International Journal of Information, Control and Computer Sciences

Decoupled Scheduling in Meta Environment

Year: 2007 Volume: 1 Issue: 3 673 - 677 Pages

Abstract: Grid scheduling is the process of mapping grid jobs to resources over multiple administrative domains. Traditionally, application-level schedulers have been tightly integrated with the application itself and were not easily applied to other applications. This design is generic that decouples the scheduler core (the search procedure) from the application-specific (e.g. application performance models) and platform-specific (e.g. collection of resource information) components used by the search procedure. In this decoupled approach the application details are not revealed completely to broker, but customer will give the application to resource provider for execution. In a decoupled approach, apart from scheduling, the resource selection can be performed independently in order to achieve scalability.

Variable Rough Set Model and Its Knowledge Reduction for Incomplete and Fuzzy Decision Information Systems

Year: 2007 Volume: 1 Issue: 6 1730 - 1734 Pages

Abstract: The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is the generalization of the variable precision rough set model for incomplete information system and that of rough set model for incomplete and fuzzy decision information system. The knowledge reduction and heuristic algorithm, built on the method and theory of precision reduction, are proposed.

A Study on Neural Network Training Algorithm for Multiface Detection in Static Images

Year: 2010 Volume: 4 Issue: 2 308 - 311 Pages

Abstract: This paper reports the study results on neural network training algorithm of numerical optimization techniques multiface detection in static images. The training algorithms involved are scale gradient conjugate backpropagation, conjugate gradient backpropagation with Polak-Riebre updates, conjugate gradient backpropagation with Fletcher-Reeves updates, one secant backpropagation and resilent backpropagation. The final result of each training algorithms for multiface detection application will also be discussed and compared.

Improving Academic Performance Prediction using Voting Technique in Data Mining

Year: 2010 Volume: 4 Issue: 2 304 - 307 Pages

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

Integrating Computational Intelligence Techniques and Assessment Agents in ELearning Environments

Year: 2007 Volume: 1 Issue: 12 3959 - 3968 Pages

Abstract: In this contribution an innovative platform is being presented that integrates intelligent agents and evolutionary computation techniques in legacy e-learning environments. It introduces the design and development of a scalable and interoperable integration platform supporting: I) various assessment agents for e-learning environments, II) a specific resource retrieval agent for the provision of additional information from Internet sources matching the needs and profile of the specific user and III) a genetic algorithm designed to extract efficient information (classifying rules) based on the students- answering input data. The agents are implemented in order to provide intelligent assessment services based on computational intelligence techniques such as Bayesian Networks and Genetic Algorithms. The proposed Genetic Algorithm (GA) is used in order to extract efficient information (classifying rules) based on the students- answering input data. The idea of using a GA in order to fulfil this difficult task came from the fact that GAs have been widely used in applications including classification of unknown data. The utilization of new and emerging technologies like web services allows integrating the provided services to any web based legacy e-learning environment.

A Study of Color Transformation on Website Images for the Color Blind

Year: 2010 Volume: 4 Issue: 2 300 - 303 Pages

Abstract: In this paper, we study on color transformation method on website images for the color blind. The most common category of color blindness is red-green color blindness which is viewed as beige color. By transforming the colors of the images, the color blind can improve their color visibility. They can have a better view when browsing through the websites. To transform colors on the website images, we study on two algorithms which are the conversion techniques from RGB color space to HSV color space and self-organizing color transformation. The comparative study focuses on criteria based on the ease of use, quality, accuracy and efficiency. The outcome of the study leads to enhancement of website images to meet the color blinds- vision requirements in perceiving image detailed.

Word Stemming Algorithms and Retrieval Effectiveness in Malay and Arabic Documents Retrieval Systems

Year: 2007 Volume: 1 Issue: 10 3197 - 3199 Pages

Authors:
Tengku Mohd T. Sembok

Abstract: Documents retrieval in Information Retrieval Systems (IRS) is generally about understanding of information in the documents concern. The more the system able to understand the contents of documents the more effective will be the retrieval outcomes. But understanding of the contents is a very complex task. Conventional IRS apply algorithms that can only approximate the meaning of document contents through keywords approach using vector space model. Keywords may be unstemmed or stemmed. When keywords are stemmed and conflated in retrieving process, we are a step forwards in applying semantic technology in IRS. Word stemming is a process in morphological analysis under natural language processing, before syntactic and semantic analysis. We have developed algorithms for Malay and Arabic and incorporated stemming in our experimental systems in order to measure retrieval effectiveness. The results have shown that the retrieval effectiveness has increased when stemming is used in the systems.

Integrating Context Priors into a Decision Tree Classification Scheme

Year: 2009 Volume: 3 Issue: 12 2866 - 2873 Pages

Abstract: Scene interpretation systems need to match (often ambiguous) low-level input data to concepts from a high-level ontology. In many domains, these decisions are uncertain and benefit greatly from proper context. This paper demonstrates the use of decision trees for estimating class probabilities for regions described by feature vectors, and shows how context can be introduced in order to improve the matching performance.

Prediction of Reusability of Object Oriented Software Systems using Clustering Approach

Year: 2010 Volume: 4 Issue: 7 1175 - 1178 Pages

Abstract: In literature, there are metrics for identifying the quality of reusable components but the framework that makes use of these metrics to precisely predict reusability of software components is still need to be worked out. These reusability metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the software component and hence improve the productivity due to probabilistic increase in the reuse level. As CK metric suit is most widely used metrics for extraction of structural features of an object oriented (OO) software; So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO and LCOM, is used to obtain the structural analysis of OO-based software components. An algorithm has been proposed in which the inputs can be given to K-Means Clustering system in form of tuned values of the OO software component and decision tree is formed for the 10-fold cross validation of data to evaluate the in terms of linguistic reusability value of the component. The developed reusability model has produced high precision results as desired.

The Labeled Classification and its Application

Year: 2008 Volume: 2 Issue: 6 2073 - 2082 Pages

Abstract: This paper presents and evaluates a new classification method that aims to improve classifiers performances and speed up their training process. The proposed approach, called labeled classification, seeks to improve convergence of the BP (Back propagation) algorithm through the addition of an extra feature (labels) to all training examples. To classify every new example, tests will be carried out each label. The simplicity of implementation is the main advantage of this approach because no modifications are required in the training algorithms. Therefore, it can be used with others techniques of acceleration and stabilization. In this work, two models of the labeled classification are proposed: the LMLP (Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro Fuzzy Classifier). These models are tested using Iris, wine, texture and human thigh databases to evaluate their performances.

A Sequential Pattern Mining Method Based On Sequential Interestingness

Year: 2008 Volume: 2 Issue: 11 3862 - 3870 Pages

Abstract: Sequential mining methods efficiently discover all frequent sequential patterns included in sequential data. These methods use the support, which is the previous criterion that satisfies the Apriori property, to evaluate the frequency. However, the discovered patterns do not always correspond to the interests of analysts, because the patterns are common and the analysts cannot get new knowledge from the patterns. The paper proposes a new criterion, namely, the sequential interestingness, to discover sequential patterns that are more attractive for the analysts. The paper shows that the criterion satisfies the Apriori property and how the criterion is related to the support. Also, the paper proposes an efficient sequential mining method based on the proposed criterion. Lastly, the paper shows the effectiveness of the proposed method by applying the method to two kinds of sequential data.

Vision Based Hand Gesture Recognition

Year: 2009 Volume: 3 Issue: 1 114 - 119 Pages

Abstract: With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands as an input device is an attractive method for providing natural Human Computer Interaction which has evolved from text-based interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to fully fledged multi-participant Virtual Environment (VE) systems. Imagine the human-computer interaction of the future: A 3Dapplication where you can move and rotate objects simply by moving and rotating your hand - all without touching any input device. In this paper a review of vision based hand gesture recognition is presented. The existing approaches are categorized into 3D model based approaches and appearance based approaches, highlighting their advantages and shortcomings and identifying the open issues.

Sparse Networks-Based Speedup Technique for Proteins Betweenness Centrality Computation

Year: 2009 Volume: 3 Issue: 4 1134 - 1141 Pages

Abstract: The study of proteomics reached unexpected levels of interest, as a direct consequence of its discovered influence over some complex biological phenomena, such as problematic diseases like cancer. This paper presents the latest authors- achievements regarding the analysis of the networks of proteins (interactome networks), by computing more efficiently the betweenness centrality measure. The paper introduces the concept of betweenness centrality, and then describes how betweenness computation can help the interactome net- work analysis. Current sequential implementations for the between- ness computation do not perform satisfactory in terms of execution times. The paper-s main contribution is centered towards introducing a speedup technique for the betweenness computation, based on modified shortest path algorithms for sparse graphs. Three optimized generic algorithms for betweenness computation are described and implemented, and their performance tested against real biological data, which is part of the IntAct dataset.

Data Mining Applied to the Predictive Model of Triage System in Emergency Department

Year: 2013 Volume: 7 Issue: 6 800 - 807 Pages

Abstract: The Emergency Department of a medical center in Taiwan cooperated to conduct the research. A predictive model of triage system is contracted from the contract procedure, selection of parameters to sample screening. 2,000 pieces of data needed for the patients is chosen randomly by the computer. After three categorizations of data mining (Multi-group Discriminant Analysis, Multinomial Logistic Regression, Back-propagation Neural Networks), it is found that Back-propagation Neural Networks can best distinguish the patients- extent of emergency, and the accuracy rate can reach to as high as 95.1%. The Back-propagation Neural Networks that has the highest accuracy rate is simulated into the triage acuity expert system in this research. Data mining applied to the predictive model of the triage acuity expert system can be updated regularly for both the improvement of the system and for education training, and will not be affected by subjective factors.

Efficient Copy-Move Forgery Detection for Digital Images

Year: 2012 Volume: 6 Issue: 11 1403 - 1406 Pages

Abstract: Due to availability of powerful image processing software and improvement of human computer knowledge, it becomes easy to tamper images. Manipulation of digital images in different fields like court of law and medical imaging create a serious problem nowadays. Copy-move forgery is one of the most common types of forgery which copies some part of the image and pastes it to another part of the same image to cover an important scene. In this paper, a copy-move forgery detection method proposed based on Fourier transform to detect forgeries. Firstly, image is divided to same size blocks and Fourier transform is performed on each block. Similarity in the Fourier transform between different blocks provides an indication of the copy-move operation. The experimental results prove that the proposed method works on reasonable time and works well for gray scale and colour images. Computational complexity reduced by using Fourier transform in this method.

A Graphical Environment for Petri Nets INA Tool Based on Meta-Modelling and Graph Grammars

Year: 2008 Volume: 2 Issue: 8 2743 - 2747 Pages

Abstract: The Petri net tool INA is a well known tool by the Petri net community. However, it lacks a graphical environment to cerate and analyse INA models. Building a modelling tool for the design and analysis from scratch (for INA tool for example) is generally a prohibitive task. Meta-Modelling approach is useful to deal with such problems since it allows the modelling of the formalisms themselves. In this paper, we propose an approach based on the combined use of Meta-modelling and Graph Grammars to automatically generate a visual modelling tool for INA for analysis purposes. In our approach, the UML Class diagram formalism is used to define a meta-model of INA models. The meta-modelling tool ATOM3 is used to generate a visual modelling tool according to the proposed INA meta-model. We have also proposed a graph grammar to automatically generate INA description of the graphically specified Petri net models. This allows the user to avoid the errors when this description is done manually. Then the INA tool is used to perform the simulation and the analysis of the resulted INA description. Our environment is illustrated through an example.

UML Modeling for Instruction Pipeline Design

Year: 2008 Volume: 2 Issue: 6 2069 - 2072 Pages

Abstract: Unified Modeling language (UML) is one of the important modeling languages used for the visual representation of the research problem. In the present paper, UML model is designed for the Instruction pipeline which is used for the evaluation of the instructions of software programs. The class and sequence diagrams are designed & performance is evaluated for instructions of a sample program through a case study.

LOD Exploitation and Fast Silhouette Detection for Shadow Volumes

Year: 2008 Volume: 2 Issue: 2 514 - 520 Pages

Abstract: Shadows add great amount of realism to a scene and many algorithms exists to generate shadows. Recently, Shadow volumes (SVs) have made great achievements to place a valuable position in the gaming industries. Looking at this, we concentrate on simple but valuable initial partial steps for further optimization in SV generation, i.e.; model simplification and silhouette edge detection and tracking. Shadow volumes (SVs) usually takes time in generating boundary silhouettes of the object and if the object is complex then the generation of edges become much harder and slower in process. The challenge gets stiffer when real time shadow generation and rendering is demanded. We investigated a way to use the real time silhouette edge detection method, which takes the advantage of spatial and temporal coherence, and exploit the level-of-details (LOD) technique for reducing silhouette edges of the model to use the simplified version of the model for shadow generation speeding up the running time. These steps highly reduce the execution time of shadow volume generations in real-time and are easily flexible to any of the recently proposed SV techniques. Our main focus is to exploit the LOD and silhouette edge detection technique, adopting them to further enhance the shadow volume generations for real time rendering.

Conflicts Identification among Non-functional Requirements using Matrix Maps

Year: 2010 Volume: 4 Issue: 8 1287 - 1292 Pages

Authors:
Abdul H
Jamil A
Imran U

Abstract: Conflicts identification among non-functional requirements is often identified intuitively which impairs conflict analysis practices. This paper proposes a new model to identify conflicts among non-functional requirements. The proposed model uses the matrix mechanism to identify the quality based conflicts among non-functional requirements. The potential conflicts are identified through the mapping of low level conflicting quality attributes to low level functionalities using the matrices. The proposed model achieves the identification of conflicts among product and process requirements, identifies false conflicts, decreases the documentation overhead, and maintains transparency of identified conflicts. The attributes are not concomitantly taken into account by current models in practice.

A Phenomic Algorithm for Reconstruction of Gene Networks

Year: 2007 Volume: 1 Issue: 7 2128 - 2133 Pages

Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.