Search Results - (( ((using factor) OR (using vector)) problem algorithm ) OR ( java application path algorithm ))

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  1. 1

    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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    Vector Evaluated Gravitational Search Algorithm (VEGSA) for multi-objective optimization problems by Zuwairie, Ibrahim, Badaruddin, Muhammad, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Sophan Wahyudi, Nawawi, Zulkifli, Md. Yusof

    Published 2012
    “…The proposed algorithm, which is called Vector Evaluated Gravitational Search Algorithm (VEGSA), uses a number of populations of particles. …”
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    Conference or Workshop Item
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    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…In creating this application, NetBeans IDE 6.5and Java Micro Edition (Java ME) are used. …”
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    Thesis
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    A Method For Solving Mult-Objective Optimization Problem: Vector Evaluated Genetic Algorithm (Vega) by Tan, Tun Tai

    Published 2009
    “…However, it is impractical to solve MOOP by using classical methods due to its complexity. Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. …”
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    Final Year Project Report / IMRAD
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    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

    Published 2014
    “…The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. …”
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    Thesis
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    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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    Article
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    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. …”
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    Thesis
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    Convergence and Diversity Evaluation for Vector Evaluated Particle Swarm Optimization by Mohd Zaidi, Mohd Tumari, Zuwairie, Ibrahim, Ismail, Ibrahim, Mohd Falfazli, Mat Jusof, Faradila, Naim, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Salinda, Buyamin, Anita, Ahmad

    Published 2013
    “…An extended PSO algorithm called Vector Evaluated Particle Swarm Optimization (VEPSO) has been introduced to solve multi-objective optimization problems. …”
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    Conference or Workshop Item
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    Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Ant Colony Optimization has been used to solve Support Vector Machine model selection problem.Ant Colony Optimization originally deals with discrete optimization problem.In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value.This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time.This study proposes an algorithm that can optimize Support Vector Machine parameters using Continuous Ant Colony Optimization without the need to discretize continuous value for Support Vector Machine parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.…”
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    Article
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    Optimization of least squares support vector machine technique using genetic algorithm for electroencephalogram multi-dimensional signals by Ahmad, Farzana Kabir, Al-Qammaz, Abdullah Yousef Awwad, Yusof, Yuhanis

    Published 2016
    “…Discrete wavelet packet transform (DWPT) has been used to extract EEG signals feature and ultimately 204,800 features from 32 subject-independent have been obtained. …”
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    Article
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    Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data by Joshua, Ibidoja Olayemi

    Published 2023
    “…Ensemble machine learning algorithms such as stacking, XGBoost, and AdaBoost can be used.…”
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    Thesis
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    Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

    Published 2025
    “…The system architecture integrates ROS 2 on a Raspberry Pi, with TCP/IP connectivity enabling remote operation. An Android mobile application, developed using Java and the java.net.Socket library, provides an intuitive and accessible user interface for seamless interaction with the robot. …”
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    Final Year Project / Dissertation / Thesis
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    Early prediction of acute kidney injury using machine learning algorithms by Ismail, Amelia Ritahani, Abdul Aziz, Normaziah, Dzaharudin, Fatimah, Mat Ralib, Azrina, Md Nor, Norzaliza, Yahya, Norzariyah

    Published 2018
    “…The ML algorithms are Support Vector Machine (SVM), Neural Network (NN), Deep learning, Decision trees and Naiive Bayes. …”
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    Proceeding Paper