sought and it cannot be processed well if signal samples are not in spherical cluster distribution. An improved clustering algorithm based on self-organizing iterative analytic is presented in this paper, which avoid relying on prior knowledge or depending on signals' distribution. Simulation result proves the validity and practicability of this algorithm. It provides a new way for object clustering problem in emitter identification.

关键词： data mining iterative methods pattern clustering radar computing radar signal processing sorting electromagnetic parameter characters emitter identification low sorting precision object self-organizing iterative cluster algorithm radar signal sorting algorithm Clustering Analysis Clustering Evaluation Emitter Identification K-means data mining Cluster Analysis radar computing Clustering algorithms Radar signal processing iterative methods sorting programs grade sorting algorithm RADAR SIGNALS emitters

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