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  1. 1
    A Standard Deviation Selection in Evolutionary Algorithm for Grouper Fish Feed Formulation

    A Standard Deviation Selection in Evolutionary Algorithm for Grouper Fish Feed Formulation by Soong, Cai Juan, Razamin, Ramli, Rosshairy, Abdul Rahman

    Published 2016
    “…In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available. …”
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  2. 2
    A standard deviation selection in evolutionary algorithm for grouper fish feed formulation

    A standard deviation selection in evolutionary algorithm for grouper fish feed formulation by Soong, Cai Juan, Ramli, Razamin, Abdul Rahman, Rosshairy

    Published 2016
    “…Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment.Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price.However, the demand of grouper fish is still insufficient from the wild catch.Therefore, there is a need to farm grouper fish to cater to the market demand.In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available.Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm.Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation.Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable.The feasible and low fitness, quick solution can be obtained. …”
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  3. 3
    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A, Aziz, IA, Hasan, MH

    Published 2020
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  4. 4
    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A., Aziz, I.A., Hasan, M.H.

    Published 2020
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  5. 5
    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm

    Prediction and investigation of surface response in high speed end milling of Ti-6Al-4V and optimization by genetic algorithm by Alam, Md. Shah, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah, Konneh, Mohamed

    Published 2010
    “…The developed quadratic prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness.…”
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  6. 6
    An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method

    An Improved Mathematical Model to Predict Surface Roughness Using Hybrid Method by M. F. F., Ab Rashid

    Published 2015
    “…Therefore, an accurate mathematical model to predict surface roughness is totally needed. This research presents a hybrid method which combine conventional multiple regression analysis and genetic algorithm to improve the accuracy of mathematical model to predict surface roughness. …”
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  7. 7
    Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

    Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy by Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  8. 8
    Predictive modelling of machining parameters of S45C mild steel

    Predictive modelling of machining parameters of S45C mild steel by Abbas, Adnan Jameel

    Published 2016
    “…Finally, selection the better algorithm that gives the best and ideal results of temperature, roughness and cutting time is selected as an ideal network for prediction the ideal cutting performance for future works.…”
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  9. 9
    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage

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

    Published 2010
    “…This paper proposed that, combination of two algorithms into one learning algorithm for predicting mass flow rate of a flow with leakage resulting in a better mass prediction error as compared to a model with single learning algorithm.…”
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  10. 10
    Ensemble dual recursive learning algorithms for identifying flow with leakage

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

    Published 2010
    “…This paper proposed that, combination of two algorithms into one learning algorithm for predicting mass flow rate of a flow with leakage resulting in a better mass prediction error compare to a model with single learning algorithm.…”
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  11. 11
    PREDICTING THE PRICE OF COTTON USING RNN AND LSTM

    PREDICTING THE PRICE OF COTTON USING RNN AND LSTM by MOHAMAD, AHMAD LUKMAN

    Published 2020
    “…The data will then be separated into training set and testing set and will be feed to the machine learning algorithm to find the pattern and try to do prediction. …”
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  12. 12
    Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.]

    Optimisation of surface roughness when CNC Turning of Al-6061: Application of Taguchi Design of experiments and genetic algorithm / Boppana V. Chowdary...[et al.] by V. Chowdary, Boppana, Jahoor,, Riaz, Ali,, Fahraz, Trishel, Gokool

    Published 2019
    “…The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). …”
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  13. 13
    Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining

    Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining by Saw, Lip Huat, Ho, Li Wen, Yew, Ming Chian, Yusof, Farazila, Pambudi, Nugroho Agung, Ng, Tan Ching, Yew, Ming Kun

    Published 2018
    “…Experimental results agreed well with the GA prediction results with a relative error of 3%. Hence, the results showed that ANFIS-GA is a faster and more accurate alternative to the existing methods for tool wear prediction.…”
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  14. 14
    Prediction of Optimum Cutting Conditions in Dry Turning Operations of S45C Mild Steel using AIS and PSO Intelligent Algorithm

    Prediction of Optimum Cutting Conditions in Dry Turning Operations of S45C Mild Steel using AIS and PSO Intelligent Algorithm by Minhat, Mohamad, Abd Rahman, Md Nizam, Abbas, Adnan Jameel

    Published 2014
    “…The suggested system is based on Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) intelligent algorithms. S45C Mild steel bars are machined at different cutting conditions (cutting speeds, feed rates and depths of cut) without the use of cutting fluid. …”
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  15. 15
    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coated WC-Co inserts

    Application of response surface methodology coupled with genetic algorithm in the optimization of cutting conditions for surface roughness in end milling of Inconel 718 using coate... by Ishtiyaq, M. H., Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

    Published 2011
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  16. 16
    Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems

    Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems by Mohd Zakimi, Zakaria, Hishamuddin, Jamaluddin, Robiah, Ahmad, Sayed Mohammad Reza, Loghmanian

    Published 2013
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  17. 17
    Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel

    Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel by Amin, A. K. M. Nurul, Hafiz, A.M. Khalid, Lajis, M. A.

    Published 2011
    “…They developed contours to select a combination of cutting speed, and feed without increasing the surface roughness. Öktem et al. [6] incorporated RSM with developed genetic algorithm to optimize cutting parameters for better surface quality in case of Inconel 718. …”
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  18. 18
    Prediction of MUET result based on KNN algorithm / Siti Fatimah Azzahra Hamrizan

    Prediction of MUET result based on KNN algorithm / Siti Fatimah Azzahra Hamrizan by Hamrizan, Siti Fatimah Azzahra

    Published 2021
    “…The study aims to explore the K-Nearest Neighbour (KNN) algorithm in solving the MUET result prediction problem, to develop a prototype of MUET result prediction based on the KNN algorithm and to evaluate the accuracy of the KNN algorithm in MUET result prediction. …”
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  19. 19
    Recursive linear network modeling for detecting gas leak

    Recursive linear network modeling for detecting gas leak by Md Akib, Afifi, Saad, Nordin, Asirvadam, Vijanth

    Published 2010
    “…Relative mass loss of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithm. The objective of this paper is to describe the use of recursive solution in order to predict the release of mass flow rate using on-line data. …”
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  20. 20
    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…The anomaly data deviate far from normal and may feed wrong information that might lead to wrong decisions and predictions. …”
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