Search Results - (( evolution detection method algorithm ) OR ( parameter interactive learning algorithm ))

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  1. 1

    A Detection Method for Text Steganalysis Using Evolution Algorithm (EA) Approach by Puriwat, Lertkrai

    Published 2012
    “…Therefore, this research employed a detection factor based on the evolution algorithm method for text steganalysis. …”
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    Thesis
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    Fitness value based evolution algorithm approach for text steganalysis model by Din, Roshidi, Samsudin, Azman, Tuan Muda, Tuan Zalizam, Lertkrai, P., Amphawan, Angela, Omar, Mohd Nizam

    Published 2013
    “…In this paper, we present a new alternative method for text steganalysis based on an evolution algorithm, implemented using the Java Evolution Algorithms Package (JEAP).The main objective of this paper is to detect the existence of hidden messages ased on fitness values of a text description.It is found that the detection performance has been influenced by two groups of fitness values which are good fitness value and bad fitness value. …”
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    Article
  3. 3

    Text steganalysis using evolution algorithm approach by Din, Roshidi, Tuan Muda, Tuan Zalizam, Lertkrai, Puriwat, Omar, Mohd Nizam, Amphawan, Angela, Aziz, Fakhrul Anuar

    Published 2012
    “…This study presents a new alternative of steganalysis method in order to detect hidden messages in text steganalysis called Evolution Detection Steganalysis System (EDSS) based on the evolution algorithm approach under Java Genetic Algorithms Package (JGAP). …”
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    Conference or Workshop Item
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    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

    Published 2019
    “…Non detection zone decreases to around zero and the proposed method has the ability to detect islanding up to 0.1% power mismatch. …”
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    Thesis
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    A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising by Al-Dabbagh, Mohanad Dawood, Al-Dabbagh, Rawaa Dawoud, Raja Abdullah, Raja Syamsul Azmir, Hashim, Fazirulhisyam

    Published 2015
    “…A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. …”
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    Article
  6. 6

    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

    Published 2015
    “…In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.…”
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    Article
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    Differential evolution algorithm for linear frequency modulation radar signal denoising by Al-Dabbagh, Mohanad Dawood Hasan, Raja Abdullah, Raja Syamsul Azmir, Al-Dabbagh, Rawaa Dawoud Hassan, Hashim, Fazirulhisyam

    Published 2013
    “…These factors influence the output signal voltage, thus creating detectable noise. Differential Evolution (DE) is an effectual, competent, and robust optimisation method used to solve different problems in the engineering and scientific domains, such as in signal processing. …”
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    Conference or Workshop Item
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    Interaction effect of process parameters and Pd-electrocatalyst in formic acid electro-oxidation for fuel cell applications: Implementing supervised machine learning algorithms by Hossain S.K.S., Ali S.S., Rushd S., Ayodele B.V., Cheng C.K.

    Published 2023
    “…Carbon nanotubes; Electrocatalysts; Electrooxidation; Forestry; Formic acid; Gaussian distribution; Learning algorithms; Palladium; Parameter estimation; Regression analysis; Support vector machines; Formic acid electrooxidation; Fuel cell application; Gaussian kernel functions; Gaussian process regression; Interaction effect; Machine learning algorithms; Performance; Process parameters; Regression trees; Support vector machine regressions; Sensitivity analysis…”
    Article
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    Development of an interactive learning tool for analysis of fast fourier transform from butterfly diagram by Mohamad Syeihri Bin Mohamad Ismail

    Published 2023
    “…This project is to develop an interactive learning tool for the analysis of Fast Fourier Transform from the butterfly diagram. …”
    text::Thesis
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    Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization by Yahya, Zainor Ridzuan

    Published 2013
    “…Three soft computing techniques namely Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) are utilized for the desired manipulation of curves and surfaces. …”
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    Thesis
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    Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining by Sivarao, Subramonian

    Published 2009
    “…The researchers conducted the prediction of laser machining quality, namely surface roughness with seven significant parameters to obtain singleton output using machine learning techniques based on Quick Back Propagation Algorithm. …”
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    Book Chapter
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    Optimized differential evolution algorithm for linear frequency modulation radar signal denoising by Al-Dabbagh, Mohanad Dawood Hasan

    Published 2013
    “…An improvement to the conventional DE algorithm has been made to change it from its classical form to be possibly applied for ambiguous targets range detection for radar system. …”
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    Thesis
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    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.…”
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    Article
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    Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid by Wahid, Muhammad Hazim

    Published 2022
    “…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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    Thesis
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