Search Results - (( java application system algorithm ) OR ( ((using factor) OR (using vector)) process algorithm ))

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  1. 1

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

    Published 2016
    “…An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. …”
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    Thesis
  2. 2

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…Using that process, we have settled on Java as our development platform as we found it able to both implement DA and the GIS component (map display and manipulation). …”
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    Final Year Project
  3. 3

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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    Thesis
  4. 4

    A Test Vector Minimization Algorithm Based On Delta Debugging For Post-Silicon Validation Of Pcie Rootport by Toh , Yi Feng

    Published 2017
    “…Test results using test vector sets containing deliberately introduced erroneous test vectors show that the minimizer is able to isolate the erroneous test vectors. …”
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    Thesis
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    Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif by Jantan, Hamidah, Mat Yusof, Norazmah, Abdul Latif, Mohd Hanapi

    Published 2014
    “…Due to that reason, this study attempts to use SVM algorithm on employee’s performance databases for talent classification. …”
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    Research Reports
  7. 7

    Random forest algorithm for co2 water alternating gas incremental recovery factor prediction by Belazreg, L., Mahmood, S.M., Aulia, A.

    Published 2020
    “…The aim of this paper is using an ensemble machine learning algorithm to develop a WAG incremental recovery factor predictive model that can be used by reservoir engineers to estimate WAG incremental recovery factor prior kick-off of laboratory experiments and comprehensive technical studies. …”
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    Article
  8. 8

    Design of intelligent Qira’at identification algorithm by Kamarudin, Noraziahtulhidayu

    Published 2017
    “…For the feature vectors that are collected from feature extraction (MFCC) and feature selection (X-ACO), the feature vectors are used as input for the classification phase. …”
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    Thesis
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  10. 10

    Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China by Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.

    Published 2024
    “…Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. …”
    Article
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    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. …”
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    Book Chapter
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    Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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    Conference or Workshop Item
  15. 15

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”
    Article
  16. 16

    Incremental continuous ant colony optimization for tuning support vector machine’s parameters by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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    Article
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    Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

    Published 2024
    “…Central to this process is the representation of words through vectors for computational interpretation. …”
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    Thesis
  19. 19

    Real-time algorithmic music composition application. by Yap, Alisa Yi Hui

    Published 2022
    “…In addition, the system also utilises JavaFx and jFugue for its graphical user interface and music programming respectively. …”
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    Final Year Project / Dissertation / Thesis
  20. 20

    An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

    Published 2012
    “…This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
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    Thesis