Search Results - (( using combination learning algorithm ) OR ( using evolution process algorithm ))

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  1. 1

    An improved method using fuzzy system based on hybrid boahs for phishing attack detection by Noor Syahirah, Nordin

    Published 2022
    “…The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. …”
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  2. 2

    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
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  3. 3

    Nature-Inspired cognitive evolution to play Ms. Pac-Man by Tse, Guan Tan, Jason Teo, Patricia Anthony

    Published 2011
    “…The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. …”
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  4. 4

    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
    “…In order to train Ridgelet probabilistic neural network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is introduced. …”
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  5. 5

    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

    Published 2012
    “…The optimal combination of λ and training sequence can be computed efficiently using a genetic permutation algorithm. …”
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  6. 6

    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

    Published 2018
    “…Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. …”
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  7. 7

    A novel approach based on machine learning and public engagement to predict water-scarcity risk in urban areas by Hanoon, Sadeq Khaleefah, Abdullah, Ahmad Fikri, M. Shafri, Helmi Z., Wayayok, Aimrun

    Published 2022
    “…The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. …”
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  8. 8

    Evolving fuzzy grammar for crime texts categorization by Mohd Sharef, Nurfadhlina, Martin, Trevor

    Published 2015
    “…Fuzzy union operator is also used to combine and transform individual text fragment grammars into more general representations of the learned text fragments. …”
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  9. 9

    A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

    Published 2011
    “…Among the latest Evaluation Algorithm (EA) have been developed is Differential Evolution (DE). …”
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  10. 10

    Transparent insights into alzheimer’s progression: a time-aware approach with explainable by Abraham Varghese, Ben George, Vinu Sherimon, Prashanth Gouda

    Published 2024
    “…Second, by giving interpretable insights into the algorithm’s decision-making process, the study hopes to empower researchers and physicians. …”
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  11. 11

    Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution by Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.

    Published 2014
    “…In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). …”
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  12. 12

    The duality of technological innovation and dynamic capabilities: the micro-foundation of China's construction machinery industry's rise up the global value chain by Xia, Weifu, Alwie, Aryaty, Taasim, Shairil Izwan, Jin, Wong Tze, Naning, Fatin Hana

    Published 2024
    “…By using structural equation modeling, dynamic panel regression and machine learning algorithms, this study reveals the following main conclusions: (1) The relationship between technological innovation duality and the global value chain position of enterprises is inverted U-shaped, and there is an optimal equilibrium point; (2) Dynamic capabilities partially mediate the relationship between technological innovation duality and global value chain climbing, among which absorptive capacity and reconstruction capacity are particularly critical; (3) The degree of internationalization of enterprises positively moderates the impact of technological innovation duality on dynamic capabilities; (4) The quality of the institutional environment moderates the relationship between technological innovation duality and dynamic capabilities, and a high-quality institutional environment strengthens the positive relationship between the two. …”
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  13. 13

    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…For this purpose, three models is developed, first using recursive least square algorithm (RLS), second using recursive instrument variable (RIV) algorithm and lastly using combination of this two algorithms. …”
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  14. 14

    Ensemble dual recursive learning algorithms for identifying flow with leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…For this purpose, three models is developed, first using recursive least square algorithm (RLS), second using recursive instrument variable (RIV) algorithm and lastly using combination of this two algorithm. …”
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  15. 15

    Mixed Unscented Kalman Filter and differential evolution for parameter identification by Legowo, Ari, Mohamad, Zahratu H., Park, HoonCheol

    Published 2013
    “…Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. …”
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  16. 16

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  17. 17

    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
    “…This paper looks at the feasibility of using the differential evolution algorithm to estimate the linear frequency modulation received signal parameters for radar signal denoising. …”
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  19. 19

    Harmonic Elimination Pulse Width Modulation Using Differential Evolution Technique For Three Phase Voltage Source Inverter by Kamisman, Norazelina

    Published 2018
    “…Computation of DE algorithm and simulation of voltage source inverter using the calculated switching angles are carried out by using Matlab/Simulink software package. …”
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  20. 20

    Exploring dynamic self-adaptive populations in differential evolution by Teo, Jason Tze Wi

    Published 2006
    “…Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. …”
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