Search Results - (( using normalization path algorithm ) OR ( parameter classification using algorithm ))

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  1. 1

    Modelling of multi-robot system for search and rescue by Poy, Yi Ler

    Published 2023
    “…It was found that the number of populations set to 75 and dynamically adjusts the value of inertial weight, the cognitive and social parameter provides the best performance in terms of shortest path length and execution time. In conclusion, this project shows that the MPSO algorithm is capable of generating a better path compared to the normal PSO algorithm in terms of average path length and execution time, making it a promising algorithm for multi-robot path planning in dynamic environments.…”
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    Final Year Project / Dissertation / Thesis
  2. 2

    Design and analysis of sequence generator module using eulerian path algorithm for DNA fragment assembly / Mustaqim Mohd Subri by Mohd Subri, Mustaqim

    Published 2013
    “…This project is to design and analysis the sequence generator module using Eulerian Path algorithm for DNA fragment assembly. …”
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    Student Project
  3. 3

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. …”
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    Thesis
  4. 4

    Optimal path planning algorithms in virtual environments by Hassan, Rohayanti

    Published 2006
    “…In relation to that, A* algorithm was used as a path finding technique to plan a collision-free-path journey from one location to another. …”
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    Thesis
  5. 5

    Design and analysis of sequence generator module using eulerian path algorithm for DNA fragment assembly: article / Mustaqim Mohd Subri by Mohd Subri, Mustaqim

    Published 2013
    “…Euler Path Algorithm is used since it has better assembling ability especially in assembling long sequence. …”
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    Article
  6. 6

    Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…Common methods associated in tuning SVM parameters will discretize the continuous value of these parameters which will result in low classification performance. …”
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    Article
  7. 7

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

    Published 2013
    “…Support Vector Machines are considered to be excellent patterns classification techniques. 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
  8. 8

    Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak by Dak, Ahmad Yusri

    Published 2019
    “…The fourth stage is to design evaluation methodology of Max-Min Rule-Based Classification Algorithm using classifier model. Finally, both algorithms are validated against the findings in various literatures. …”
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    Thesis
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    GPS boundary navigation of DrosoBots using MATLAB simulation by Zainal Abidin, Zulkifli, Ngah , Vmi Kalthum, Arshad, Mohd Rizal, Kok , Chee Hou

    Published 2010
    “…To implement this, region recognition and several path planning algorithms have been utilized. Besides that, pixilation of path is also used to estimate the coverage of the path planning. …”
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    Proceeding Paper
  13. 13

    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
  14. 14

    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. The value of parameter is already set to use when applying every dataset in an experiment. …”
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    Undergraduates Project Papers
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    Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

    Published 2023
    “…Genetic Algorithm (GA) is used to search for the best parameter of SVM classification by using combination of random and pre-populated genomes from Pre-Populated Database (PPD). …”
    Conference Paper
  18. 18

    Restaurant locator using Djikstra Algorithm / Mohamad Aliff Hakimi Lukman Hakim by Lukman Hakim, Mohamad Aliff Hakimi

    Published 2017
    “…In network theory, it’s known as the shortest-path problem, and Dijkstra’s algorithm is usually used to solve it. …”
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    Thesis
  19. 19

    Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor by Intan Noradybah Md Rodi

    Published 2019
    “…Thehighest accuracy for classification map of Gunung Basor is by using maximum likelihood algorithm with an accuracy of 82.90%. …”
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    Undergraduate Final Project Report
  20. 20

    Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters by Teguh, Sutanto, Muhammad Rafli, Aditya, Haldi, Budiman, M.Rezqy, Noor Ridha, Usman, Syapotro, Noor, Azijah

    Published 2024
    “…This study compares four machine learning algorithms Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in water quality classification based on contaminant parameters. …”
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    Article