Search Results - (( data predictive modified algorithm ) OR ( java application scheduling algorithm ))

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  1. 1

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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    Conference or Workshop Item
  2. 2

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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    Final Year Project
  3. 3

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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    Thesis
  4. 4

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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    Thesis
  5. 5

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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    Thesis
  6. 6

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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    Thesis
  7. 7

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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    Article
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    A modified π rough k-means algorithm for web page recommendation system by Zidane, Khaled Ali Othman

    Published 2018
    “…Secondly, a modified πRKM algorithm is proposed to partition the user session data. …”
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    Thesis
  10. 10

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…The research implements stock prediction analysis as a case study for training the neural network by adopting MGWO algorithm. …”
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    Thesis
  11. 11

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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    Thesis
  12. 12

    Modified artificial neural network (ANN) models for Malaysian construction costs indices (MCCI) data / Saadi Ahmad Kamaruddin by Ahmad Kamaruddin, Saadi

    Published 2018
    “…However, this algorithm is not totally robust in the presence of outliers that usually exist in the routine time series data, and this may cause false prediction of future values. …”
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    Book Section
  13. 13

    A WAVELET BASED SIGNAL DENOISING AND HYDROCARBON PREDICTION ALGORITHM FOR A NEW MARINE CSEM ANTENNA DESIGN by NYAMASVISVA, TADIWA ELISHA

    Published 2017
    “…So do other noisy elements, therefore there is a need to identify, quantify and eliminate these noises from the receiver data for better prediction of HC. This research work proposes a modified computational algorithm for decomposing and denoising the MCSEM data. …”
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    Thesis
  14. 14

    Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming by Chuan, Zun Liang, Tham, Ren Sheng, Tan, Chek Cheng, Abraham Lim, Bing Sern, Chong, Yeh Sai

    Published 2025
    “…By employing the proposed innovative modified stacked Multiple Linear Regression-Support Vector Regression-based (MLR-SVR-based) algorithms, and ranking them utilizing the modified Taguchi-based VIseKriterijumska Optimizacija I Kompromisno Resenje (Taguchi-based VIKOR) multi-criteria decision-making algorithm, the analysis demonstrated high predictive accuracy even with limited data. …”
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    Article
  15. 15

    A modified weight optimisation for higher-order neural network in time series prediction by Husaini, Noor Aida

    Published 2020
    “…Hence, motivated by the advantages of those Modified Cuckoo Search (MCS), the improvement of the MCS called Modified Cuckoo Search-Markov chain Monté Carlo (MCS-MCMC) learning algorithm is proposed for weight optimisation. …”
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    Thesis
  16. 16

    Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives by Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.

    Published 2025
    “…This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. …”
    Article
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    Modification of the CREAMS Nutrient submodel by Saleh, Abdul Razak

    Published 2011
    “…The CREAMS nutrient submodel was modified to improve the prediction, of the nitrogen loss from a flat agricultural field with a fluctuating water table.The CREAMS nutrient submodal was modified by incorporating a water function in the CREAMS denitrification algorithm.The capability of the CREAMS nutrient submodel and modified CREAMS nutrient submodel in predicting nitrogen loss was evaluated by using linear regression analysis, t-test on the slope and intercept of the regression equation, standard deviation of differences, absolute average differences, and percent error.Observed data from an experimental plot near Baton Rouge, Louisiana, USA were used in this study.The modified model underestimated the total nitrogen losses by 2% compared to 35% overestimation by the CREAMS model.Overall performance of the modified model in predicting nitrogen losses was satisfactory.…”
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    Conference or Workshop Item
  18. 18

    Smart Agriculture Economics and Engineering: Unveiling the Innovation Behind AI-Enhanced Rice Farming by Zun Liang, Chuan, Tham, Ren Sheng, Tan, Chek Cheng, Abraham Lim, Bing Sern, David Lau, King Luen, Chong, Yeh Sai

    Published 2024
    “…To address these challenges, an innovative Artificial Intelligence-based (AI-based) predictive algorithm has been proposed, leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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    Conference or Workshop Item
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