Search results for: classifier fusion

Authors: Omid Sojodishijani, Saeed Ebrahimijam, Vahid Rostami

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This paper presents an optimized algorithm for robot localization which increases the correctness and accuracy of the estimating position of mobile robot to more than 150% of the past methods [1] in the uncertain and noisy environment. In this method the odometry and vision sensors are combined by an adapted well-known discrete kalman filter [2]. This technique also decreased the computation process of the algorithm by DKF simple implementation. The experimental trial of the algorithm is performed on the robocup middle size soccer robot; the system can be used in more general environments.

Keywords: Discrete Kalman filter, odometry sensor, omnidirectional vision sensor, Robot Localization.

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Authors: Lucas A. Silva, Carla R. Aguiar

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This work presents a proposal to perform contextual sentiment analysis using a supervised learning algorithm and disregarding the extensive training of annotators. To achieve this goal, a web platform was developed to perform the entire procedure outlined in this paper. The main contribution of the pipeline described in this article is to simplify and automate the annotation process through a system of analysis of congruence between the notes. This ensured satisfactory results even without using specialized annotators in the context of the research, avoiding the generation of biased training data for the classifiers. For this, a case study was conducted in a blog of entrepreneurship. The experimental results were consistent with the literature related annotation using formalized process with experts.

Keywords: Contextualized classifier, naïve Bayes, sentiment analysis, untrained annotators.

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Authors: M.Radmehr, S.M.Anisheh, I.Yousefian

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In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.

Keywords: Fractal Dimension, Offline Signature Recognition, Radon Transform, Support Vector Machine

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Authors: Nebi Gedik

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Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi

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A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.

Keywords: Artificial neural networks, cable, fault location andfault classification.

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Authors: Othman Maklouf, Abdurazag Ghila, Ahmed Abdulla

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This paper introduces a low cost INS/GPS algorithm for land vehicle navigation application. The data fusion process is done with an extended Kalman filter in cascade configuration mode. In order to perform numerical simulations, MATLAB software has been developed. Loosely coupled configuration is considered. The results obtained in this work demonstrate that a low-cost INS/GPS navigation system is partially capable of meeting the performance requirements for land vehicle navigation. The relative effectiveness of the kalman filter implementation in integrated GPS/INS navigation algorithm is highlighted. The paper also provides experimental results; field test using a car is carried out.

Keywords: GPS, INS, IMU, Kalman filter.

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Authors: Robert E. Hursig, Jane X. Zhang

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A robust still image face localization algorithm capable of operating in an unconstrained visual environment is proposed. First, construction of a robust skin classifier within a shifted HSV color space is described. Then various filtering operations are performed to better isolate face candidates and mitigate the effect of substantial non-skin regions. Finally, a novel Bhattacharyya-based face detection algorithm is used to compare candidate regions of interest with a unique illumination-dependent face model probability distribution function approximation. Experimental results show a 90% face detection success rate despite the demands of the visually noisy environment.

Keywords: Audio-visual speech recognition, Bhattacharyyacoefficient, face detection,

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Authors: S. Veera Ragavan, V. Ganapathy

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Many environment specific methods and systems for Robot Navigation exist. However vast strides in the evolution of navigation technologies and system techniques create the need for a general unified framework that is scalable, modular and dynamic. In this paper a Unified Framework for a Robust Conflict-free Robot Navigation System that can be used for either a structured or unstructured and indoor or outdoor environments has been proposed. The fundamental design aspects and implementation issues encountered during the development of the module are discussed. The results of the deployment of three major peripheral modules of the framework namely the GSM based communication module, GIS Module and GPS module are reported in this paper.

Keywords: Localization, Sensor Fusion, Mapping, GIS, GPS, and Autonomous Mobile Robot Navigation.

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Authors: Kai-Chao Yang, Chun-Ming Huang, Chih-Chiao Yang, Chien-Ming Wu

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Sensors have been used in various kinds of academic fields and applications. In this article, we propose the idea of modularized sensors that combine multiple sensor modules into a unique sensor. We divide a sensor into several units according to functionalities. Each unit has different sensor modules, which share the same type of connectors and can be serially and arbitrarily connected each other. A user can combine different sensor modules into a sensor platform according to requirements. Compared with current modularized sensors, the proposed sensor platform is highly flexible and reusable. We have implemented the prototype of the proposed sensor platform, and the experimental results show the proposed platform can work correctly.

Keywords: Sensor device, sensor fusion.

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Authors: Pilar Rey-del-Castillo, Jesús Cardeñosa

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There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

Keywords: Classifier, imputation techniques, fuzzy systems, fuzzy min-max neural networks.

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Authors: Sang-Hong Park

Abstract:

This paper proposes an efficient method to classify inverse synthetic aperture (ISAR) images. Because ISAR images can be translated and rotated in the 2-dimensional image place, invariance to the two factors is indispensable for successful classification. The proposed method achieves invariance to translation and rotation of ISAR images using a combination of two-dimensional Fourier transform, polar mapping and correlation-based alignment of the image. Classification is conducted using a simple matching score classifier. In simulations using the real ISAR images of five scaled models measured in a compact range, the proposed method yields classification ratios higher than 97 %.

Keywords: Radar, ISAR, radar target classification, radar imaging.

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Authors: Hossein Sahoolizadeh, Davood Sarikhanimoghadam, Hamid Dehghani

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This paper proposes new hybrid approaches for face recognition. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform dimensionality reduction and linear discriminate analysis on the down sampled Gabor wavelet faces can increase the discriminate ability. Nearest feature space is extended to various similarity measures. In our experiments, proposed Gabor wavelet faces combined with extended neural net feature space classifier shows very good performance, which can achieve 93 % maximum correct recognition rate on ORL data set without any preprocessing step.

Keywords: Face detection, Neural Networks, Multi-layer Perceptron, Gabor wavelets.

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Authors: Ikmal Hisyam Mohamad Paris, Lilly Suriani Affendey, Norwati Mustapha

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In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student-s class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.

Keywords: Classification, Data Mining, Prediction, Combination of Multiple Classifiers.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

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In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Feature Selection, Learning with Kernels, SupportVector Machine, and Classification.

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Authors: A. Greco, N. Mammone, F.C. Morabito, M.Versaci

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This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.

Keywords: Tokamak, sensors, artificial neural network.

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Authors: Eric Jiang

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As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and naïve Bayes algorithms.

Keywords: Feature Selection, Latent Semantic Indexing, Text Classification, Vector Space Model.

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Authors: Khin May Win, Nan Sai Moon Kham

Abstract:

Identification of cancer genes that might anticipate the clinical behaviors from different types of cancer disease is challenging due to the huge number of genes and small number of patients samples. The new method is being proposed based on supervised learning of classification like support vector machines (SVMs).A new solution is described by the introduction of the Maximized Margin (MM) in the subset criterion, which permits to get near the least generalization error rate. In class prediction problem, gene selection is essential to improve the accuracy and to identify genes for cancer disease. The performance of the new method was evaluated with real-world data experiment. It can give the better accuracy for classification.

Keywords: Microarray data, feature selection, recursive featureelimination, support vector machines.

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Authors: Dewi Nasien, Siti S. Yuhaniz, Habibollah Haron

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In a handwriting recognition problem, characters can be represented using chain codes. The main problem in representing characters using chain code is optimizing the length of the chain code. This paper proposes to use randomized algorithm to minimize the length of Freeman Chain Codes (FCC) generated from isolated handwritten characters. Feedforward neural network is used in the classification stage to recognize the image characters. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of isolated handwritten when tested on NIST database.

Keywords: Handwriting Recognition, Freeman Chain Code andFeedforward Backpropagation Neural Networks.

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Authors: Qiong Wang

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One of the most important problems to solve is eye location for a driver fatigue monitoring system. This paper presents an efficient method to achieve fast and accurate eye location in grey level images obtained in the real-word driving conditions. The structure of eye region is used as a robust cue to find possible eye pairs. Candidates of eye pair at different scales are selected by finding regions which roughly match with the binary eye pair template. To obtain real one, all the eye pair candidates are then verified by using support vector machines. Finally, eyes are precisely located by using binary vertical projection and eye classifier in eye pair images. The proposed method is robust to deal with illumination changes, moderate rotations, glasses wearing and different eye states. Experimental results demonstrate its effectiveness.

Keywords: eye location, structure feature, driver fatiguemonitoring

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Authors: Lei Zhang, Tao Wang, Xiantong Zhen

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Naive Bayes Nearest Neighbor (NBNN) and its variants, i,e., local NBNN and the NBNN kernels, are local feature-based classifiers that have achieved impressive performance in image classification. By exploiting instance-to-class (I2C) distances (instance means image/video in image/video classification), they avoid quantization errors of local image descriptors in the bag of words (BoW) model. However, the performances of NBNN, local NBNN and the NBNN kernels have not been validated on video analysis. In this paper, we introduce these three classifiers into human action recognition and conduct comprehensive experiments on the benchmark KTH and the realistic HMDB datasets. The results shows that those I2C based classifiers consistently outperform the SVM classifier with the BoW model.

Keywords: Instance-to-class distance, NBNN, Local NBNN, NBNN kernel.

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Authors: Jaehwan Kang, Haejung Lee

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This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 309 female speakers and extracted a total of 134 speech features from the voice data consisting of 5 sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents 3 types of decisions: reserved, positive and negative decisions. In conclusion, 71.5% of the voice data were diagnosed by this system, of which 47.7% were correct positive decisions and 69.7% were correct negative decisions.

Keywords: Voice Classifier, Sasang Constitution Medicine, Traditional Korean Medicine, SCM, TKM.

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Authors: S. Burgalat, L. Teilhac, A. Brand, E. Chastel, M. Jumeline

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The paper presents actual benefits and drawbacks of a multidirectional autonomous hovercraft conceived with limited resources and designed for indoor exploration. Recent developments in the field have led to the apparition of very powerful automotive systems capable of very high calculation and exploration in complex unknown environments. They usually propose very complex algorithms, high precision/cost sensors and sometimes have heavy calculation consumption with complex data fusion. These systems are usually powerful but have a certain price, and the benefits may not be worth the cost, especially considering their hardware limitations and their power consumption. The present approach is to build a compromise between cost, power consumption and results preciseness.

Keywords: Hovercraft, Indoor Exploration, Autonomous, Multidirectional, Wireless Control.

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Authors: V. Geetha, K. S. Aprameya

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Early diagnosis of dental caries is essential for maintaining dental health. In this paper, method for diagnosis of dental caries is proposed using Laplacian filter, adaptive thresholding, texture analysis and Support Vector Machine (SVM) classifier. Analysis of the proposed method is compared with Otsu thresholding, watershed segmentation and active contouring method. Adaptive thresholding has comparatively better performance with 96.9% accuracy and 96.1% precision. The results are validated using statistical method, two-way ANOVA, at significant level of 5%, that shows the interaction of proposed method on performance parameter measures are significant. Hence the proposed technique could be used for detection of dental caries in automated computer assisted diagnosis system.

Keywords: Computer assisted diagnosis, dental caries, dental radiography, image segmentation.

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD.

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Authors: Suzan Wedyan

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Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.

Keywords: Associative Classification, Classification, Data Mining, Learning, Rule Ranking, Rule Pruning, Prediction.

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Authors: Jianhua Wang, Yan Yu

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This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid model intrusion detection system uses DARPA data set to do test evaluation. It uses ROC curve to display the test result intuitively. After the experiment it proves this hybrid model intrusion detection system can effectively improve the detection rate, and reduce the rate of false alarm and fail.

Keywords: RBF, Elman, anomaly detection, misuse detection, hybrid neural network.

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Authors: A. Borji, M. Hamidi

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In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces

Keywords: Persian font recognition, support vector machine, gabor filter.

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Authors: Tetyana Baydyk, Ernst Kussul, Sandra Bonilla Meza

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There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.

Keywords: Face recognition, Labeled Faces in the Wild (LFW) database, Random Local Descriptor (RLD), random features.

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Authors: Dewi Y. Liliana, Agung Hardianto, M. Ridok

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Digital news with a variety topics is abundant on the internet. The problem is to classify news based on its appropriate category to facilitate user to find relevant news rapidly. Classifier engine is used to split any news automatically into the respective category. This research employs Support Vector Machine (SVM) to classify Indonesian news. SVM is a robust method to classify binary classes. The core processing of SVM is in the formation of an optimum separating plane to separate the different classes. For multiclass problem, a mechanism called one against one is used to combine the binary classification result. Documents were taken from the Indonesian digital news site, www.kompas.com. The experiment showed a promising result with the accuracy rate of 85%. This system is feasible to be implemented on Indonesian news classification.

Keywords: classification, Indonesian news, text processing, support vector machine

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