Big data clustering using grid computing and ant-based algorithm

Big data has the power to dramatically change the way institutes and organizations use their data. Transforming the massive amounts of data into knowledge will leverage the organizations performance to the maximum.Scientific and business organizations would benefit from utilizing big data. However, there are many challenges in dealing with big data such as storage, transfer, management and manipulation of big data.Many techniques are required to explore the hidden pattern inside the big data which have limitations in terms of hardware and software implementation. This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm

Fuzzy multi criteria evaluation for performance of bus companies

A multi criteria decision making in ranking the bus companies using fuzzy rule is proposed. The proposed method uses the application of fuzzy sets and approximate reasoning in deciding the ranking of the performance of several bus companies. The proposed method introduces data normalization using similarity function which dampens extreme values that exist in the data. The use of the model is suitable in evaluating situation that involves subjectivity, vagueness and imprecise information. Experimental results are comparable to several previous methods

Analysis and Decentralised Optimal Flow Control of Heterogeneous Computer Communication Network Models

General closed queueing networks are used to model the local flow control in multiclass computer communication networks with single and multiple transmission links. The problem of analysing multiclass general closed queueing network models with single server and multiserver is presented followed by the problem of decentralised optimal local flow control of multiclass general computer communication networks with single and multiple transmission links. The generalised exponential (GE) distributional model with known first two moments has been used to represent general interarrival and transmission time distributions as various users have various traffic characteristics. A new method of general model reduction using the Norton' s theorem for general queueing networks in conjunction with the universal maximum entropy algorithm is proposed for the analysis of large general closed queueing networks. This extension to Norton's theorem has an advantage over the direct application of the universal maximum entropy approach whereby the study of a subset of queueing centres of interest can be done without repeatedly solving the entire network. The principle of maximum entropy is used to derive new approximate solutions for the joint queue length distributions of multiclass general queueing network models with single server and multiserver and favourable comparisons with other methods are made. The decentralised optimal local flow control of the multiclass computer communication networks with single and multiple transmission links is shown to be a state dependent window type mechanism that has been traditionally used in practice

Fuzzy subjective evaluation of Asia Pacific airport services

This paper presents a fuzzy decision-making model to determine the ranking of fourteen Asia Pacific airports based on the services provided to passengers. Airport services were represented by six attributes namely comfort, processing time, convenience, courtesy of staff, information visibility and security. Data for the attributes given by travel experts are in the triangular fuzzy number form. Based on fuzzy set and approximate reasoning, the model allows decision makers to make the best choice in accordance with human thinking and reasoning processes.The use of fuzzy rules which are extracted directly from the input data in making evaluation, contributes to a better decision and is less dependent on experts.Experimental results show that the proposed model is comparable to previous studies.The model is suitable for various fuzzy environments

Prediction accuracy measurements for ensemble classifier

Multiple classifier combination (or ensemble method) has been shown to be very helpful in improving the performance of classification over single classifier approach. The diversity among base classifiers (or ensemble members) is important when constructing a classifier ensemble.Although there have been several measures of diversity, but there is no reliable measure that can predict the ensemble accuracy. The base classifiers accuracy will increase when the diversity decreases and this is known as the accuracy-diversity dilemma.This paper presents a new method to measure diversity in classifier ensembles.Furthermore another parameter which based on this diversity measure is defined.It is hope that the new parameter will be able to predict the ensemble accuracy.Based on experimental results on classification of 84 samples of fruit images using nearest mean classifier ensembles, it has been shown that there is a positive linear relationship between the new parameter and the ensemble accuracy.This parameter is expected to assist in constructing diverse and accurate ensemble

Knowledge acquisition and dissemination for emergency situation

Emergency situation is highly uncertain, dynamic, time pressure in making decisions and involves multi organizations and multi jurisdiction level. This paper presents a conceptual architecture that can be used by emergency response task force in assisting the victims of the disaster. Flood disaster is used as a case study. The architecture describes the knowledge and communication for flood emergency response management

Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction

Combination of several classifiers has been very useful in improving the prediction accuracy and in most situations multiple classifiers perform better than single classifier.However not all combining approaches are successful at producing multiple classifiers with good classification accuracy because there is no standard resolution in constructing diverse and accurate classifier ensemble.This paper proposes ant system-based feature set partitioning algorithm in constructing k-nearest neighbor (k-NN) and linear discriminant analysis (LDA) ensembles. Experiments were performed on several University California, Irvine datasets to test the performance of the proposed algorithm.Experimental results showed that the proposed algorithm has successfully constructed better classifier ensemble for k-NN and LDA

Combined nearest mean classifiers for multiple feature classification

Pattern classification is an important stage in many image processing applications. This paper proposes a technique that is based on nearest mean classifier for classification.The proposed technique integrates three classifiers and uses colour and shape features. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classiffiers and results obtained showed that the technique was able to provide good accurac

Searching Malay text using stemming algorithm

Stemming is an important process to improve performance of a search engine by reducing the variant word forms to common forms. This paper evaluates the retrieval effectiveness of stemming algorithm in searching and retrieving relevant Malay web pages based on user natural query words. The retrieved web pages are weighted and ranked using inverse document frequency function. The retrieval effectiveness is measured using standard recall and precision. Experiments performed show that searching with stemming improves retrieval effectiveness when compared to searching without stemming algorithm

Multi objective bee colony optimization framework for grid job scheduling

Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources