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  1. 1
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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  2. 2
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…The proposed algorithm is grounded on the two famous metaheuristic algorithms: cuckoo search (CS) and covariance matrix adaptation evolution strategy (CMA-es). …”
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  3. 3
    Implementation of a parallel XTS encryption mode of operation

    Implementation of a parallel XTS encryption mode of operation by Alomari, Mohammad Ahmed, Samsudin, Khairulmizam, Ramli, Abdul Rahman

    Published 2014
    “…The parallel XTS algorithm has shown a speedup of 1.80, with 90% efficiency, faster than the sequential algorithm. …”
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  4. 4
    Email spam classification based on deep learning methods: A review

    Email spam classification based on deep learning methods: A review by Tusher, Ekramul Haque, Mohd Arfian, Ismail, Anis Farihan, Mat Raffei

    Published 2025
    “…Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. …”
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  5. 5
    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification

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

    Published 2012
    “…Individual emotional states are highly variable and are subject to evolution from personal experiences. For this reason, the above system is designed to be able to perform learning and classification in real-time to account for inter-individual and intra-individual emotional drift over time. …”
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  6. 6
    Artificial fish swarm optimization for multilayer network learning in classification problems

    Artificial fish swarm optimization for multilayer network learning in classification problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam

    Published 2012
    “…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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  7. 7
    Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems

    Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2012
    “…In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems. …”
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  8. 8
    Parallel implementation on improved error signal of backpropagation algorithm

    Parallel implementation on improved error signal of backpropagation algorithm by Mohd Aris, Teh Noranis, Sulaiman, Md. Nasir, Mohd Saman, Md. Yazid, Othman, Mohamed

    Published 2002
    “…Further study on the improved BP algorithm is carried out on many processors, which is implemented using the Sequent Symmetry SE30 machine. …”
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  9. 9
    Parallel implementation on improved error signal of backpropagation algorithm

    Parallel implementation on improved error signal of backpropagation algorithm by Mohd Aris, Teh Noranis

    Published 2001
    “…The experiments are implemented using the Sequent Symmetry SE30 parallel machine. …”
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  10. 10
    High performance visualization of human tumor growth software

    High performance visualization of human tumor growth software by Alias, Norma, Mohd. Said, Norfarizan, Khalid, Siti Nur Hidayah, Sin, Dolly Tien Ching, Phang, Tau Ing

    Published 2008
    “…This paper focuses on the implementation of parallel algorithm for the simulation of hyperbolic equation for breast pressure and parabolic equation of brain tumors growth. …”
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  11. 11
    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm

    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2011
    “…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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  12. 12
    A Parallel new high order iterative algorithm on shared memory multiprocessors parallel computer

    A Parallel new high order iterative algorithm on shared memory multiprocessors parallel computer by Mohamed Othman, Abdul Rahman Abdullah

    Published 2004
    “…In this paper, the parallel implementation of the algorithm with optimal strategy on shared memory multiprocessors (SMP) was presented and discussed. …”
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  13. 13
    Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems

    Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems by Uvaraja, Vikneswary

    Published 2018
    “…Additionally, the MTS algorithm is also implemented in parallel computing to produce parallel MTS for generating comparable solutions in shorter computational times. …”
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  14. 14
    Enhancement of parallel thinning algorithm for handwritten characters using neural network

    Enhancement of parallel thinning algorithm for handwritten characters using neural network by Engkamat, Adeline

    Published 2005
    “…Thus, this project presents a framework for the implementation of a multilayer perceptron neural network with backpropagation algorithm, in the Parker thinning algorithm in order to produce a fast fully parallel thinning algorithm. …”
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  15. 15
    Fast Finite Difference Time Domain Algorithms for Solving Antenna Application Problem

    Fast Finite Difference Time Domain Algorithms for Solving Antenna Application Problem by Hasan, Mohammad Khatim

    Published 2008
    “…This thesis describes the implementations of new parallel and sequential algorithms for electromagnetic wave propagation from a monopole antenna. …”
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  16. 16
    Discretization of crack propagation on parallel computing : complexity and parallel algorithms with source code

    Discretization of crack propagation on parallel computing : complexity and parallel algorithms with source code by Alias, Norma, Islam, Md. Rajibul

    Published 2010
    “…The element stiffness matrix which had been set up earlier can be used to describe the elastic deformation since the numerical FEM is emphasized as a platform for discretization of one-dimensional continua problems where the stress is uniaxial and implemented on parallel algorithm using the distributed parallel computer system. …”
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  17. 17
    A New Quadratic Binary Harris Hawk Optimization For Feature Selection

    A New Quadratic Binary Harris Hawk Optimization For Feature Selection by Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah

    Published 2019
    “…A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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  18. 18
    A Parallel AGE Method for Parabolic Problems with Special Geometries

    A Parallel AGE Method for Parabolic Problems with Special Geometries by Muda, Zaiton, Othman, Mohamed, Chuah, Chin Yew

    Published 1994
    “…This paper reports the development and implementation of the parallel AGE algorithm. The results from parallel implementation are compared with those of the sequential implementation.…”
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  19. 19
    Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi

    Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi by Hayawi, Mustafa Jabbar

    Published 2015
    “…The control of a parallel manipulator based on joint space control is implemented by determine the lengths of the electrohydraulic actuators EHA individually. …”
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  20. 20
    Discretization of crack propagation on parallel computing: complexity and parallel algorithms with source code

    Discretization of crack propagation on parallel computing: complexity and parallel algorithms with source code by Alias, Norma, Islam, Md. Rajibul

    Published 2010
    “…The element stiffness matrix which had been set up earlier can be used to describe the elastic deformation since the numerical FEM is emphasized as a platform for discretization of one-dimensional continua problems where the stress is uniaxial and implemented on parallel algorithm using the distributed parallel computer system. …”
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