Dino Isa

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.

Using the self organizing map for clustering of text documents

An increasing number of computational and statistical approaches have been used for text classification, including nearest-neighbor classification, naive Bayes classification, support vector machines, decision tree induction, rule induction, and artificial neural networks. Among these approaches, naive Bayes classifiers have been widely used because of its simplicity. Due to the simplicity of the Bayes formula, the naive Bayes classification algorithm requires a relatively small number of training data and shorter time in both the training and classification stages as compared to other classifiers. However, a major short coming of this technique is the fact that the classifier will pick the highest probability category as the one to which the document is annotated too. Doing this is tantamount to classifying using only one dimension of a multi-dimensional data set. The main aim of this work is to utilize the strengths of the self organizing map (SOM) to overcome the inadvertent dimensionality reduction resulting from using only the Bayes formula to classify. Combining the hybrid system with new ranking techniques further improves the performance of the proposed document classification approach. This work describes the implementation of an enhanced hybrid classification approach which affords a better classification accuracy through the utilization of two familiar algorithms, the naive Bayes classification algorithm which is used to vectorize the document using a probability distribution and the self organizing map (SOM) clustering algorithm which is used as the multi-dimensional unsupervised classifier.

A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine

This work implements a new text document classifier by integrating the K-nearest neighbor (KNN) classification approach with the support vector machine (SVM) training algorithm. The proposed Nearest Neighbor-Support Vector Machine hybrid classification approach is coined as SVM-NN. The KNN has been reported as one of the widely used text classification approaches due to its simplicity and efficiency in handling various types of text classification tasks. However, there exists a major problem of the KNN in determining the appropriate value for parameter K in order to guarantee high classification effectiveness. This is due to the fact that the selection of the value of parameter K has high impact on the accuracy of the KNN classifier. Other than determining the optimal value of parameter K, the KNN is also a lazy learning method which keeps the entire training samples until classification time. Hence, the computational process of the KNN has become intensive when the value of parameter K increases. In this paper, we propose the SVM-NN hybrid classification approach with the objective that to minimize the impact of parameter on classification accuracy. In the training stage, the SVM is utilized to reduce the training samples for each of the available categories to their support vectors (SVs). The SVs from different categories are used as the training data of nearest neighbor classification algorithm in which the Euclidean distance function is used to calculate the average distance between the testing data point to each set of SVs of different categories. The classification decision is made based on the category which has the shortest average distance between its SVs and the testing data point. The experiments on several benchmark text datasets show that the classification accuracy of the SVM-NN approach has low impact on the value of parameter, as compared to the conventional KNN classification model.

Feature selection for support vector machine-based face-iris multimodal biometric system

Multimodal biometric can overcome the limitation possessed by single biometric trait and give better classification accuracy. This paper proposes face-iris multimodal biometric system based on fusion at matching score level using support vector machine (SVM). The performances of face and iris recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Besides, a simple computation speed-up method is proposed for SVM. The results show that the proposed feature selection method is able improve the classification accuracy in terms of total error rate. The support vector machine-based fusion method also gave very promising results.

A REVIEW OF METAL OXIDE COMPOSITE ELECTRODE MATERIALS FOR ELECTROCHEMICAL CAPACITORS

With the emerging technology in the 21st century, which requires higher electrochemical performances, metal oxide composite electrodes in particular offer complementary properties of individual materials via the incorporation of both physical and chemical charge storage mechanism together in a single electrode. Numerous works reviewed herein have identified a wide variety of attractive metal oxide-based composite electrode material for symmetric and asymmetric electrochemical capacitors. The focus of the review is the detailed literature data and discussion regarding the electrochemical performance of various metal oxide composite electrodes fabricated from different configurations including binary and ternary composites. Additionally, projection of future development in hybrid capacitor coupling lithium metal oxides and carbonaceous materials are found to obtain significantly higher energy storage than currently available commercial electrochemical capacitors. This review describes the novel concept of l...

High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic

High Relevance Keyword Extraction (HRKE) facility is introduced to Bayesian text classification to perform feature/keyword extraction during the classifying stage, without needing extensive pre-classification processes. In order to perform the task of keyword extraction, HRKE facility uses the posterior probability value of keywords within a specific category associated with text document. The experimental results show that HRKE facility is able to ensure promising classification performance for Bayesian classifier while dealing with different text classification domains of varying characteristics. This method guarantees an effective and efficient Bayesian text classifier which is able to handle different domains of varying characteristics, with high accuracy while maintaining the simplicity and low cost processes of the conventional Bayesian classification approach.

PIPELINE DEFECT PREDICTION USING SUPPORT VECTOR MACHINES

Oil and gas pipeline condition monitoring is a potentially challenging process due to varying temperature conditions, harshness of the flowing commodity and unpredictable terrains. Pipeline breakdown can potentially cost millions of dollars worth of loss, not to mention the serious environmental damage caused by the leaking commodity. The proposed techniques, although implemented on a lab scale experimental rig, ultimately aim at providing a continuous monitoring system using an array of different sensors strategically positioned on the surface of the pipeline. The sensors used are piezoelectric ultrasonic sensors. The raw sensor signal will be first processed using the discrete wavelet transform (DWT) as a feature extractor and then classified using the powerful learning machine called the support vector machine (SVM). Preliminary tests show that the sensors can detect the presence of wall thinning in a steel pipe by classifying the attenuation and frequency changes of the propagating lamb waves. The SVM algorithm was able to classify the signals as abnormal in the presence of wall thinning.

Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach

This paper proposes an automatic folder allocation system for text documents through the implementation of a hybrid classification method which combines the Bayesian (Bayes) approach and the Support Vector Machines (SVMs). Folder allocation for text documents in computer is typically executed manually by the user. Every time the user creates text documents by using text editors or downloads the documents from the internet, and wishes to store these documents on the computer, the user needs to determine and allocate the appropriate folder in which to store these new documents. This situation is inconvenient as repeating the folder allocation each time a text document is stored becomes tedious especially when the numbers and layers of folders are huge and the structure is complex and continuously growing. This problem can be overcome by implementing Artificial Intelligence machine learning methods to classify the new text documents and allocate the most appropriate folder as the storage for them. In this paper we propose the Bayes-SVMs hybrid classification framework to perform the tedious task of automatically allocating the right folder for text documents in computers.

Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

Highlights? This paper presents an oil and gas pipeline failure prediction system using LRUT in conjunction with Euclidean-SVM approach. ? The conventional NDT pipeline failure prediction systems are deployed at pre-determined intervals. ? LRUT pipeline failure prediction system with Euclidean-SVM provides continuous monitoring for pipelines and makes decision without human errors and misinterpretations. ? In contrast to the conventional SVM, Euclidean-SVM is less dependent on the choice of the kernel function and parameters. ? Experimental results show that Euclidean-SVM has more consistent and better classification performance as compared to the conventional SVM. This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.

Electrochemical studies on nanometal oxide-activated carbon composite electrodes for aqueous supercapacitors

In present study, the electrochemical performance of eco-friendly and cost-effective titanium oxide (TiO2)-based and zinc oxide-based nanocomposite electrodes were studied in neutral aqueous Na2SO3 electrolyte, respectively. The electrochemical properties of these composite electrodes were studied using cyclic voltammetry (CV), galvanostatic charge–discharge (CD) and electrochemical impedance spectroscopy (EIS). The experimental results reveal that these two nanocomposite electrodes achieve the highest specific capacitance at fairly low oxide loading onto activated carbon (AC) electrodes, respectively. Considerable enhancement of the electrochemical properties of TiO2/AC and ZnO/AC nanocomposite electrodes is achieved via synergistic effects contributed from the nanostructured metal oxides and the high surface area mesoporous AC. Cations and anions from metal oxides and aqueous electrolyte such as Ti4+, Zn2+, Na+ and can occupy some pores within the high-surface-area AC electrodes, forming the electric double layer at the electrode–electrolyte interface. Additionally, both TiO2 and ZnO nanoparticles can provide favourable surface adsorption sites for anions which subsequently facilitate the faradaic processes for pseudocapacitive effect. These two systems provide the low cost material electrodes and the low environmental impact electrolyte which offer the increased charge storage without compromising charge storage kinetics.