Search Results - (( using optimization modified algorithm ) OR ( variable prediction using algorithm ))

Refine Results
  1. 1

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

    Published 2019
    “…Subsequently, the research attempts to construct an ensemble model applying Modified Grey Wolf Optimizer (MGWO) and neural network for stock prediction. …”
    Get full text
    Get full text
    Thesis
  2. 2

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
    Get full text
    Get full text
    Thesis
  3. 3

    Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques by Hammid, Ali Thaeer

    Published 2018
    “…Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. …”
    Get full text
    Get full text
    Thesis
  4. 4
  5. 5

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
    “…In this work, the optimal base pressure is determined using the PCA-BAS-ENN-based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for the smooth flow of aerodynamic vehicles. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  6. 6

    Robust correlation feature selection based support vector machine approach for high dimensional datasets by Baba, Ishaq Abdullahi, Mohammed, Mohammed Bappah, Jillahi, Kamal Bakari, Umar, Aliyu, Hendi, Hasan Talib

    Published 2025
    “…The second step utilizes the estimates of weights from the first step to select the most important variables for the model. The third step employs the support vector machine algorithm to calculate prediction values. …”
    Get full text
    Get full text
    Get full text
    Article
  7. 7

    A hybrid prediction model for short-term load forecasting in power systems by Zuriani, Mustaffa, Mohd Herwan, Sulaiman

    Published 2024
    “…Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. …”
    Get full text
    Get full text
    Get full text
    Article
  8. 8

    Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

    Published 2010
    “…Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. …”
    Get full text
    Get full text
    Thesis
  9. 9

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…The second test evaluates IFS in a controlled study using simulated datasets. Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  10. 10

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

    Published 2024
    “…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  11. 11

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

    Published 2025
    “…An extreme learning machine (ELM) prediction approach refined by Aquila optimizer (AO), a new cutting-edge metaheuristic optimization algorithm was utilized to develop a prediction model for the performance assessment of the developed solder composites. …”
    Article
  12. 12

    Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

    Published 2021
    “…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
    Get full text
    Get full text
    Article
  13. 13

    Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

    Published 2021
    “…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
    Get full text
    Get full text
    Article
  14. 14

    Comparative modelling of strength properties of hydrated-lime activated rice husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (AN... by Onyelowe, K. C., Alaneme, G. U., Onyia, M. E., Bui Van, D., Dimonyeka, M. U., Nnadi, E., Ogbonna, C., Odum, L. O., Aju, D. E., Abel, C., Udousoro, I. M., Onukwugha, E.

    Published 2021
    “…The models were compared in terms of accuracy of prediction using MAE, RMSE and coefficient of determination and from the computed results, 0.2750, 0.4154 and 0.9983 respectively for ANN model while 0.3737, 0.6654 and 0.9894 respectively was obtained for fuzzy logic model. …”
    Get full text
    Get full text
    Get full text
    Article
  15. 15

    Hybrid dynamic scheduling model for flexible manufacturing system with machine availability and new job arrivals by Paslar, Shahla

    Published 2015
    “…The idea of hybridizing the newly developed biogeography based optimization algorithm (BBO) with variable neighborhood structure (VNS) is proposed in order to produce a high performance initial schedule in terms of minimum completion time, tardiness and flow time within reasonable amount of time. …”
    Get full text
    Get full text
    Thesis
  16. 16

    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…A comparison was done against particle swarm optimization, genetic algorithm, and sine–cosine algorithm, where the modified cuckoo search algorithm showed the lowest root mean square error and fastest convergence rate among the three algorithms.…”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

    Published 2018
    “…Since in practice turbines do not work in ideal conditions, the theoretical power curve provided by manufacturers is avoided and a power curve approximated by MHTan is used instead. Several statistical methods are used to predict wind speed and the best one is selected for prediction over longer horizons. …”
    Get full text
    Get full text
    Get full text
    Thesis
  18. 18
  19. 19
  20. 20