Search Word(s) in Title, Keywords, Authors, and Abstract: Classification

A Deep Belief Network for Electricity Utilisation Feature Analysis of Air Conditioners Using a Smart IoT Platform
Wei Song, Ning Feng, Yifei Tian, Simon Fong and Kyungeun Cho
Page: 162~175, Vol. 14, No.1, 2018
10.3745/JIPS.04.0056

Keywords: Cloud Computing, Deep Belief Network, IoT, Power Conservation, Smart Metre
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Currently, electricity consumption and feedback mechanisms are being widely researched in Internet of Things (IoT) areas to realise power consumption monitoring and management through the remote control of appliances. This paper aims to develop a smart electricity utilisation IoT platform with a deep belief network for electricity utilisation feature modelling. In the end node of electricity utilisation, a smart monitoring and control module is developed for automatically operating air conditioners with a gateway, which connects and controls the appliances through an embedded ZigBee solution. To collect electricity consumption data, a programmable smart IoT gateway is developed to connect an IoT cloud server of smart electricity utilisation via the Internet and report the operational parameters and working states. The cloud platform manages the behaviour planning functions of the energy-saving strategies based on the power consumption features analysed by a deep belief network algorithm, which enables the automatic classification of the electricity utilisation situation. Besides increasing the user’s comfort and improving the user’s experience, the established feature models provide reliable information and effective control suggestions for power reduction by refining the air conditioner operation habits of each house. In addition, several data visualisation technologies are utilised to present the power consumption datasets intuitively


Variations of AlexNet and GoogLeNet to Improve Korean Character Recognition Performance
Sang-Geol Lee, Yunsick Sung, Yeon-Gyu Kim and Eui-Young Cha
Page: 205~217, Vol. 14, No.1, 2018
10.3745/JIPS.04.0061

Keywords: Classification, CNN, Deep Learning, Korean Character Recognition
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Deep learning using convolutional neural networks (CNNs) is being studied in various fields of image recognition and these studies show excellent performance. In this paper, we compare the performance of CNN architectures, KCR-AlexNet and KCR-GoogLeNet. The experimental data used in this paper is obtained from PHD08, a large-scale Korean character database. It has 2,187 samples of each Korean character with 2,350 Korean character classes for a total of 5,139,450 data samples. In the training results, KCR-AlexNet showed an accuracy of over 98% for the top-1 test and KCR-GoogLeNet showed an accuracy of over 99% for the top-1 test after the final training iteration. We made an additional Korean character dataset with fonts that were not in PHD08 to compare the classification success rate with commercial optical character recognition (OCR) programs and ensure the objectivity of the experiment. While the commercial OCR programs showed 66.95% to 83.16% classification success rates, KCR-AlexNet and KCR-GoogLeNet showed average classification success rates of 90.12% and 89.14%, respectively, which are higher than the commercial OCR programs’ rates. Considering the time factor, KCR-AlexNet was faster than KCR-GoogLeNet when they were trained using PHD08; otherwise, KCR-GoogLeNet had a faster classification speed.


Impact of Instance Selection on kNN-Based Text Categorization
Fatiha Barigou
Page: 418~434, Vol. 14, No.2, 2018
10.3745/JIPS.02.0080

Keywords: Classification Accuracy, Classification Efficiency, Data Reduction, Instance Selection, k-Nearest Neighbors, Text Categorization
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With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.


Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm
Youssef Fahim, Hamza Rahhali, Mohamed Hanine, El-Habib Benlahmar, El-Houssine Labriji, Mostafa Hanoune and Ahmed Eddaoui
Page: 569~589, Vol. 14, No.3, 2018
10.3745/JIPS.01.0028

Keywords: Bat-Algorithm, Cloud Computing, Load Balancing, Pre-scheduling, Virtual Machines
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Cloud computing, also known as country as you go”, is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders’ number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software’s profitability. Our axis of research is the load balancing between a data center’s virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels (‘odd levels’ or ‘even levels’) in ascending order based on the meta-heuristic “Bat-algorithm”. The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.


Contribution to Improve Database Classification Algorithms for Multi-Database Mining
Salim Miloudi, Sid Ahmed Rahal and Salim Khiat
Page: 709~726, Vol. 14, No.3, 2018
10.3745/JIPS.04.0075

Keywords: Connected Components, Database Classification, Graph-Based Algorithm, Multi-Database Mining
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Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.


miRNA Pattern Discovery from Sequence Alignment
Xiaohan Sun and Junying Zhang
Page: 1527~1543, Vol. 13, No.6, 2017
10.3745/JIPS.04.0051

Keywords: Deep Sequencing Data, miRNA, Pattern Discovery
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MiRNA is a biological short sequence, which plays a crucial role in almost all important biological process. MiRNA patterns are common sequence segments of multiple mature miRNA sequences, and they are of significance in identifying miRNAs due to the functional implication in miRNA patterns. In the proposed approach, the primary miRNA patterns are produced from sequence alignment, and they are then cut into short segment miRNA patterns. From the segment miRNA patterns, the candidate miRNA patterns are selected based on estimated probability, and from which, the potential miRNA patterns are further selected according to the classification performance between authentic and artificial miRNA sequences. Three parameters are suggested that bi-nucleotides are employed to compute the estimated probability of segment miRNA patterns, and top 1% segment miRNA patterns of length four in the order of estimated probabilities are selected as potential miRNA patterns.


Generation of Finite Inductive, Pseudo Random, Binary Sequences
Paul Fisher, Nawaf Aljohani and Jinsuk Baek
Page: 1554~1574, Vol. 13, No.6, 2017
10.3745/JIPS.01.0021

Keywords: Pseudo Random, Linear Shift Registers, Finite Induction, Graphs, Hamiltonian Cycles
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This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS’s to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS’s can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS’s also have maximum Shannon entropy, while sequences from LFSR’s do not, nor are such sequences FI random.


Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network
Sanjeev Kumar and Mahesh Chandra
Page: 703~715, Vol. 13, No.4, 2017
10.3745/JIPS.01.0007

Keywords: Cascade-Forward Back Propagation Technique, Computer-Aided Diagnosis (CAD), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gray-Level Co-Occurrence Matrix (GLCM), Mammographic Image Analysis Society (MIAS) Database, Modified Sigmoid Function
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Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray- level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.


A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification
Yasmina Teldja Amghar and Hadria Fizazi
Page: 215~235, Vol. 13, No.2, 2017
10.3745/JIPS.01.0014

Keywords: Bacterial Foraging Optimization Algorithm, Hybrid, Image Classification, Radial Basic Function
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Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification


A Multi-Objective TRIBES/OC-SVM Approach for the Extraction of Areas of Interest from Satellite Images
Wafaa Benhabib and Hadria Fizazi
Page: 321~339, Vol. 13, No.2, 2017
10.3745/JIPS.02.0054

Keywords: Image Classification, MO-TRIBES, OC-SVM, Remote Sensing
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In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MOTRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.


Rough Set-Based Approach for Automatic Emotion Classification of Music
Babu Kaji Baniya and Joonwhoan Lee
Page: 400~416, Vol. 13, No.2, 2017
10.3745/JIPS.04.0032

Keywords: Attributes, Covariance, Discretize, Rough Set, Rules
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Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the 4th order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.


A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network
Bayu Adhi Tama and Kyung-Hyune Rhee
Page: 1203~1212, Vol. 13, No.5, 2017
10.3745/JIPS.03.0080

Keywords: Classifier Ensembles, Classifier’s Significance, Intrusion Detection Systems (IDSs), Wireless Network
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Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.


Nearest Neighbor Based Prototype Classification Preserving Class Regions
Doosung Hwang and Daewon Kim
Page: 1345~1357, Vol. 13, No.5, 2017
10.3745/JIPS.04.0045

Keywords: Class Prototype, Dissimilarity, Greedy Method, Nearest-Neighbor Rule, Set Cover Optimization
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A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.


Content-based Image Retrieval Using Texture Features Extracted from Local Energy and Local Correlation of Gabor Transformed Images
Hee-Hyung Bu, Nam-Chul Kim, Bae-Ho Lee and Sung-Ho Kim
Page: 1372~1381, Vol. 13, No.5, 2017
10.3745/JIPS.02.0075

Keywords: Content-based Image Retrieval, Gabor Transformation, Local Energy, Local Correlation, Texture Feature
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In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.


The Effects of Industry Classification on a Successful ERP Implementation Model
Sangmin Lee and Dongho Kim
Page: 169~181, Vol. 12, No.1, 2016
10.3745/JIPS.03.0047

Keywords: Enterprise Applications, Enterprise Resource Planning, ERP Industry, ERP Succe
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Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.


Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis
Leila Boussaad, Mohamed Benmohammed and Redha Benzid
Page: 392~409, Vol. 12, No.3, 2016
10.3745/JIPS.02.0043

Keywords: Active Appearance Model, Age-Invariant, Face Recognition, Kernel Fisher Analysis, 2D-Discrete Cosine Transform
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The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.


Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images
Arif Muntasa
Page: 591~611, Vol. 12, No.4, 2016
10.3745/JIPS.01.0011

Keywords: Eigenspaces, Feature Extraction, Homogeneous, Non-homogeneous
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High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces


Learning to Prevent Inactive Student of Indonesia Open University
Bayu Adhi Tama
Page: 165~172, Vol. 11, No.2, 2015
10.3745/JIPS.04.0015

Keywords: Educational Data Mining, Ensemble Techniques, Inactive Student, Open University
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The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.


A Dataset of Online Handwritten Assamese Characters
Udayan Baruah and Shyamanta M. Hazarika
Page: 325~341, Vol. 11, No.3, 2015
10.3745/JIPS.02.0008

Keywords: Assamese, Character Recognition, Dataset Collection, Data Verification, Online Handwriting, Support Vector Machine
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This paper describes the Tezpur University dataset of online handwritten Assamese characters. The online data acquisition process involves the capturing of data as the text is written on a digitizer with an electronic pen. A sensor picks up the pen-tip movements, as well as pen-up/pen-down switching. The dataset contains 8,235 isolated online handwritten Assamese characters. Preliminary results on the classification of online handwritten Assamese characters using the above dataset are presented in this paper. The use of the support vector machine classifier and the classification accuracy for three different feature vectors are explored in our research.


Classification of Textured Images Based on Discrete Wavelet Transform and Information Fusion
Chaimae Anibou, Mohammed Nabil Saidi and Driss Aboutajdine
Page: 421~437, Vol. 11, No.3, 2015
10.3745/JIPS.02.0028

Keywords: Discrete Wavelet Transform, Feature Extraction, Fuzzy Set Theory, Information Fusion, Probability Theory, Segmentation, Supervised Classification
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This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on infor- mation fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the per- formance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.


A Lightweight and Effective Music Score Recognition on Mobile Phones
Tam Nguyen and Gueesang Lee
Page: 438~449, Vol. 11, No.3, 2015
10.3745/JIPS.02.0023

Keywords: Mobile Camera, Music Score, SVM, Symbol Classification
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Recognition systems for scanned or printed music scores that have been implemented on personal computers have received attention from numerous scientists and have achieved significant results over many years. A modern trend with music scores being captured and played directly on mobile devices has become more interesting to researchers. The limitation of resources and the effects of illumination, distortion, and inclination on input images are still challenges to these recognition systems. In this paper, we introduce a novel approach for recognizing music scores captured by mobile cameras. To reduce the complexity, as well as the computational time of the system, we grouped all of the symbols extracted from music scores into ten main classes. We then applied each major class to SVM to classify the musical symbols separately. The experimental results showed that our proposed method could be applied to real time applications and that its performance is competitive with other methods.


Default Prediction for Real Estate Companies with Imbalanced Dataset
Yuan-Xiang Dong , Zhi Xiao and Xue Xiao
Page: 314~333, Vol. 10, No.2, 2014
10.3745/JIPS.04.0002
Keywords: Default prediction, Imbalanced dataset, Real estate listed companies, Minoritysample generation approach
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When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the twoclass imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using predictionoriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.


Extreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Deepak Ghimire and Joonwhoan Lee
Page: 443~458, Vol. 10, No.3, 2014
10.3745/JIPS.02.0004
Keywords: Bagging, Ensemble Learning, Extreme Learning Machine, Facial Expression Recognition, Histogram of Orientation Gradient
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An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.


Multimodal Biometric Using a Hierarchical Fusion of a Person’s Face, Voice, and Online Signature
Youssef Elmir, Zakaria Elberrichi and Réda Adjoudj
Page: 555~567, Vol. 10, No.4, 2014
10.3745/JIPS.02.0007
Keywords: Hierarchical Fusion, LDA, Multimodal Biometric Fusion, PCA
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Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.


Optical Character Recognition for Hindi Language Using a Neural-network Approach
Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato
Page: 117~140, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.117
Keywords: OCR, Pre-processing, Segmentation, Feature Vector, Classification, Artificial Neural Network (ANN)
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Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document"'"s textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.


Region-Based Facial Expression Recognition in Still Images
Gawed M. Nagi, Rahmita Rahmat, Fatimah Khalid and Muhamad Taufik
Page: 173~188, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.173
Keywords: Facial Expression Recognition (FER), Facial Features Detection, Facial Features Extraction, Cascade Classifier, LBP, One-Vs-Rest SVM
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In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.


Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer
Thang Hoang, Thuc Nguyen, Chuyen Luong, Son Do and Deokjai Choi
Page: 333~348, Vol. 9, No.2, 2013
10.3745/JIPS.2013.9.2.333
Keywords: Gait Recognition, Mobile Security, Accelerometer, Pattern Recognition, Authentication, Identification, Signal Processing
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Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.


Classifying Malicious Web Pages by Using an Adaptive Support Vector Machine
Young Sup Hwang, Jin Baek Kwon, Jae Chan Moon and Seong Je Cho
Page: 395~404, Vol. 9, No.3, 2013
10.3745/JIPS.2013.9.3.395
Keywords: adaptive classification, malicious web pages, support vector machine
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In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.


Opinion Bias Detection Based on Social Opinions for Twitter
A-Rong Kwon and Kyung-Soon Lee
Page: 538~547, Vol. 9, No.4, 2013
10.3745/JIPS.2013.9.4.538
Keywords: Social opinion, Personal opinion, Bias detection, Sentiment, Target
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In this paper, we propose a bias detection method that is based on personal and social opinions that express contrasting views on competing topics on Twitter. We used unsupervised polarity classification is conducted for learning social opinions on targets. The tf-idf algorithm is applied to extract targets to reflect sentiments and features of tweets. Our method addresses there being a lack of a sentiment lexicon when learning social opinions. To evaluate the effectiveness of our method, experiments were conducted on four issues using Twitter test collection. The proposed method achieved significant improvements over the baselines.


A Feature Selection-based Ensemble Method for Arrhythmia Classification
Erdenetuya Namsrai, Tsendsuren Munkhdalai, Meijing Li, Jung-Hoon Shin, Oyun-Erdene Namsrai and Keun Ho Ryu
Page: 31~40, Vol. 9, No.1, 2013
10.3745/JIPS.2013.9.1.031
Keywords: Data Mining, Ensemble Method, Feature Selection, Arrhythmia Classification
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In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.


An Active Co-Training Algorithm for Biomedical Named-Entity Recognition
Tsendsuren Munkhdalai, Meijing Li, Unil Yun, Oyun-Erdene Namsrai and Keun Ho Ryu
Page: 575~588, Vol. 8, No.4, 2012
10.3745/JIPS.2012.8.4.575
Keywords: Biomedical Named-Entity Recognition, Co-Training, Semi-Supervised Learning, Feature Processing, Text Mining
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Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-bycommittee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of fmeasure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.


An Approach to Art Collections Management and Content-based Recovery
Concepcion Perez de Celis Herrero, Jaime Lara Alvarez, Gustavo Cossio Aguilar and Maria J. Somodevilla Garcia
Page: 447~458, Vol. 7, No.3, 2011
10.3745/JIPS.2011.7.3.447
Keywords: Search by Content, Faceted Classification, IT, Collections Management, Metadata, Information Retrieval
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This study presents a comprehensive solution to the collection management, which is based on the model for Cultural Objects (CCO). The developed system manages and spreads the collections that are safeguarded in museums and galleries more easily by using IT. In particular, we present our approach for a non-structured search and recovery of the objects based on the annotation of artwork images. In this methodology, we have introduced a faceted search used as a framework for multi-classification and for exploring/browsing complex information bases in a guided, yet unconstrained way, through a visual interface.


A Survey of RFID Deployment and Security Issues
Amit Grover and Hal Berghel
Page: 561~580, Vol. 7, No.4, 2011
10.3745/JIPS.2011.7.4.561
Keywords: RFID, RFID Standards, RFID Protocols, RFID Security, EPC structure, RFID Applications, RFID Classification
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This paper describes different aspects of a typical RFID implementation. Section 1 provides a brief overview of the concept of Automatic Identification and compares the use of different technologies while Section 2 describes the basic components of a typical RFID system. Section 3 and Section 4 deal with the detailed specifications of RFID transponders and RFID interrogators respectively. Section 5 highlights different RFID standards and protocols and Section 6 enumerates the wide variety of applications where RFID systems are known to have made a positive improvement. Section 7 deals with privacy issues concerning the use of RFIDs and Section 8 describes common RFID system vulnerabilities. Section 9 covers a variety of RFID security issues, followed by a detailed listing of countermeasures and precautions in Section 10.


Stream-based Biomedical Classification Algorithms for Analyzing Biosignals
Simon Fong, Yang Hang, Sabah Mohammed and Jinan Fiaidhi
Page: 717~732, Vol. 7, No.4, 2011
10.3745/JIPS.2011.7.4.717
Keywords: Data Stream Mining, VFDT, OVFDT, C4.5 and Biomedical Domain
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Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.


A Classifiable Sub-Flow Selection Method for Traffic Classification in Mobile IP Networks
Akihiro Satoh, Toshiaki Osada, Toru Abe, Gen Kitagata, Norio Shiratori and Tetsuo Kinoshita
Page: 307~322, Vol. 6, No.3, 2010
10.3745/JIPS.2010.6.3.307
Keywords: Mobile IP Network, Traffic Classification, Network Management, Traffic Engineering, Machine Learning
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Traffic classification is an essential task for network management. Many researchers have paid attention to initial sub-flow features based classifiers for traffic classification. However, the existing classifiers cannot classify traffic effectively in mobile IP networks. The classifiers depend on initial sub-flows, but they cannot always capture the sub-flows at a point of attachment for a variety of elements because of seamless mobility. Thus the ideal classifier should be capable of traffic classification based on not only initial sub-flows but also various types of sub-flows. In this paper, we propose a classifiable sub-flow selection method to realize the ideal classifier. The experimental results are so far promising for this research direction, even though they are derived from a reduced set of general applications and under relatively simplifying assumptions. Altogether, the significant contribution is indicating the feasibility of the ideal classifier by selecting not only initial sub-flows but also transition sub-flows.


A Dynamic Approach to Estimate Change Impact using Type of Change Propagation
Chetna Gupta, Yogesh Singh and Durg Singh Chauhan
Page: 597~608, Vol. 6, No.4, 2010
10.3745/JIPS.2010.6.4.597
Keywords: Change Impact Analysis, Regression Testing, Software Maintenance, Software Testing
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Software evolution is an ongoing process carried out with the aim of extending base applications either for adding new functionalities or for adapting software to changing environments. This brings about the need for estimating and determining the overall impact of changes to a software system. In the last few decades many such change/impact analysis techniques have been developed to identify consequences of making changes to software systems. In this paper we propose a new approach of estimating change/impact analysis by classifying change based on type of change classification e.g. (a) nature and (b) extent of change propagation. The impact set produced consists of two dimensions of information: (a) statements affected by change propagation and (b) percentage i.e. statements affected in each category and involving the overall system. We also propose an algorithm for classifying the type of change. To establish confidence in effectiveness and efficiency we illustrate this technique with the help of an example. Results of our analysis are promising towards achieving the aim of the proposed endeavor to enhance change classification. The proposed dynamic technique for estimating impact sets and their percentage of impact will help software maintainers in performing selective regression testing by analyzing impact sets regarding the nature of change and change dependency.


SVD-LDA: A Combined Model for Text Classification
Nguyen Cao Truong Hai, Kyung-Im Kim and Hyuk-Ro Park
Page: 5~10, Vol. 5, No.1, 2009
10.3745/JIPS.2009.5.1.005
Keywords: Latent Dirichlet Allocation, Singular Value Decomposition, Input Filtering, Text Classification, Data Preprocessing.
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Text data has always accounted for a major portion of the world¡¯s information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a ¡°clean and clear¡± space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.


An Efficient Web Ontology Storage Considering Hierarchical Knowledge for Jena-based Applications
Dongwon Jeong, Heeyoung Shin, Doo-Kwon Baik and Young-Sik Jeong
Page: 11~18, Vol. 5, No.1, 2009
10.3745/JIPS.2009.5.1.011
Keywords: Ontology, Jena, OWL, Ontology, Storage, Hierarchical Structure
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As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.


Black Bridge: A Scatternet Formation Algorithm for Solving a New Emerging Problem
Minyi Guo, Yanqin Yang, Gongwei Zhang, Feilong Tang and Yao Shen
Page: 167~174, Vol. 5, No.4, 2009
10.3745/JIPS.2009.5.4.167
Keywords: Bluetooth, Statternet Formation, Bluetooth Communication Protocol
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Nowadays, it has become common to equip a device with Bluetooth. As such devices become pervasive in the world; much work has been done on forming them into a network, however, almost all the Bluetooth Scatternet Formation Algorithms assume devices are homogeneous. Even the exceptional algorithms barely mentioned a little about the different characteristics of devices like computational abilities, traffic loads for special nodes like bridge nodes or super nodes, which are usually the bottleneck in the scatternet. In this paper, we treat the devices differently not only based on the hardware characteristics, but also considering other conditions like different classes, different groups and so on. We use a two-phase Scatternet Formation Algorithm here: in the first phase, construct scatternets for a specified kind of devices; in the second phase, connect these scatternets by using least other kinds of devices as bridge nodes. Finally, we give some applications to show the benefit of classification.


Inverted Index based Modified Version of KNN for Text Categorization
Taeho Jo
Page: 17~26, Vol. 4, No.1, 2008
10.3745/JIPS.2008.4.1.017
Keywords: String Vector, K- Nearest Neighbor, Text Categorization
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This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.


Inverted Index based Modified Version of K-Means Algorithm for Text Clustering
Taeho Jo
Page: 67~76, Vol. 4, No.2, 2008
10.3745/JIPS.2008.4.2.067
Keywords: String Vector, K Means Algorithm, Text Clustering
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This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.


Optimization of Domain-Independent Classification Framework for Mood Classification
Sung-Pil Choi, Yuchul Jung and Sung-Hyon Myaeng
Page: 73~81, Vol. 3, No.2, 2007
None
Keywords: Text Classification, Mood Categorization, Information Retrieval, Feature Selection, Text Classification Application
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In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naïve Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.


Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera
Wook-Sun Shin, Doo-Heon Song and Chang-Hun Lee
Page: 52~57, Vol. 2, No.1, 2006
None
Keywords: Vehicle Type classification, Road Lane Detection, Model fitting, Vanishing Point, Machine Learning
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One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.


Robust Real-time Intrusion Detection System
Byung-Joo Kim and Il-Kon Kim
Page: 9~13, Vol. 1, No.1, 2005
None
Keywords: real-time IDS, kernel PCA. LS-SVM
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Computer security has become a critical issue with the rapid development of business and other ftansaction systems over the Intemet. The application of atlificial intelligence, machine learning and data mining techdques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performaace of a classifier. Selecting important features from input data leads to simplification olthe problem, and faster and more accuate detection rates. Thus selecting important features is ar impofiant issue in intrusion detection. Alother issue in intrusion detection is that inost of the intrusion detection systems are performed by offJine and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an online feature extraction method with the Least Squares Suppofi Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intntsion detection systems.


A Multiple Instance Learning Problem Approach Model to Anomaly Network Intrusion Detection
Ill-Young Weon, Doo-Heon Song, Sung-Bum Ko and Chang-Hoon Lee
Page: 14~21, Vol. 1, No.1, 2005
None
Keywords: Multiple Instance Learning Problem, Network Intrusion Detection, Anomaly Detection
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Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.


A Hierarchical Text Rating System for Objectionable Documents
Chi Yoon Jeong, Seung Wan Han and Taek Yong Nam
Page: 22~26, Vol. 1, No.1, 2005
None
Keywords: Objectionable documents, document analysis, text classification, hierarchical system, SVM
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In this paper, we classified the objectionable texts into four rates according to their harmfulness and proposed the hierarchical text rating system for objectionable documents. Since the documents in the same category have similarities in used words, expressions and structure of the document, the text rating system, which uses a single classification model, has low accuracy. To solve this problem, we separate objectionable documents into several subsets by using their properties, and then classify the subsets hierarchically. The proposed system consists of three layers. In each layer, we select features using the chi-square statistics, and then the weight of the features, which is calculated by using the TF-IDF weighting scheme, is used as an input of the non-linear SVM classifier. By means of a hierarchical scheme using the different features and the different number of features in each layer, we can characterize the objectionability of documents more effectively and expect to improve the performance of the rating system. We compared the performance of the proposed system and performance of several text rating systems and experimental results show that the proposed system can archive an excellent classification performance.


A Hardware/Software Codesign for Image Processing in a Processor Based Embedded System for Vehicle Detection
Hosun Moon, Sunghwan Moon, Youngbin Seo and Yongdeak Kim
Page: 27~31, Vol. 1, No.1, 2005
None
Keywords: Embedded System, ITS, Image Processing, Vehicle Detect
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Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.


Two-Dimensional Qualitative Asset Analysis Method based on Business Process-Oriented Asset Evaluation
Jung-Ho Eom, Seon-Ho Park, Tae-Kyung Kim and Tai-Myoung Chung
Page: 79~85, Vol. 1, No.1, 2005
None
Keywords: Risk management, Risk Analysis, Asset analysis, 2-dimensional qualitative analysis
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In this paper, we dealt with substantial asset analysis methodology applied to twodimensional asset classification and qualitative evaluation method according to the business process. Most of the existent risk analysis methodology and tools presented classification by asset type and physical evaluation by a quantitative method. We focused our research on qualitative evaluation with 2-dimensional asset classification. It converts from quantitative asset value with purchase cost, recovery and exchange cost, etc. to qualitative evaluation considering specific factors related to the business process. In the first phase, we classified the IT assets into tangible and intangible assets, including human and information data asset, and evaluated their value. Then, we converted the quantitative asset value to the qualitative asset value using a conversion standard table. In the second phase, we reclassified the assets using 2-dimensional classification factors reflecting the business process, and applied weight to the first evaluation results. This method is to consider the organization characteristics, IT asset structure scheme and business process. Therefore, we can evaluate the concrete and substantial asset value corresponding to the organization business process, even if they are the same asset type.


A Statistic Correlation Analysis Algorithm Between Land Surface Temperature and Vegetation Index
Hyung Moo Kim, Beob Kyun Kim and Kang Soo You
Page: 102~106, Vol. 1, No.1, 2005
None
Keywords: LST, NDVI, Correlation Analysis, Landsat ETM+
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As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.


Gait Recognition Algorithm Based on Feature Fusion of GEI Dynamic Region and Gabor Wavelets
Jun Huang, Xiuhui Wang and Jun Wang
Page: 0~0, Vol. 0, No.0, 0
10.3745/JIPS.02.0088
Keywords: Gait Recognition, Feature Fusion, Gabor Wavelets, GEI, KPCA
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The paper proposes a novel gait recognition algorithm based on feature fusion of gait energy image (GEI) dynamic region and Gabor, which consists of four steps. First, the gait contour images are extracted through the object detection, binarization and morphological process. Secondly, features of GEI at different angles and Gabor features with multiple orientations are extracted from the dynamic part of GEI, respectively. Then averaging method is adopted to fuse features of GEI dynamic region with features of Gabor wavelets on feature layer and the feature space dimension is reduced by an improved Kernel Principal Component Analysis (KPCA). Finally, the vectors of feature fusion are input into the support vector machine (SVM) based on multi classification to realize the classification and recognition of gait. The primary contributions of the paper are: a novel gait recognition algorithm based on based on feature fusion of GEI and Gabor is proposed; an improved KPCA method is used to reduce the feature matrix dimension; a SVM is employed to identify the gait sequences. The experimental results suggest that the proposed algorithm yields over 90% of correct classification rate, which testify that the method can identify better different human gait and get better recognized effect than other existing algorithms.