Search results for: nearest neighbor rule classifiers (k-NNR).

Authors: Hong Choon Ong

Abstract:

Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Naïve Bayes. The improvement in the classification using structural learning is shown if there exist relationship between the features or when they are not independent.

Keywords: Bayesian Network, Classification, Naïve Bayes, Structural Learning.

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

Abstract:

Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: Fake news detection, types of fake news, machine learning, natural language processing, classification techniques.

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Authors: Pankaj Swarnkar, Shailendra Jain, R.K Nema

Abstract:

Adaptive control involves modifying the control law used by the controller to cope with the fact that the parameters of the system being controlled change drastically due to change in environmental conditions or in system itself. This technique is based on the fundamental characteristic of adaptation of living organism. The adaptive control process is one that continuously and automatically measures the dynamic behavior of plant, compares it with the desired output and uses the difference to vary adjustable system parameters or to generate an actuating signal in such a way so that optimal performance can be maintained regardless of system changes. This paper deals with application of model reference adaptive control scheme in first order system. The rule which is used for this application is MIT rule. This paper also shows the effect of adaptation gain on the system performance. Simulation is done in MATLAB and results are discussed in detail.

Keywords: Adaptive control system, Adaptation gain, MIT rule, Model reference adaptive control.

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Authors: Sarabjeet Kaur Kochhar

Abstract:

With a surge of stream processing applications novel techniques are required for generation and analysis of association rules in streams. The traditional rule mining solutions cannot handle streams because they generally require multiple passes over the data and do not guarantee the results in a predictable, small time. Though researchers have been proposing algorithms for generation of rules from streams, there has not been much focus on their analysis. We propose Association rule profiling, a user centric process for analyzing association rules and attaching suitable profiles to them depending on their changing frequency behavior over a previous snapshot of time in a data stream. Association rule profiles provide insights into the changing nature of associations and can be used to characterize the associations. We discuss importance of characteristics such as predictability of linkages present in the data and propose metric to quantify it. We also show how association rule profiles can aid in generation of user specific, more understandable and actionable rules. The framework is implemented as SUPAR: System for Usercentric Profiling of Association Rules in streaming data. The proposed system offers following capabilities: i) Continuous monitoring of frequency of streaming item-sets and detection of significant changes therein for association rule profiling. ii) Computation of metrics for quantifying predictability of associations present in the data. iii) User-centric control of the characterization process: user can control the framework through a) constraint specification and b) non-interesting rule elimination.

Keywords: Data Streams, User subjectivity, Change detection, Association rule profiles, Predictability.

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Authors: R. AliMohammadzadeh, M. Rahgozar, A. Zarnani

Abstract:

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.

Keywords: XML, Data Mining, Association Rule Mining.

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Authors: Yoshiaki Shikata, Wataru Katagiri, Yoshitaka Takahashi

Abstract:

We propose a novel prioritized limited processor-sharing (PS) rule and a simulation algorithm for the performance evaluation of this rule. The performance measures of practical interest are evaluated using this algorithm. Suppose that there are two classes and that an arriving (class-1 or class-2) request encounters n1 class-1 and n2 class-2 requests (including the arriving one) in a single-server system. According to the proposed rule, class-1 requests individually and simultaneously receive m / (m * n1+ n2) of the service-facility capacity, whereas class-2 requests receive 1 / (m *n1 + n2) of it, if m * n1 + n2 ≤ C. Otherwise (m * n1 + n2 > C), the arriving request will be queued in the corresponding class waiting room or rejected. Here, m (
1) denotes the priority ratio, and C ( ∞), the service-facility capacity. In this rule, when a request arrives at [or departs from] the system, the extension [shortening] of the remaining sojourn time of each request receiving service can be calculated using the number of requests of each class and the priority ratio. Employing a simulation program to execute these events and calculations enables us to analyze the performance of the proposed prioritized limited PS rule, which is realistic in a time-sharing system (TSS) with a sufficiently small time slot. Moreover, this simulation algorithm is expanded for the evaluation of the prioritized limited PS system with N
3 priority classes.

Keywords: PS rule, priority ratio, service-facility capacity, simulation algorithm, sojourn time, performance measures

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

Abstract:

Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: M. A. Sedghamiz, S. Raeissi

Abstract:

This study presents three different approaches to estimate bubble point pressures for the binary system of CO2 and ethyl palmitate fatty acid ethyl ester. The first method involves the Peng-Robinson (PR) Equation of State (EoS) with the conventional mixing rule of Van der Waals. The second approach involves the PR EOS together with the Wong Sandler (WS) mixing rule, coupled with the UNIQUAC GE model. In order to model the bubble point pressures with this approach, the volume and area parameter for ethyl palmitate were estimated by the Hansen group contribution method. The last method involved the Peng-Robinson, combined with the Wong-Sandler method, but using NRTL as the GE model. Results using the Van der Waals mixing rule clearly indicated that this method has the largest errors among all three methods, with errors in the range of 3.96-6.22%. The PR-WS-UNIQUAC method exhibited small errors, with average absolute deviations between 0.95 to 1.97 percent. The PR-WS-NRTL method led to the least errors, where average absolute deviations ranged between 0.65-1.7%.

Keywords: Bubble pressure, Gibbs excess energy model, mixing rule, CO2 solubility, ethyl palmitate.

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

Abstract:

Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: LiDAR, real-time system, clustering, tracking, data association.

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Authors: Zaw Tun, Aung Htein Maw

Abstract:

The nature of wireless ad hoc and sensor networks make them very attractive to attackers. One of the most popular and serious attacks in wireless ad hoc networks is wormhole attack and most proposed protocols to defend against this attack used positioning devices, synchronized clocks, or directional antennas. This paper analyzes the nature of wormhole attack and existing methods of defending mechanism and then proposes round trip time (RTT) and neighbor numbers based wormhole detection mechanism. The consideration of proposed mechanism is the RTT between two successive nodes and those nodes- neighbor number which is needed to compare those values of other successive nodes. The identification of wormhole attacks is based on the two faces. The first consideration is that the transmission time between two wormhole attack affected nodes is considerable higher than that between two normal neighbor nodes. The second detection mechanism is based on the fact that by introducing new links into the network, the adversary increases the number of neighbors of the nodes within its radius. This system does not require any specific hardware, has good performance and little overhead and also does not consume extra energy. The proposed system is designed in ad hoc on-demand distance vector (AODV) routing protocol and analysis and simulations of the proposed system are performed in network simulator (ns-2).

Keywords: AODV, Wormhole attacks, Wireless ad hoc andsensor networks

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Authors: Adam Świtoński, Agnieszka Michalczuk, Henryk Josiński, Andrzej Polański, KonradWojciechowski

Abstract:

The method of gait identification based on the nearest neighbor classification technique with motion similarity assessment by the dynamic time warping is proposed. The model based kinematic motion data, represented by the joints rotations coded by Euler angles and unit quaternions is used. The different pose distance functions in Euler angles and quaternion spaces are considered. To evaluate individual features of the subsequent joints movements during gait cycle, joint selection is carried out. To examine proposed approach database containing 353 gaits of 25 humans collected in motion capture laboratory is used. The obtained results are promising. The classifications, which takes into consideration all joints has accuracy over 91%. Only analysis of movements of hip joints allows to correctly identify gaits with almost 80% precision.

Keywords: Biometrics, dynamic time warping, gait identification, motion capture, time series classification, quaternion distance functions, attribute ranking.

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Authors: D. Sasikala, K. Premalatha

Abstract:

The use of eXtensible Markup Language (XML) in web, business and scientific databases lead to the development of methods, techniques and systems to manage and analyze XML data. Semi-structured documents suffer due to its heterogeneity and dimensionality. XML structure and content mining represent convergence for research in semi-structured data and text mining. As the information available on the internet grows drastically, extracting knowledge from XML documents becomes a harder task. Certainly, documents are often so large that the data set returned as answer to a query may also be very big to convey the required information. To improve the query answering, a Semantic Tree Based Association Rule (STAR) mining method is proposed. This method provides intentional information by considering the structure, content and the semantics of the content. The method is applied on Reuter’s dataset and the results show that the proposed method outperforms well.

Keywords: Semi--structured Document, Tree based Association Rule (TAR), Semantic Association Rule Mining.

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Authors: Andrey V. Timofeev

Abstract:

The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, Classifiers Ensembles, LPBoost, C-OTDR systems, ν-OTDR systems.

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Authors: Harshit Mehrotra, Gaurav Agrawal, M.C. Srivastava

Abstract:

Computerized lip reading has been one of the most actively researched areas of computer vision in recent past because of its crime fighting potential and invariance to acoustic environment. However, several factors like fast speech, bad pronunciation, poor illumination, movement of face, moustaches and beards make lip reading difficult. In present work, we propose a solution for automatic lip contour tracking and recognizing letters of English language spoken by speakers using the information available from lip movements. Level set method is used for tracking lip contour using a contour velocity model and a feature vector of lip movements is then obtained. Character recognition is performed using modified k nearest neighbor algorithm which assigns more weight to nearer neighbors. The proposed system has been found to have accuracy of 73.3% for character recognition with speaker lip movements as the only input and without using any speech recognition system in parallel. The approach used in this work is found to significantly solve the purpose of lip reading when size of database is small.

Keywords: Contour Velocity Model, Lip Contour Tracking, LipReading, Visual Character Recognition.

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Authors: Inaki Maulida Hakim, Alesandro Romero

Abstract:

This research was conducted in an automotive company in Indonesia to overcome the problem of high logistics cost. The problem causes high of additional truck delivery. From the breakdown of the problem, chosen one route, which has the highest gap value, namely for RE-04. Research methodology will be started from calculating the ideal condition, making simulation, calculating the ideal logistic cost, and proposing an improvement. From the calculation of the ideal condition, box arrangement was done on the truck has efficiency with three trucks delivery per day. Route simulation making uses Tecnomatix Plant Simulation software as a visualization for the company about how the system is occurred on route RE-04 in ideal condition. The last step is proposing improvements on the area of route RE-04. The route arrangement is done by Saving Method and sequence of each supplier with the Nearest Neighbor. The results of the proposed improvements are three new route groups, where was expected to decrease logistics cost and increase the average of the truck efficiency per day.

Keywords: Logistic cost, milkrun, simulation, efficiency.

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Authors: S. Qaedi, S. Seyedtabaii

Abstract:

Non-Destructive evaluation of in-service power transformer condition is necessary for avoiding catastrophic failures. Dissolved Gas Analysis (DGA) is one of the important methods. Traditional, statistical and intelligent DGA approaches have been adopted for accurate classification of incipient fault sources. Unfortunately, there are not often enough faulty patterns required for sufficient training of intelligent systems. By bootstrapping the shortcoming is expected to be alleviated and algorithms with better classification success rates to be obtained. In this paper the performance of an artificial neural network, K-Nearest Neighbour and support vector machine methods using bootstrapped data are detailed and shown that while the success rate of the ANN algorithms improves remarkably, the outcome of the others do not benefit so much from the provided enlarged data space. For assessment, two databases are employed: IEC TC10 and a dataset collected from reported data in papers. High average test success rate well exhibits the remarkable outcome.

Keywords: Dissolved gas analysis, Transformer incipient fault, Artificial Neural Network, Support Vector Machine (SVM), KNearest Neighbor (KNN)

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Authors: Loai AbdAllah, Ilan Shimshoni

Abstract:

Missing values in data are common in real world applications. Since the performance of many data mining algorithms depend critically on it being given a good metric over the input space, we decided in this paper to define a distance function for unlabeled datasets with missing values. We use the Bhattacharyya distance, which measures the similarity of two probability distributions, to define our new distance function. According to this distance, the distance between two points without missing attributes values is simply the Mahalanobis distance. When on the other hand there is a missing value of one of the coordinates, the distance is computed according to the distribution of the missing coordinate. Our distance is general and can be used as part of any algorithm that computes the distance between data points. Because its performance depends strongly on the chosen distance measure, we opted for the k nearest neighbor classifier to evaluate its ability to accurately reflect object similarity. We experimented on standard numerical datasets from the UCI repository from different fields. On these datasets we simulated missing values and compared the performance of the kNN classifier using our distance to other three basic methods. Our  experiments show that kNN using our distance function outperforms the kNN using other methods. Moreover, the runtime performance of our method is only slightly higher than the other methods.

Keywords: Missing values, Distance metric, Bhattacharyya distance.

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Authors: M. Moslehpour, S. Khorsandi

Abstract:

Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier.

Keywords: NDP, IPsec, SEND, CGA, Modifier, Malicious node, Self-Computing, Distributed-Computing.

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Authors: Kriangkrai Maneerat, Chutima Prommak

Abstract:

This paper presents an analysis of the localization accuracy of indoor positioning systems using Cramer-s rule via IEEE 802.15.4 wireless sensor networks. The objective is to study the impact of the methods used to convert the received signal strength into the distance that is used to compute the object location in the wireless indoor positioning system. Various methods were tested and the localization accuracy was analyzed. The experimental results show that the method based on the empirical data measured in the non line-of-sight (NLOS) environment yield the highest localization accuracy; with the minimum error distance less than 3 m.

Keywords: Indoor positioning systems, localization accuracy, wireless networks, Cramer's rule.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

Abstract:

The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: Emotion recognition, facial recognition, signal processing, machine learning.

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Authors: Prasan Pitiranggon, Nunthika Benjathepanun, Somsri Banditvilai, Veera Boonjing

Abstract:

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

Keywords: Fuzzy Rule Base, Rule Extraction, Rule Generation, Support Vector Machine.

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Authors: N. M. Kamoh, D. G. Gyemang, M. C. Soomiyol

Abstract:

This paper compared the efficiency of Simpson’s 1/3 and 3/8 rules for the numerical solution of first order Volterra integro-differential equations. In developing the solution, collocation approximation method was adopted using the shifted Legendre polynomial as basis function. A block method approach is preferred to the predictor corrector method for being self-starting. Experimental results confirmed that the Simpson’s 3/8 rule is more efficient than the Simpson’s 1/3 rule.

Keywords: Collocation shifted Legendre polynomials, Simpson’s rule and Volterra integro-differential equations.

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Authors: Emad E Abdallah, A.F. Otoom, ArwaSaqer, Ola Abu-Aisheh, Diana Omari, Ghadeer Salem

Abstract:

As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Naïve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features.

Keywords: Digital investigation, cybercrimes, emails forensics, anonymous emails, writing style, and authorship analysis

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Authors: R. Krishnamoorthi, N. Kannan

Abstract:

In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using the proposed Orthogonal Polynomials based transformation. We propose to generate the codebook by partitioning these feature vectors into a binary tree. Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed coding are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. The new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality.

Keywords: Orthogonal Polynomials, Image Coding, Vector Quantization, TSVQ, Binary Tree Classifier

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Authors: B. Prabhakara Rao, M. V. H. Bhaskara Murthy

Abstract:

An Ad hoc wireless network comprises of mobile terminals linked and communicating with each other sans the aid of traditional infrastructure. Optimized Link State Protocol (OLSR) is a proactive routing protocol, in which routes are discovered/updated continuously so that they are available when needed. Hello messages generated by a node seeks information about its neighbor and if the latter fails to respond to a specified number of hello messages regulated by neighborhood hold time, the node is forced to assume that the neighbor is not in range. This paper proposes to evaluate OLSR routing protocol in a random mobility network having various neighborhood hold time intervals. The throughput and delivery ratio are also evaluated to learn about its efficiency for multimedia loads.

Keywords: Ad hoc Network, Optimized Link State Routing, Multimedia traffic

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Authors: Sangdeok Lee, Sangwon Han, Changtaek Hyun

Abstract:

Construction defects are major components that result in negative impacts on project performance including schedule delays and cost overruns. Since construction defects generally occur when a few associated causes combine, a thorough understanding of defect causality is required in order to more systematically prevent construction defects. To address this issue, this paper uses association rule mining (ARM) to quantify the causality between defect causes, and social network analysis (SNA) to find indirect causality among them. The suggested approach is validated with 350 defect instances from concrete works in 32 projects in Korea. The results show that the interrelationships revealed by the approach reflect the characteristics of the concrete task and the important causes that should be prevented.

Keywords: Causality, defect causes, social network analysis, association rule mining.

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Authors: Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh

Abstract:

Although face recognition seems as an easy task for human, automatic face recognition is a much more challenging task due to variations in time, illumination and pose. In this paper, the influence of time-lapse on visible and thermal images is examined. Orthogonal moment invariants are used as a feature extractor to analyze the effect of time-lapse on thermal and visible images and the results are compared with conventional Principal Component Analysis (PCA). A new triangle square ratio criterion is employed instead of Euclidean distance to enhance the performance of nearest neighbor classifier. The results of this study indicate that the ideal feature vectors can be represented with high discrimination power due to the global characteristic of orthogonal moment invariants. Moreover, the effect of time-lapse has been decreasing and enhancing the accuracy of face recognition considerably in comparison with PCA. Furthermore, our experimental results based on moment invariant and triangle square ratio criterion show that the proposed approach achieves on average 13.6% higher in recognition rate than PCA.

Keywords: Infrared Face recognition, Time-lapse, Zernike moment invariants

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

Abstract:

STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to real-world data

Keywords: Rule induction, decision table, missing data, noise.

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Authors: L. A. Muhammad, M. S. Argungu

Abstract:

Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Haluk Gümüskaya, Hüseyin Hakkoymaz

Abstract:

This paper describes WiPoD (Wireless Position Detector) which is a pure software based location determination and tracking (positioning) system. It uses empirical signal strength measurements from different wireless access points for mobile user positioning. It is designed to determine the location of users having 802.11 enabled mobile devices in an 802.11 WLAN infrastructure and track them in real time. WiPoD is the first main module in our LBS (Location Based Services) framework. We tested K-Nearest Neighbor and Triangulation algorithms to estimate the position of a mobile user. We also give the analysis results of these algorithms for real time operations. In this paper, we propose a supportable, i.e. understandable, maintainable, scalable and portable wireless positioning system architecture for an LBS framework. The WiPoD software has a multithreaded structure and was designed and implemented with paying attention to supportability features and real-time constraints and using object oriented design principles. We also describe the real-time software design issues of a wireless positioning system which will be part of an LBS framework.

Keywords: Indoor location determination and tracking, positioning in Wireless LAN.

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