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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
    “…Efforts are made to develop a new tool or method for extracting information from XML data directly without any preprocessing or post processing of the XML documents. Association rules express the probability of the existing of a set of items when another set of items exists. …”
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  2. 2
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  4. 4
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  5. 5
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  6. 6
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  7. 7
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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  8. 8
    Priority and dynamic quantum time algorithms for central processing unit scheduling

    Priority and dynamic quantum time algorithms for central processing unit scheduling by Mohammed, Maysoon A.

    Published 2018
    “…Central Processing Unit is scheduled using different types of scheduling algorithms. …”
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  9. 9
    Pre-processing of input features using LPC and warping process

    Pre-processing of input features using LPC and warping process by Sudirman, Rubita, Sh-Hussain, Salleh, Ming, Ting Chee

    Published 2005
    “…Another task is to align the input frames (test set) to the reference template (training set) using a modified DTW algorithm called DTW fixing frame (DTW-FF)algorithm. …”
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  10. 10
    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri

    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri by Mohd Sabri, Norlina

    Published 2020
    “…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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  11. 11
    Enhanced marking process (EMP) for constructing dominating set in mobile ad-hoc networks with unidirectional links

    Enhanced marking process (EMP) for constructing dominating set in mobile ad-hoc networks with unidirectional links by Waheed, Bassam M. S., Mohd Ali, Borhanuddin, Khatun, Sabira, Mohd Sidek, Roslina

    Published 2014
    “…An enhanced distributed algorithm is presented that is based on the marking process which is has been introduced in earlier work. …”
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  12. 12
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Besides, some real data sets were examined to validate the proposed algorithm. …”
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  13. 13
    Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim

    Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim by Che Ibrahim, Mohd Erman Safawie

    Published 2012
    “…The objectives of this project to set-up Beowulf cluster computer to apply the Travelling Salesman Problem in parallel by using Genetic Algorithms and evaluate sequential algorithms and parallel algorithms by Genetic Algorithms. …”
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  14. 14
    A filtering algorithm for efficient retrieving of DNA sequence

    A filtering algorithm for efficient retrieving of DNA sequence by Abdul Rahman, Mohd Nordin, Mohd. Saman, Md. Yazid, Ahmad, Aziz, Md. Tap, Abu Osman

    Published 2009
    “…The aim of this study is to develop a filtering algorithm in order to reduce the iteration of dynamic programming process and therefore an efficient process of retrieving a set of similar DNA sequences in database can be made. …”
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  15. 15
    Clustering chemical data set using particle swarm optimization based algorithm

    Clustering chemical data set using particle swarm optimization based algorithm by Triyono, Triyono

    Published 2008
    “…The biological activities of these compounds have a large significance in the process of designing and discovering new drugs. Many algorithms had been applied to cluster chemical data set such as Ward’s algorithm. …”
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  16. 16
    Warpage analysis on thick shell part using response surface methodology (RSM) and bat algorithm (BA) to optimize parameter setting in injection molding process

    Warpage analysis on thick shell part using response surface methodology (RSM) and bat algorithm (BA) to optimize parameter setting in injection molding process by Ch'Ng, S. Q., Nasir, S. M., Fathullah, M., Noriman, N. Z., Mohd Sazli, Saad

    Published 2018
    “…BA will optimize the process parameters setting and improve warpage value based on the results in AMI. …”
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  17. 17
    Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm

    Optimization of micro-end milling process parameters of titanium alloy using non-dominated sorting genetic algorithm by Golshan, Abolfazl

    Published 2013
    “…The optimization results demonstrate the high performance of this method to obtain the Pareto optimal set of solutions in the micro-end milling process. …”
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  18. 18
    Dynamical fuzzy autocatalytic set of combustion process in circulating fluidized bed boiler using transition matrix / Hazwani Hashim

    Dynamical fuzzy autocatalytic set of combustion process in circulating fluidized bed boiler using transition matrix / Hazwani Hashim by Hashim, Hazwani

    Published 2014
    “…However the result reveals that by-product of the process is not in accordance to the real process. …”
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  19. 19
    DTWFF-pitch feature and faster neural network convergence for speech recognition

    DTWFF-pitch feature and faster neural network convergence for speech recognition by Sudirman, Rubita, Salleh, Sh. Hussain, Salleh, Shaharuddin

    Published 2007
    “…The first task is to extract pitch features using Pitch Scale Harmonic Filter algorithm. Another task is to align the input frames (test set) to the reference template (training set) using DTW fixing frame (DTW-FF) algorithm. …”
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  20. 20
    Efficient management of Top-k queries over Uncertain Data Streams with dynamic Sliding Window Model

    Efficient management of Top-k queries over Uncertain Data Streams with dynamic Sliding Window Model by Raja Wahab, Raja Azhan Syah

    Published 2024
    “…The large amount of data generated and frequent changes in a short time make conventional processing methods insufficient. The main issues are minimizing redundant scans of the whole data set, improving uncertainty computation, and only processing the most recent tuple items. …”
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