Search Results - (( java implementation mining algorithm ) OR ( using constructs learning algorithm ))

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  1. 1

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

    Published 2012
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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    Thesis
  2. 2

    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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    Article
  3. 3

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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    Conference or Workshop Item
  4. 4

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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    Conference or Workshop Item
  5. 5

    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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    Final Year Project / Dissertation / Thesis
  6. 6

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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    Thesis
  7. 7

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

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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    Thesis
  8. 8

    Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization by Kamal Zuhairi, Zamli, Din, Fakhrud, Alhadawi, Hussam S.

    Published 2023
    “…This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. …”
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    Article
  9. 9

    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules by Rizauddin, Saian

    Published 2013
    “…The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. …”
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    Thesis
  10. 10

    Effectiveness of algorithm visualisation in studying complex algorithms: a case study using TRAKLA Ravie / Chandren Muniyandi, Ali Maroosi by Muniyandi, Chandren, Maroosi, Ali

    Published 2015
    “…TRAKLA2 is a visualisation tool used to enhance the process of learning algorithm construction and optimization. …”
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    Article
  11. 11

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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    Thesis
  12. 12

    Development Of Construction Noise Prediction Method Using Deep Learning Model by Siew, Jun Teng

    Published 2021
    “…The performance of this model is believed to be superior to the majority of manual predictions using a simple prediction chart. It will eventually become mainstream of the construction noise prediction method and will also be used in industries other than construction.…”
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    Final Year Project / Dissertation / Thesis
  13. 13

    Construction Noise Prediction Using Stochastic Deep Learning by Ooi, Wei Chien

    Published 2022
    “…The programming algorithm of stochastic modelling was executed in MATLAB, whereas the deep learning model was established by using Python 3.6 programming language in Spyder. …”
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    Final Year Project / Dissertation / Thesis
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    An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

    Published 2015
    “…Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm.This research proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution(VLFCWS), in order to construct a set of relevant features in learning relational data. …”
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    Research Report
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    Sentiment analysis for airline services on Twitter using deep learning with word embedding / Mawada Mohamed Nour El Daim El Khalifa by Mawada Mohamed , Nour El Daim El Khalifa

    Published 2020
    “…The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. …”
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    Thesis
  19. 19

    A Novel Aggregate Classification Technique Using Moment Invariants and Cascaded Multilayered Perceptron Network by Md Sani, Zamani

    Published 2009
    “…The c-MLP network consists of three MLPs which are arranged in a serial combination and trained with the same learning algorithm. The proposed method has been tested and compared with twelve machine learning algorithms namely Levenberg-Marquardt (LM), Broyden-Fletcher-Goldfarb-Shanno quasi-newton (BFG), Resilient back propagation (RP), Scaled conjugate gradient (SCG), Conjugate gradient with Powell-Beale restarts (CGB), Conjugate gradient with Fletcher-Reeves updates (CGF), Conjugate gradient with Polak-Ribiere updates (CGP), One step secant (OSS), Bayesian regularization (BR), Gradient descent (GD), Gradient descent with momentum and adaptive learning rate (GDX) and Gradient descent with momentum (GDM) algorithms. …”
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    Article
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