Search results for: classification framework.

Authors: Porojan S., Sandu L., Topală F.

Abstract:

Simulation of occlusal function during laboratory material-s testing becomes essential in predicting long-term performance before clinical usage. The aim of the study was to assess the influence of chamfer preparation depth on failure risk of heat pressed ceramic crowns with and without zirconia framework by means of finite element analysis. 3D models of maxillary central incisor, prepared for full ceramic crowns with different depths of the chamfer margin (between 0.8 and 1.2 mm) and 6-degree tapered walls together with the overlying crowns were generated using literature data (Fig. 1, 2). The crowns were designed with and without a zirconia framework with a thickness of 0.4 mm. For all preparations and crowns, stresses in the pressed ceramic crown, zirconia framework, pressed ceramic veneer, and dentin were evaluated separately. The highest stresses were registered in the dentin. The depth of the preparations had no significant influence on the stress values of the teeth and pressed ceramics for the studied cases, only for the zirconia framework. The zirconia framework decreases the stress values in the veneer.

Keywords: Finite element analysis, full ceramic crown, zirconia framework, stresses.

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Authors: Rinkaj Goyal, Pravin Chandra, Yogesh Singh

Abstract:

In this paper we describe our efforts to design and implement an agent development framework that has the potential to scale to the size of any underlying network suitable for various ECommerce activities. The main novelty in our framework is it-s capability to allow the development of sophisticated, secured agents which are simple enough to be practical. We have adopted FIPA agent platform reference Model as backbone for implementation along with XML for agent Communication and Java Cryptographic Extension and architecture to realize the security of communication information between agents. The advantage of our architecture is its support of agents development in different languages and Communicating with each other using a more open standard i.e. XML

Keywords: Agent, Agent Development Framework, Agent Coordination, Security

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Authors: Faisal Alshuwaier, Ali Areshey

Abstract:

Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound (BB) method to simplify the texts.

Keywords: Extraction, Max-Prod, Fuzzy Relations, Text Mining, Memberships, Classification.

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Authors: Francesco Maiorana

Abstract:

In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house dataset from a Knowledge Discovery from Text (KDT) application and on a set of publicly available datasets used in international feature selection competitions. These datasets come from KDT applications, drug discovery as well as other applications. The knowledge of the correct classification available for the training and validation datasets is used to optimize the parameters for positive and negative feature extractions. The process becomes feasible for large and sparse datasets, as the ones obtained in KDT applications, by using both compression techniques to store the similarity matrix and speed up techniques of the Kohonen algorithm that take advantage of the sparsity of the input matrix. These improvements make it feasible, by using the grid, the application of the methodology to massive datasets.

Keywords: Clustering algorithm, Data mining, Feature selection, Grid, Kohonen Self Organizing Map.

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Authors: T. Anbu, K. Aravind Kumar

Abstract:

The road environment information is needed accurately for applications such as road maintenance and virtual 3D city modeling. Mobile laser scanning (MLS) produces dense point clouds from huge areas efficiently from which the road and its environment can be modeled in detail. Objects such as buildings, cars and trees are an important part of road environments. Different methods have been developed for detection of above such objects, but still there is a lack of accuracy due to the problems of illumination, environmental changes, and multiple objects with same features. In this work the comparison between different classifiers such as Multiclass SVM, kNN and Multiclass LDA for the road environment detection is analyzed. Finally the classification accuracy for kNN with LBP feature improved the classification accuracy as 93.3% than the other classifiers.

Keywords: Classifiers, feature extraction, mobile-based laser scanning, object location estimation.

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Authors: Konstantinos Liagkouras, Konstantinos Metaxiotis

Abstract:

In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques.

Keywords: Portfolio selection, optimization techniques, financial models, stochastics, heuristics.

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Authors: Jehad Al Dallal, Paul Sorenson

Abstract:

An application framework provides a reusable design and implementation for a family of software systems. Frameworks are introduced to reduce the cost of a product line (i.e., family of products that share the common features). Software testing is a time consuming and costly ongoing activity during the application software development process. Generating reusable test cases for the framework applications at the framework development stage, and providing and using the test cases to test part of the framework application whenever the framework is used reduces the application development time and cost considerably. Framework Interface Classes (FICs) are classes introduced by the framework hooks to be implemented at the application development stage. They can have reusable test cases generated at the framework development stage and provided with the framework to test the implementations of the FICs at the application development stage. In this paper, we conduct a case study using thirteen applications developed using three frameworks; one domain oriented and two application oriented. The results show that, in general, the percentage of the number of FICs in the applications developed using domain frameworks is, on average, greater than the percentage of the number of FICs in the applications developed using application frameworks. Consequently, the reduction of the application unit testing time using the reusable test cases generated for domain frameworks is, in general, greater than the reduction of the application unit testing time using the reusable test cases generated for application frameworks.

Keywords: FICs, object-oriented framework, object-orientedframework application, software testing.

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Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor

Abstract:

This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.

Keywords: Intelligent transportation systems, object detection, video processing, road traffic, vehicle counting, vehicle classification.

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Authors: Janet L. Kourik, Peter E. Maher

Abstract:

To investigate the correspondence of theory and practice, a successfully implemented Knowledge Management System (KMS) is explored through the lens of Alavi and Leidner-s proposed KMS framework for the analysis of an information system in knowledge management (Framework-AISKM). The applied KMS system was designed to manage curricular knowledge in a distributed university environment. The motivation for the KMS is discussed along with the types of knowledge necessary in an academic setting. Elements of the KMS involved in all phases of capturing and disseminating knowledge are described. As the KMS matures the resulting data stores form the precursor to and the potential for knowledge mining. The findings from this exploratory study indicate substantial correspondence between the successful KMS and the theory-based framework providing provisional confirmation for the framework while suggesting factors that contributed to the system-s success. Avenues for future work are described.

Keywords: Applied KMS, education, knowledge management (KM), KM framework, knowledge management system (KMS).

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Authors: Minsoo Lee, Yearn Jeong Kim, Yun-mi Kim, Sujeung Cheong, Sookyung Song

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Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.

Keywords: Ant Colony System, DNA chip data, Classification.

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Authors: Tudor Barbu

Abstract:

An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Then, an automatic pixel classification approach is proposed. The feature vectors are clustered using an unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: Image segmentation, moment-based texture analysis, automatic classification, validity indexes.

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Authors: Rami N. Khushaba, Adel Al-Jumaily

Abstract:

The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

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Authors: Yin-Yann Chen

Abstract:

As the gradual increase of the enterprise scale, the firms may possess many manufacturing plants located in different places geographically. This change will result in the multi-site production planning problems under the environment of multiple plants or production resources. Our research proposes the structural framework to analyze the multi-site planning problems. The analytical framework is composed of six elements: multi-site conceptual model, product structure (bill of manufacturing), production strategy, manufacturing capability and characteristics, production planning constraints, and key performance indicators. As well as the discussion of these six ingredients, we also review related literatures in this paper to match our analytical framework. Finally we take a real-world practical example of a TFT-LCD manufacturer in Taiwan to explain our proposed analytical framework for the multi-site production planning problems.

Keywords: Multi-Site, Production Planning, Supply Chain, TFT-LCD.

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Authors: Qiang Fu, Md. Abu Saleh

Abstract:

Research studies are highly fragmented when an Innovation Management Framework is being discussed. With the aim to identify an Innovation Management Framework/Assessment Tool suitable for Small & Medium Enterprises (SMEs) in the service industry, this researcher critically reviewed existing innovation management frameworks and assessment models/tools and discovered a number of literature gaps. It is established that the existing literature lacks generally agreed innovation management dimensions, commonly accepted knowledge creation through empirical studies on innovation management in SMEs, effective innovation management performance measurements, suitable innovation management framework in SMEs, and studies on innovation management in the service industry, in particular in retail SMEs. As such, there is a dire need to develop an appropriate firm-level innovation management framework suitable for SMEs in the service industry for future research projects and further studies. In addition, this researcher also discussed the significance of establishing such an innovation management framework.

Keywords: innovation management, innovation management framework, innovation management assessment tools, SMEs, service industry

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Authors: Essam Al-Daoud

Abstract:

Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.

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Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal

Abstract:

In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.

Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.

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Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.

Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.

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Authors: Ajay Verma, Nitin Seth

Abstract:

The purpose of this paper is to highlight the importance of the concept of competitiveness in the supply chain and to present a conceptual framework for Supply Chain Competitiveness (SCC). The framework is based on supply chain activities, which are inputs, necessary for SCC and the benefits which are the outputs of SCC. A literature review is conducted on key supply chain competitiveness issues, its determinants, its various dimensions followed by exploration for SCC. Based on the insights gained, a conceptual framework for SCC is presented based on activities for SCC, SCC environment and outcomes of SCC. The information flow in the conceptual framework is bi-directional at all levels and the activities are interrelated in a global competitive environment. The activities include the activities of suppliers, manufacturers and distributors, giving more emphasis on manufacturers- activities. Further, implications of various factors such as economic, politicolegal, technical, socio-cultural, competition, demographic etc. are also highlighted. The SCC framework is an attempt to cover the relatively less explored area of supply chain competitiveness. It is expected that this work will further motivate researchers, academicians and practitioners to work in this area and offers conceptual help in providing a directions for supply chain competitiveness which leads to improvement in the supply chain and supply chain performance.

Keywords: Competitive advantage, supply chain, SCC, supplychain management.

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Authors: Babak Darvish Rouhani, Mohd Naz’ri Mahrin, Fatemeh Nikpay, Maryam Khanian Najafabadi, Pourya Nikfard

Abstract:

Enterprise Architecture (EA) Implementation Methodologies have become an important part of EA projects. Several implementation methodologies have been proposed, as a theoretical and practical approach, to facilitate and support the development of EA within an enterprise. A significant question when facing the starting of EA implementation is deciding which methodology to utilize. In order to answer this question, a framework with several criteria is applied in this paper for the comparative analysis of existing EA implementation methodologies. Five EA implementation methodologies including: EAP, TOGAF, DODAF, Gartner, and FEA are selected in order to compare with proposed framework. The results of the comparison indicate that those methodologies have not reached a sufficient maturity as whole due to lack of consideration on requirement management, maintenance, continuum, and complexities in their process. The framework has also ability for the evaluation of any kind of EA implementation methodologies.

Keywords: Enterprise Architecture, Enterprise Architecture Implementation Methodology. EAIM, Evaluating EAIM, Framework for evaluation.

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Authors: Irina Pyrlina

Abstract:

This article presents the results of a study conducted to identify operational risks for information systems (IS) with service-oriented architecture (SOA). Analysis of current approaches to risk and system error classifications revealed that the system error classes were never used for SOA risk estimation. Additionally system error classes are not normallyexperimentally supported with realenterprise error data. Through the study several categories of various existing error classifications systems are applied and three new error categories with sub-categories are identified. As a part of operational risks a new error classification scheme is proposed for SOA applications. It is based on errors of real information systems which are service providers for application with service-oriented architecture. The proposed classification approach has been used to classify SOA system errors for two different enterprises (oil and gas industry, metal and mining industry). In addition we have conducted a research to identify possible losses from operational risks.

Keywords: Enterprise architecture, Error classification, Oil&Gas and Metal&Mining industries, Operational risks, Serviceoriented architecture

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Authors: Rana Yousef, Khalil el Hindi

Abstract:

The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and RBF networks were trained using these sets of centers. The performance of the RBF networks is studied in terms of classification accuracy and training time. The results obtained were compared with two Radial Basis Function Networks: RBF networks that use all instances of the training set as center points (RBF-ALL) and Probabilistic Neural Networks (PNN). The former achieves high classification accuracies and the latter requires smaller training time. Results showed that RBF networks trained using sets of centers located by noise-filtering techniques (ALLKNN and ENN) rather than pure reduction techniques produce the best results in terms of classification accuracy. The results show that these networks require smaller training time than that of RBF-ALL and higher classification accuracy than that of PNN. Thus, using ALLKNN and ENN to select center points gives better combination of classification accuracy and training time. Our experiments also show that using the reduced sets to train the networks is beneficial especially in the presence of noise in the original training sets.

Keywords: Radial basis function networks, Instance-based reduction, PNN.

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: Hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection.

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Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan

Abstract:

The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK

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Authors: Jaehwan Kang, Haejung Lee

Abstract:

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

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

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Authors: Nadia Albu, Cătălin Nicolae Albu, Mădălina Maria Gîrbină, Maria Iuliana Sandu

Abstract:

Professions are concerned about the public image they have, and this public image is represented by stereotypes. Research is needed to understand how accountants are perceived by different actors in the society in different contexts, which would allow universities, professional bodies and employers to adjust their strategies to attract the right people to the profession and their organizations. We aim to develop in this paper a framework to be used in empirical testing in different environments to determine and analyze the accountant-s stereotype. This framework will be useful in analyzing the nuances associated to the accountant-s image and in understanding the factors that may lead to uniformity in the profession and of those leading to diversity from one context (country, type of countries, region) to another.

Keywords: accounting profession, accounting stereotype, framework, public image

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Authors: Kamolchanok Panishkan, Kanokporn Swangjang, Natdhera Sanmanee, Daoroong Sungthong

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The objective of this research is to study principal component analysis for classification of 67 soil samples collected from different agricultural areas in the western part of Thailand. Six soil properties were measured on the soil samples and are used as original variables. Principal component analysis is applied to reduce the number of original variables. A model based on the first two principal components accounts for 72.24% of total variance. Score plots of first two principal components were used to map with agricultural areas divided into horticulture, field crops and wetland. The results showed some relationships between soil properties and agricultural areas. PCA was shown to be a useful tool for agricultural areas classification based on soil properties.

Keywords: soil organic matter, soil properties, classification, principal components

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Authors: Samir Brahim Belhaouari

Abstract:

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.

Keywords: Pattern recognition, Time series, k-Nearest Neighbor, k-means cluster, Gaussian Mixture Model, Classification

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Authors: Osa D Egonwa

Abstract:

In the 21^st century, the disciplinary boundaries of past centuries that we often create through mainstream art historical classification, techniques and sources may have been eroded by visual culture, which seems to provide a more inclusive umbrella for the new ways artists go about the creative process and its resultant commodities. Over the past four decades, artists in Africa have resorted to new materials, techniques and themes which have affected our ways of research on these artists and their art. Frontline artists such as El Anatsui, Yinka Shonibare, Erasmus Onyishi are demonstrating that any material is just suitable for artistic expression. Most of times, these materials come with their own techniques/effects and visual syntax: a combination of materials compounds techniques, formal aesthetic indexes, halo effects, and iconography. This tends to challenge the categories and we lean on to view, think and talk about them. This renders our main stream art historical research methods inadequate, thus suggesting new discursive concepts, terms and theories. This paper proposed the Africanist eclectic methods derived from the dual framework of Masquerade Theory and What Comes Behind Six is More Than Seven. This paper shares thoughts/research on art historical methods, terminological re-alignments on classification/source data, presentational format and interpretation arising from the emergent trends in our subject. The outcome provides useful tools to mediate new thoughts and experiences in recent African art and visual culture.

Keywords: Art Historical Methods, Classifications, Concepts , Re-alignment.

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Authors: S. Veera Ragavan, V. Ganapathy

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

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

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Authors: Faisal Aburub, Wael Hadi

Abstract:

Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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