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Automated Cardiac Disease Diagnosis Using Support Vector Machine

Automated Cardiac Disease Diagnosis Using Support Vector Mac...

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International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA)

作者： Samir S. Yadav Shivajirao M. Jadhav Rohit G. Bonde Shivani T. Chaudhari Dr. Babasaheb Ambedkar Technological University Raigad India

ISBN: (数字)9781728123394

ISBN: (纸本)9781728123400

This research is intended for the creation of an automatic cardiovascular disease detection system with pre-marked SPECT (Single photon emission computed tomography)data for training the program and for subsequently supplying it with unclassified SPECT data for classification as an indication of probable heart disease or not. We used a well-known and widely used classification algorithm named Support Vector Machines (SVM) for the purposes of this classification. In other terms, you instruct the machine; the system learns from the inputs you give. When the training is over, you can provide the system with undefined SPECT data and it is immediately identified by the system Clearly, the accuracy of this classification would depend on the precision of the pictures of the training system, the support vector machine parameters, the sophistication and many more variables that are discussed later. The project is of considerable significance in today's world, particularly if businesses offer a million-dollarreward to anyone capable of building automated systems detecting nearly every Star Wars (such as transcoder) disease.

关键词： aNN Support Vector Machines Linear Kernel SPECT Gaussian Kernel SVM Support Vector Network single photon emission computed tomography Heart Diseases Taxonomy Disease diagnosis Detection systems sorting algorithm Training Photon emission Business

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Towards Using Subpattern Distributions in Social Network Analysis

Towards Using Subpattern Distributions in Social Network Ana...

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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

作者： Benjamin Cabrera University Duisburg-Essen

ISBN: (纸本)9781509028474

Determining the frequencies and the distribution of small subgraph patterns in a large input graph is an important part of many graph based mining tasks such as Frequent Subgraph Mining (FSM) and Motif Detection. Due to the exponential number of such graph patterns the interpretation of the mining results is mostly limited to finding unexpectedly frequent patterns, and in general identifying few particularly interesting patterns to then understand their function in the network. However, the full distribution of patterns itself encodes much more information about the underlying graph. Looking at this pattern distribution could be of particular interest in Social Network Analysis because social networks seem to have more random noise and less hard structural constraints compared to other types of graphs. This makes it unlikely to find meaning in only the most frequent patterns. In this paper we contribute in two ways to the usage of pattern distributions in networks analysis. First, we introduce two different sampling-based algorithms for computing the mentioned pattern distributions. Second we sketch two original ideas which can be used to harness the information in pattern distributions for gaining insights on global and local properties of Social Networks.

关键词： Social network services Image edge detection Approximation algorithms Databases Context Classification algorithms Clustering algorithms distribution (property) Social network services Edge detection sorting algorithm Clustering algorithms Social network analysis Approximation algorithms Network analysis Database Social Networks subschema Line graph

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Unsupervised Hyperspectral Image Classification algorithm By Integrating Spatial-Spectral Information

Unsupervised Hyperspectral Image Classification Algorithm By...

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International Conference on Audio, Language and Image Processing

作者： Belkacem Baassou Mingyi He Shaohui Mei Yifan Zhang Shaanxi Provincial Key Laboratory of Information Acquisition and Processing (IAP) School of Electronics and Information Northwestern Polytechnical University Shaanxi Provincial Key Laboratory of Information Acquisition and Processing (IAP)School of Electronics and Information Northwestern Polytechnical University

ISBN: (纸本)9781467301732

An integrated spatial-spectral information algorithm for hyper spectral image classification is proposed, which uses spatial pixel association (SPA)by exploiting spectral information divergence (SID) and spectral clustering to reduce regions number and improve classification accuracy. Moreover, a class boundary correction method is also developed to minimize the misclassified pixels at the edge of each class and to solve the problem of merged classes. Experiments with hyper spectral data demonstrate the effectiveness and advantages of the proposed frame work over some traditional methods in term of classification accuracy.

关键词： frame work Image classification hyperspectral imagery sorting algorithm pixel TRADITIONAL METHODS Sudden Infant Death

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Incremental fuzzy learning algorithms in big data problems: a study on the size of learning subsets

Incremental fuzzy learning algorithms in big data problems: ...

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IEEE International Conference on Fuzzy Systems

作者： Rocio Romero-Zaliz Antonio González Raúl Pérez Department of Computer Science and Artificial Intelligence ETSIIT University of Granada Granada Spain

Incremental approaches may be used to speed up the learning process when a classification algorithm is dealing with big data bases. In this work we present a study on how the size and composition of the set of learnin... 详细信息

ISBN: (纸本)9781509060351

关键词： Big Data Adaptation models Prediction algorithms Databases Pragmatics Iris Computer science big data Pragmatics Prediction algorithms computer science Iris Iris Database Adaptation models sorting algorithm Incremental

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FPGA Implementation of Real-Time Soldier Activity Detection based on Neural Network Classifier in Smart Military Suit

FPGA Implementation of Real-Time Soldier Activity Detection ...

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IEEE Bombay Section Signature Conference (IBSSC)

作者： Nikhil B. Gaikwad Avinash G. Keskar Varun Tiwari NC Shivaprakash Electronics and Communication Department Visvesvaraya National Institute of Technology (VNIT) Nagpur India Department of Instrumentation and Applied Physics Indian Institute of Science (IISc) C V Raman Ave Bengaluru India

ISBN: (纸本)9781538674765

The wearable technology carries sufficient potential to incorporate smartness into working of the military workforces like the Military Control Unit, Medical Responders, Backup Unit and War Strategist. The proposed work focuses on real-time soldier activity detection, which is essential for the operation of the smart military suit. The customized Artificial Neural Network (ANN) IP core is developed for the soldier activity classification, which is an integral component of suit gateway design. The multilayer perceptron (7-5-4) classification algorithm is implemented on the low-cost (99$) FPGA evaluation platform by using Xilinx vivado and system generator development tools. The training (70%) and testing (30%) of this ANN design is performed on the UCI human activity dataset. The LabVIEW GUI and IP test design completed the hardware testing of this IP. The presented ANN IP is able to achieve 98.5% classification accuracy by utilizing minimal FPGA (Artix-7 xc7a35t) resources. The implemented ANN design requires only 285 nanoseconds for a classification and consumes 103 milliwatts of dynamic power. The system's accuracy at different development levels is also studied in this work.

关键词： Smart military suit Soldier activity detection Artificial neural network Smart sensor gateway Network centric warfare Field programmable gate array Field programmable gate arrays Artificial neural networks Soldiers Activity Detection Military Personnel Hardware Test brightness EIS IMPLEMENTATION PLANS Bayesian classifier sorting algorithm

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FEATURE SELECTION BY HIGH DIMENSIONAL MODEL REPRESENTATION AND ITS APPLICATION TO REMOTE SENSING

FEATURE SELECTION BY HIGH DIMENSIONAL MODEL REPRESENTATION A...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Gulsen Taskin Kaya Huseyin Kaya Okan K. Ersoy Istanbul Technical Univ. Informatics Institute Purdue Univ. School of Electrical and Computer Eng.

ISBN: (纸本)9781467311601

In supervised classification, performance of a classification algorithm depends on several factors when dealing with a data set with a large number of features. One of them is the size of training data used in classification with respect to the number of features. Having less number of training data than the number of features can cause a reduction in classification performance with the addition of more features, called Hughes effect. A large number of features does not also guarantee higher classification accuracy as some of these features may be irrelevant for predicting the class labels. Additionally, the more features used during the training process, the higher is the computational time elapsed. Therefore, in terms of classification accuracy and less computational time, dimensionality reduction in feature size should be considered during classification when having the data with a large number of features. Two categories for reduction of dimensionality have been commonly encountered in the literature;feature extraction and feature selection. In feature extraction methods, new features are generated by taking linear combination of existing features. Fisher's linear discriminant analysis (LDA) is one of the most commonly used methods which aim to estimate a linear transformation to map data from a high dimensional space into a low-dimensional subspace that maintains unchanged separability. There are also many feature extraction algorithms developed and applied to hyperspectral images in the literature. Benediktsson et al. used decision boundary feature extraction method (DBFE) in classification of hyperspectral data with neural networks [1]. Discriminant analysis feature extraction (DAFE) was also another feature extraction method used for dimensionality reduction in classification. Kuo et al. proposed a new nonparametric feature extraction method (NWFE) based on scatter matrices for reduction of features of high-dimensional data[2].

关键词： Feature extraction dimensionality reduction remote sensing sorting algorithm computational time Linear Combination linear transformations

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Risk Aware SUAS Path Planning in an Unstructured Wildfire Environment

Risk Aware SUAS Path Planning in an Unstructured Wildfire En...

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American Control Conference

作者： Rachit Aggarwal Alexander Soderlund Mrinal Kumar David Grymin Department of Mechanical and Aerospace Engineering The Ohio State University Columbus OH USA Department of Mechanical and Aerospace University The Ohio State University Columbus OH USA Control Science Center of Excellence Air Force Research Laboratory Wright-Patterson Air Force Base OH 45433

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper addresses path planning for small unmanned aerial vehicles (SUAS) in an unstructured environment with poorly understood and time varying obstacles. These environmental characteristics manifest in an evolving wildfire, which is used as the test-case for this work. An evidential framework is employed to estimate the current state of wildfire and the resulting heat aura at flight level. This approach accounts for ignorance in estimation that can result from frequent conflict among sensors operating in a harsh environment combined with a computational forecasting agent that must operate with poor models of fire evolution. The objective of SUAS mission design is to visit such points of high conflict in order to provide additional situational awareness. A novel unsupervised classification algorithm based on statistical formalism is developed to identify distinct keep-out zones within the estimated heat aura. Boundaries of identified obstacles are modeled as probabilistic barriers. The planning problem is posed as a chance-constrained optimal control problem, which is transcribed to a nonlinear program via pseudospectral discretization. It is shown that by actively varying the probability of violating obstacle boundaries, a family of solutions can be generated that elicit the risk associated with each mission design.

关键词： Path planning Wildfire flight altitude Nonlinear Programming Small unmanned aerial vehicles Harsh environments Awareness sorting algorithm Aura current status Environments CASING INSPECTION

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CENTROID-BASED TEXTURE CLASSIFICATION USING THE SIRV REPRESENTATION

CENTROID-BASED TEXTURE CLASSIFICATION USING THE SIRV REPRESE...

引用

IEEE International Conference on Image Processing

作者： Aurélien Schutz Lionel Bombrun Yannick Berthoumieu Université de Bordeaux ENSEIRB-Matmeca Laboratoire IMS Groupe Signal et Image

ISBN: (纸本)9781479923427

This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on multivariate stochastic modeling. The main interest of the multivariate modeling comparatively to the univariate case is to consider spatial dependency as additional features for characterizing texture content. The aim of this paper is twofold. Firstly, we introduce the Spherically Invariant Random Vector (SIRV) representation for the modeling of wavelet coefficients. Secondly, from the specific properties of the SIRV process, i.e. the independence between the two sub-processes of the compound model, we derive centroid estimation scheme. Experiments from various conventional texture databases are conducted and demonstrate the interest of the proposed classification algorithm.

关键词： textured images Jeffrey divergence SIRV model centroid supervised classification Centroid sorting algorithm textures additional features specific properties

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Semi-Supervised Sleep-Stage Scoring Based on Single Channel EEG

Semi-Supervised Sleep-Stage Scoring Based on Single Channel ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： A. M. Munk K. V. Olesen S. W. Gangstad L. K. Hansen Technical University of Denmark Department of Applied Mathematics and Computer Science DK-2800 Kongens Lyngby Denmark

ISBN: (纸本)9781538646595

The field of automatic sleep stage classification based on EEG has enjoyed substantial attention during the last decade, which has resulted in several supervised classification algorithms with highly encouraging performance. Such supervised machine learning algorithms require large training sets that have been manually labelled, and are time- and resource-consuming to acquire. Here we present a semi-supervised approach that can learn to distinguish the sleep stages from a one-night data set where only a fraction has been manually labelled. We show that for fractions larger than 50%, our semi-supervised approach performs as good as a similar, fully-supervised model.

关键词： EEG semi-supervised learning sleep stage scoring non-negative matrix factorizaton generalizable Gaussian mixture model Electroencephalography Semi-supervised Learning Scoring Scoring Manual handling Sleep Stages sorting algorithm

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ON THE ARCHITECTURE OF A BIG DATA CLASSIFICATION TOOL BASED ON A MAP REDUCE APPROACH FOR HYPERSPECTRAL IMAGE ANALYSIS

ON THE ARCHITECTURE OF A BIG DATA CLASSIFICATION TOOL BASED ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： V. A. Ayma R. S. Ferreira P. N. Happ D. A. B. Oliveira G. A. O. P. Costa R. Q. Feitosa A. Plaza P. Gamba Dept. of Electrical Engineering Pontifical Catholic University of Rio de Janeiro Dept. of Technology of Computers and Communications University of Extremadura Dept. of Electronics University of Pavia

ISBN: (纸本)9781479979301

Advances in remote sensors are providing exceptional quantities of large-scale data with increasing spatial, spectral and temporal resolutions, raising new challenges in its analysis, e.g. those presents in classification processes. This work presents the architecture of the InterIMAGE Cloud Platform (ICP): Data Mining Package;a tool able to perform supervised classification procedures on huge amounts of data, on a distributed infrastructure. The architecture is implemented on top of the MapReduce framework. The tool has four classification algorithms implemented taken from WEKA's machine learning library, namely: Decision Trees, Naive Bayes, Random Forest and Support Vector Machines. The SVM classifier was applied on datasets of different sizes (2 GB, 4 GB and 10 GB) for different cluster configurations (5, 10, 20, 50 nodes). The results show the tool as a potential approach to parallelize classification processes on big data.

关键词： MapReduce Big Data Classification algorithms Cloud Computing big data ARCHITECTURE Cloud Computing Building construction sorting algorithm Support Vector Network image analysis Remote sensing equipment temporal resolution Decision Trees

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