Search Results - (( java implication tree algorithm ) OR ( wolf classification learning algorithm ))

  • Showing 1 - 7 results of 7
Refine Results
  1. 1
  2. 2
  3. 3

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

    Published 2019
    “…The proposed approachoutperforms existing available meta-heuristic algorithms. Specifically, the proposed model achieved 97% classification rate, 95% precise prediction and less than 2.0 error rate. …”
    Get full text
    Get full text
    Thesis
  4. 4

    Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification by Adamu, Shamsuddeen, Alhussian, Hitham, Aziz, Norshakirah, Abdulkadir, Said Jadid, Alwadin, Ayed, Abdullahi, Mujaheed, Garba, Aliyu

    Published 2025
    “…Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. …”
    Get full text
    Get full text
    Article
  5. 5

    Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

    Published 2021
    “…Results show that the best feature selection is term frequency and trimming of the feature with a frequency greater than 95%. The best news classification approach is based on Deep Learning techniques that provide the most accurate classification. …”
    Get full text
    Get full text
    Book Section
  6. 6

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

    Published 2016
    “…The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  7. 7

    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
    Get full text
    Get full text
    Get full text
    Article