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  1. 1
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  2. 2
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
    “…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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  3. 3
    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network

    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

    Published 2021
    “…On the whole, BR algorithm with 60% training and 30 hidden nodes were successfully developed for BOD analysis, meanwhile, 70% training for COD analysis with the regression values of 0.9978 and 0.9976 respectively. …”
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  4. 4
    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks

    Toxic Gas Dispersion Model Based On Neural Pattern Recognition Networks by Roslan, Nurfarah Arina

    Published 2022
    “…As a result, BR algorithm using 70% training and 28 hidden neurons give the best performance with R-value of 0.95214. …”
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  5. 5
    Extending the decomposition algorithm for support vector machines training

    Extending the decomposition algorithm for support vector machines training by Zaki, N,M., Deris, S., Chin, K.K.

    Published 2003
    “…The decomposition algorithm developed by Osuna et al. (1997a) reduces the training cost to an acceptable level. …”
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  6. 6
    Slew Control of Prolate Spinners Using Single Magnetorquer

    Slew Control of Prolate Spinners Using Single Magnetorquer by Juntian, Si, Yang, Gao, Abadi, Chanik

    Published 2016
    “…E XISTING research [1–5] on the prolate spinning spacecraft attitude maneuver has developed a series of slew algorithms using a single thruster in two categories: half-cone derived algorithms and pulse-train algorithms. …”
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  7. 7
    Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms

    Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms by Veza, I., Irianto, Panchal, H., Paristiawan, P.A., Idris, M., Fattah, I.M.R., Putra, N.R., Silambarasan, R.

    Published 2022
    “…However, most studies of ANN to estimate the biomassâ�� HHV only use one algorithm to train a small number of biomass datasets. …”
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  8. 8
    Performance of various training algorithms on scene illumination classification

    Performance of various training algorithms on scene illumination classification by Hesamian, Mohammad Hesam, Mashohor, Syamsiah, Saripan, M. Iqbal, Wan Adnan, Wan Azizun, Hesamian, B., Hooshyari, M. M.

    Published 2015
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  9. 9
    Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

    Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction by Heng S.Y., Ridwan W.M., Kumar P., Ahmed A.N., Fai C.M., Birima A.H., El-Shafie A.

    Published 2023
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  10. 10
    Advances of metaheuristic algorithms in training neural networks for industrial applications

    Advances of metaheuristic algorithms in training neural networks for industrial applications by Chong H.Y., Yap H.J., Tan S.C., Yap K.S., Wong S.Y.

    Published 2023
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  11. 11
    Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection

    Enhancement of bearing defect diagnosis via genetic algorithm optimized feature selection by Chia, Yee Shin

    Published 2015
    “…Therefore genetic algorithm (GA) is employed to search for a minimum number of relevant features nonlinearly to increase the classification accuracy while reducing the computational effort of the training process. …”
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  12. 12
    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM

    PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM by ABDALLA, OSMAN AHMED

    Published 2011
    “…This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. …”
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  13. 13
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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  14. 14
    Development of high quality speech compression system for Quranic recitation based on modified CELP algorithm

    Development of high quality speech compression system for Quranic recitation based on modified CELP algorithm by Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2013
    “…In this paper, we developed a high quality speech compression for Quranic recitation by modifying Code Excited Linear Prediction (CELP) algorithm. …”
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  15. 15
    REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION

    REDUCING LATENCY IN A VIRTUAL REALITY-BASED TRAINING APPLICATION by P ISKANDAR, YULITA HANUM

    Published 2006
    “…In order to overcome latency problem, this research is an attempt to suggest a new prediction algorithm based on heuristic that could be used to develop a more effective and general system for virtual training applications. …”
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  16. 16
    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi's Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Rama Rao

    Published 2009
    “…The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.…”
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  17. 17
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…Keywords - artificial neural network; mean absolute percentage error; genetic algorithm; simulated annealing; correlation analysisAbstract — Electrical energy demand forecasting plays a pivotal role as a decision support tool in the modern power industry. …”
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  18. 18
    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks

    Autoreclosure in Extra High Voltage Lines using Taguchi’s Method and Optimized Neural Networks by Desta, Zahlay F., K.S., Ramarao, Taj, Mohammed Baloch

    Published 2008
    “…The developed algorithm is effectively trained, verified and validated with a set of training, dedicated testing and validation data respectively.…”
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  19. 19
    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof

    Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof by Yusof, Yusman

    Published 2019
    “…In the algorithm development a step-by-step example of the algorithm implementation is presented and then successfully implemented in Lego Mindstorm obstacle avoiding mobile robot as a proof of concept implementation of the hybrid AI algorithm. …”
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  20. 20
    Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks

    Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks by Abusnaina, Ahmed A. A.

    Published 2015
    “…In addition, training NN is still highly-time consuming. The Mussels Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired ecologically by mussels movement behavior. …”
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