Search Results - (( learning implementation new algorithm ) OR ( java data optimization algorithm ))

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  1. 1

    Fuzzy modeling using Bat Algorithm optimization for classification by Noor Amidah, Ahmad Sultan

    Published 2018
    “…A Sazonov Engine which is a fuzzy java engine is use to apply Bat Algorithm in the experiment. …”
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    Undergraduates Project Papers
  2. 2

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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    Article
  3. 3

    Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation by Mat Jani H., Lee S.P.

    Published 2023
    “…The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. …”
    Conference paper
  4. 4

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…As an alternative, having a self-learning algorithm embedded in an autonomous system allows the system to instinctively acquire new knowledge and learn from experience. …”
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    Thesis
  5. 5

    A random search based effective algorithm for pairwise test data generation by Sabira, Khatun, K. F., Rabbi, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2011
    “…This paper proposes an effective random search based pairwise test data generation algorithm named R2Way to optimize the number of test cases. …”
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    Conference or Workshop Item
  6. 6

    E4ML: Educational Tool for Machine Learning by Sainin, Mohd Shamrie, Siraj, Fadzilah

    Published 2003
    “…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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    Conference or Workshop Item
  7. 7

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…This algorithm has been tested and implemented successfully via a dual beam optical scanning system.…”
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    Thesis
  8. 8
  9. 9

    EasyA: Easy and effective way to generate pairwise test data by Rabbi, Khandakar Fazley, Sabira, Khatun, Che Yahaya, Yaakub, Klaib, Mohammad F. J.

    Published 2013
    “…This paper proposes a matrix based calculation for pairwise test data generation algorithm named EasyA to optimize the number of test cases. …”
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    Conference or Workshop Item
  10. 10

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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    Thesis
  11. 11

    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
  12. 12

    IMPLEMENTATION OF IMAGE TEXTURE ANALYSIS USING GRAY LEVEL RUN LENGTH APPROACH by MOHD YAKOP, SITI HAJAR

    Published 2006
    “…The objective of this project is to develop algorithms inMATLAB and be able to implement image texture analysis by using the developed algorithms. …”
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    Final Year Project
  13. 13

    Active force control with iterative learning control algorithm for a vehicle suspension by Rosmazi, Rosli

    Published 2013
    “…The new control scheme named active force control with iterative learning control algorithm (AFCIL) is complemented by the classic proportionalintegral-derivative (PID) control incorporated and designed as the outermost control loop. …”
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    Thesis
  14. 14
  15. 15

    A conceptual multi-agent framework using ant colony optimization and fuzzy algorithms for learning style detection by Basheer G.S., Ahmad M.S., Tang A.Y.C.

    Published 2023
    “…We propose a new dimension to detect learning styles, which involves the individuals of learners' social surrounding such as friends, parents, and teachers in developing a novel agent-based framework. …”
    Conference Paper
  16. 16

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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    Article
  17. 17

    Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media A., Abu Osman, Md. Tap

    Published 2011
    “…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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    Article
  18. 18

    A machine learning approach to tourism recommendations system by Chia, An

    Published 2025
    “…To overcome this problem, this project implements machine learning algorithms with collaborative filtering, content-based filtering and hybrid filtering approaches. …”
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    Final Year Project / Dissertation / Thesis
  19. 19

    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

    Published 2012
    “…Hence, this indicates that Invasive Weed Optimization could be implemented as a new learning algorithm for an Artificial Neural Network.…”
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    Student Project
  20. 20

    Computationally efficient sequential learning algorithms for direct link resource-allocating networks by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George W

    Published 2005
    “…Computationally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). …”
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    Article