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  1. 1
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  2. 2
    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments

    Developing an intelligent system to acquire meeting knowledge in problem-based learning environments by Chiang, A., Baba, M.S.

    Published 2006
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  3. 3
    An Educational Tool Aimed at Learning Metaheuristics

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. …”
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  4. 4
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  5. 5
    Mapreduce algorithm for weather dataset

    Mapreduce algorithm for weather dataset by Khalid Adam, Ismail Hammad

    Published 2017
    “…This result has revealed the significant impact to the used of MapReduce Algorithm in weather prediction. …”
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  6. 6
    Dung Beetle Optimization algorithm for predicting damage to 2D truss structures

    Dung Beetle Optimization algorithm for predicting damage to 2D truss structures by Lan, Hoang Ton-That

    Published 2025
    “…This study predicts the degree of damage in 2D truss members using the Dung Beetle Optimization algorithm (DBO) in conjunction with finite element analysis for searching natural frequencies. …”
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  7. 7
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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  8. 8
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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  9. 9
    Angle Based Protein Tertiary Structure Prediction Using Bees Optimization Algorithm

    Angle Based Protein Tertiary Structure Prediction Using Bees Optimization Algorithm by Al-Qattan, Zakaria Noor Aldeen Mahmood

    Published 2010
    “…The experiment was conducted on short sequence protein (Met-enkephaline) which has been used in previous researches. The prototype system was built using Visual C# 2008 to fulfill the protein 3D structure prediction requirements. …”
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  10. 10
    Adaptive mesh refinement immersed boundary method for simulations of laminar flows past a moving thin elastic structure

    Adaptive mesh refinement immersed boundary method for simulations of laminar flows past a moving thin elastic structure by Aldlemy, M.S., Rasani, M.R., Ariffin, A.K., Ya, T.M.Y.S.T.

    Published 2020
    “…The cut-cell IBM is used to predict the body forces while FEM is used to predict deformation of the thin elastic structure in order to integrate the motions of the fluid and solid at the interface. …”
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  11. 11
    MapReduce algorithm for weather dataset

    MapReduce algorithm for weather dataset by Majid, Mazlina A., Romli, Awanis, Ahmad, Noraziah, Hammad, Khalid Adam Ismail

    Published 2018
    “…This result has revealed the significant impact to the used of MapReduce Algorithm in weather prediction. …”
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  12. 12
    Integration of GWO-LSSVM for time series predictive analysis

    Integration of GWO-LSSVM for time series predictive analysis by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Bariah, Yusob, Ernawan, Ferda

    Published 2016
    “…It is worth noting that, mitigating the effects of floods can be accomplished by using either structural or non-structural measures, or by a combination of both. …”
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  13. 13
    Development of optimized damage prediction method for health monitoring of ultra high performance fiber-reinforced concrete communication tower

    Development of optimized damage prediction method for health monitoring of ultra high performance fiber-reinforced concrete communication tower by Gatea, Sarah Jabbar

    Published 2018
    “…The verification results indicate that all the structural defects were predicted with high accuracy by the developed hybrid algorithm in cases of healthy and damaged structures. …”
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  14. 14
    River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

    River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia by Mustafa, M.R., Rezaur, R.B., Saiedi, Saied, Isa, M.H.

    Published 2012
    “…It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. …”
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  15. 15
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. …”
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  16. 16
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. …”
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  17. 17
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. …”
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  18. 18
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. …”
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  19. 19
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. …”
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  20. 20
    Model structure selection for a discrete-time non-linear system using genetic algorithm

    Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

    Published 2004
    “…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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