Mohd. Noor Md. Sap

Universiti Teknologi Malaysia, Bharu, Johor, Malaysia

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Publications (34)0 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Fault tolerant Grid scheduling is of vital importance in the Grid computing world. Task replication and checkpointing is two popular methods to achieve a fault tolerant scheduling. Replication method is not an applicable way in economic-based grid computing due to use a large number of resources. The cost of spent time must be paid by consumer for all participant nodes. In this paper, we proposed a fault-tolerant scheduling technique based on Multi-Checkpointing by using rough set theory for economic-based grid with respect to minimum cost, high efficiency, and minimum latency. In our proposed approach, we assume that if one of the provider nodes is failed, there is not enough time to start a task on a new node from beginning again. The experimental results show a promising method with less computation cost price and better fault-tolerance in acceptable completion time.
    Networked Digital Technologies, 2009. NDT '09. First International Conference on; 08/2009
  • E. Mohebi, M.N.M. Sap
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    ABSTRACT: The Kohonen self organizing map is widely used as a popular tool in the exploratory phase of data mining. The SOM (self organizing maps) maps high dimensional space into a 2-dimensional grid by placing similar elements close together, forming clusters. Recently research experiments presented that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the uncertainty, an optimized clustering algorithm based on SOM which employs the rough set theory and the simulated annealing as a general technique for optimization problems is proposed. The optimized two-level stage SA-Rough SOM (simulated annealing - rough self organizing map) (first using SOM to produce the prototypes that are then clustered in the second stage based on the combination of rough set and simulated annealing) is found to perform well and more accurate compared with the crisp clustering methods (i.e. Incremental SOM) and reduces the errors.
    Computer Modelling and Simulation, 2009. UKSIM '09. 11th International Conference on; 04/2009
  • E. Mohebi, M. N. M. Sap
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    ABSTRACT: The Kohonen self organizing map is an excellent tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the uncertainty, a two-level clustering algorithm based on SOM which employs the rough set theory is proposed. The two-level stage Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well and more accurate compared with the proposed crisp clustering method (Incremental SOM) and reduces the errors.
    02/2009: pages 183-196;
  • Ehsan Mohebi, M. N. M. Sap
    Enterprise Information Systems, 11th International Conference, ICEIS 2009, Milan, Italy, May 6-10, 2009. Proceedings; 01/2009
  • Ehsan Mohebi, M. N. M. Sap
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    ABSTRACT: The Kohonen Self Organizing Map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered. In this paper a two-level clustering based on SOM is proposed, which employs rough set theory to capture the inherent uncertainty involved in cluster analysis. The two-stage procedure (first using SOM to produce the prototypes that are then clustered in the second stage) is found to perform well when compared with crisp clustering of the data and increase the accuracy.
    First Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009, Dong hoi, Quang binh, Vietnam, April 1-3, 2009; 01/2009
  • Chaliaw Phetking, Mohd. Noor Md. Sap, Ali Selamat
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    ABSTRACT: Financial time series often exhibit high degrees of fluctuation which are considered as noise in time series analysis. To remove noise, several lower bounding the Euclidean distance based dimensionality reduction methods are applied. But, however, these methods do not meet the constraint of financial time series analysis that wants to retain the important points and remove others. Therefore, although a number of methods can retain the important points in the financial time series reduction, but, however, they loss the nature of financial time series which consist of several uptrends, downtrends and sideway trends in different resolutions and in the zigzag directions. In this paper, we propose the zigzag based perceptually important point identification method to collect those zigzag movement important points. Further, we propose zigzag based multiway search tree to index these important points. We evaluate our methods in time series dimensionality reduction. The results show the significant performance comparing to other original method.
    Proceedings of the 8th IEEE International Conference on Cognitive Informatics, ICCI 2009, June 15-17, 2009, Hong Kong, China; 01/2009
  • E. Mohebi, M. N. M. Sap
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    ABSTRACT: One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the ambiguity involved in cluster analysis, a combination of Rough set Theory and Simulated Annealing is proposed that has been applied on the output grid of SOM. Experiments show that the proposed two-stage algorithm, first using SOM to produce the prototypes then applying rough set and SA in the second stage in order to assign the overlapped data to true clusters they belong to, outperforms the proposed crisp clustering algorithms (i.e. I-SOM) and reduces the errors.
    12/2008: pages 389-401;
  • M.N.M. Sap, M. Kohram
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    ABSTRACT: Support vector machines (SVM) have been extensively used for classification purposes in a broad range of applications. These learning machines base their classification on the Euclidean distance of the data vectors or their dot products. These measures do not account for the spectral signature information that can be achieved from remote sensing images. Given the high value of this information, integrating it into the SVM algorithm is a reasonable suggestion. This paper utilizes the spectral angle (SA) function as a measure for classification of a hyperspectral image. The SA function is joined together with the radial basis function (RBF) to form a spectral angle based RBF function. Experimentation results are promising and confirm that this approach can compete with existing classification methods.
    Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on; 06/2008
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    C. Phetking, M.N. Md. Sap, A. Selamat
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    ABSTRACT: Mining financial time series data without ignoring its characteristics is very important. Financial time series data normally fluctuate unexpectedly which courses very high dimensions. The peak and the dip points of the series may appear frequently over time. These points are known as the most important points which reflect some related events to the market. However, to manipulate financial time series, researchers usually decrease this complexity of time series in their techniques. Consequently, transforming the time series into another easily understanding representation is usually considered as an appropriate approach. In this paper, we propose a multiresolution important point retrieval method for financial time series representation. The idea of the method is based on finding the most important points in multiresolution. These retrieved important points are recorded in each resolution. The collected important points are used to construct the TS-binary search tree. From the TS-binary search tree, the application of time series segmentation is conducted. The experimental results show that the TS-binary search tree representation for financial time series exhibits different performance in different number of cutting points, however, in the empirical results, the number of cutting points which are larger than 12 points show the better results.
    Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on; 06/2008
  • Siriluck Lorpunmanee, Mohd. Noor Md. Sap
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    ABSTRACT: This paper proposes the idea of adaptive job scheduling algorithm by using hybrid Ant Colony Optimization (ACO) and Tabu algorithms. The idea behind the scheduling algorithm is evaluation of completion time of jobs in a service Grid. The algorithm comprises of two main techniques; first of all, Grid Information Service (GIS) collects information from each grid node, ACO evaluates complete time of jobs in possible grid nodes and then assigns job to appropriate grid node. ACO is used to minimize the average completion time of jobs through optimal job allocation on each node as well. While, Tabu algorithm is used to adjust performance of grid system because online jobs are submitted to grid system from time to time. This paper shows that the algorithm can find an optimal processor for each machine to allocate to a job that minimizes the tardiness time of a job when the job is scheduled in the system.
    01/2008;
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    Mohd. Noor Md. Sap
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    ABSTRACT: A very important task in pattern recognition is the incorporation of prior information into the learning algorithm. In SUppOlt vector machines this task is performed via the kernel function. Thus for each application if the right kernel function is chosen, the amount of prior information fed into the machine is increased and thus the machine will perform with much more functionality. In the case of hyper-spectral imagery the amount of information available prior to classification is a vast amount. Current available kernels do not take full advantage of the amount of information available in these images. This paper focuses on deriving a set of kernels specific to these imagery. These kernels make use of the spectral signature available in images. Subsequently we use mixtures of these kernels to derive new and more efficient kernels for classification. Results show that these kernels do in fact improve classification accuracy and use the prior information available in imagery to a better degree.
    01/2008;
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    ABSTRACT: Rice, Oryza sativa, also called paddy rice, common rice, lowland and upland.rice. This food grain is produced at least 95 countries around the globe, with China producing 36% of the world's production in 1999, followed by India at 21%, Indonesia at 8%, Bangladesh and Vietnam each producing about 5%. The United States produced about 1.5% of the world's accounts for about 15% of the annual world exports of rice. However the Modern agriculture is influenced by both the pressure for increased productivity and increased stresses caused by plant pests. Geographical Information Systems and Global Positioning Systems are currently being used for variable rate application of pesticides, herbicide and fertilizers in Precision Agriculture applications, but the comparatively lesser­ used tools of Neural Network can be of additional value in integrated pest management practices. This study details spatial analysis and clustering using Neural Network based on Kohonen Self Organizing map (SOM) as applied to integrated agricultural rice pest management in Malaysia.
    01/2008;
  • Mojtaba Kohram, Mohd. Noor Md. Sap
    MICAI 2008: Advances in Artificial Intelligence, 7th Mexican International Conference on Artificial Intelligence, Atizapán de Zaragoza, Mexico, October 27-31, 2008, Proceedings; 01/2008
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    ABSTRACT: Grid computing is the principle in utilizing and sharing large-scale resources to solve complex scientific problems. Under this principle, Grid environment has problems in flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major problems include optimal job scheduling, and which grid nodes allocate the resources for each job. This paper proposes the model for optimizing jobs scheduling in Grid environment. The model presents the results of the simulation of the Grid environment of jobs allocation to different nodes. We develop the results of job characteristics to three classifications depending on jobs run time in machines, which have been obtained using the optimization of jobs scheduling. The results prove the model by using Fuzzy c-mean clustering technique for predicting the characterization of jobs and optimization of jobs scheduling in Grid environment. This prediction and optimization engine will provide jobs scheduling base upon historical information. This paper presents the need for such a prediction and optimization engine that discusses the approach for history-based prediction and optimization. Simulation runs demonstrate that our algorithm leads to better results than the traditional algorithms for scheduling policies used in Grid environment.
    J. J. Appl. Sci. 01/2007; 9.
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    Int. Arab J. Inf. Technol. 01/2007; 4:247-254.
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    ABSTRACT: It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods.
    Hybrid Information Technology, International Conference on. 11/2006; 1:329-334.
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    ABSTRACT: The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest.
    INDICON, 2005 Annual IEEE; 01/2006
  • IASTED International Conference on Artificial Intelligence and Applications, part of the 24th Multi-Conference on Applied Informatics, Innsbruck, Austria, February 13-16, 2006; 01/2006
  • A. Majid Awan, Mohd. Noor Md. Sap
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    ABSTRACT: This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatial constraints is presented. The algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis, and thus can be used for predicting oil-palm yield by analyzing various factors affecting the yield.
    Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings; 01/2006
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    ABSTRACT: Grid computing is the principle in utilizing and sharing large-scale resources to solve the complex scientific problem. Under this principle, Grid environment has problems in providing flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major problem is in optimal job scheduling, which Grid nodes need to allocate the resources for each job. This paper proposes the models for multi-objective jobs scheduling in Grid environment. The model presents the strategies of allocating jobs to different nodes. We develop the models based on multi-objective of genetic algorithm to select multiple optimization scheduling of the jobs. In this preliminary tests we show how the solution founded may minimize the average waiting time and the make-span time in Grid environment. The benefits of the usage of multiple objective genetic algorithm is improving the performance of the scheduling is discussed. The simulation has been obtained using historical information to study the job scheduling in Grid environment. The experimental results have shown that the scheduling system using the multiple objective genetic algorithms can allocate jobs efficiently and effectively.
    01/2006;