Search Results - weight distribution ((using algorithm) OR (learning algorithm))

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  1. 1

    Development of lung cancer prediction system using meta-heuristic optimized deep learning model by Mohamed Shakeel, Pethuraj

    Published 2023
    “…Finally, the classification is implemented using an ensemble classifier, deep learning instantaneously trained a neural network and an Autoencoder-based Recurrent Neural Network (ARNN) classification algorithm. …”
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    Thesis
  2. 2

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”
    Article
  3. 3

    A modified artificial neural network (ANN) algorithm to control shunt active power filter (SAPF) for current harmonics reduction by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Mohd Radzi, Mohd Amran, Mailah, Nashiren Farzilah

    Published 2013
    “…The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. …”
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    Conference or Workshop Item
  4. 4

    A Study of Automated Essay Scoring Frameworks on Evaluating Malaysian University English Test Essays Based on Syntactic and Semantic Features by Chun Then, Lim

    Published 2023
    “…Besides, we also found that the differences between machine learning and deep learning algorithms were not obvious, and neither algorithm's performance can be considered excellent because the quadratically weighted Kappa (QWK) scores were less than 0.75. …”
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  5. 5

    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia by Hindia, Mhd Nour

    Published 2015
    “…The selection is based on the user preferences since it uses a self-learning algorithm to determine triggers and handover thresholds dynamically. …”
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  6. 6
  7. 7

    Orientation and scale based weights initialization scheme for deep convolutional neural networks by Azizi Abdullah, Wong, En Ting

    Published 2020
    “…A crucial component in the CNN is the convolution filters which consist of a series of predefined filter weight initialization values. The filter weights are then automatically learned by the neural network throughout the back- propagation training algorithm. …”
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    Article
  8. 8

    Comparison of diverse ensemble neural network for large data classification by Mumtazimah, Mohamad, Md Yazid, Mohamad Saman

    Published 2015
    “…DRT is an enhanced algorithm based on distributed random for different neural networks. …”
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    Article
  9. 9

    Classification of imbalanced travel mode choice to work data using adjustable svm model by Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E.E.

    Published 2021
    “…For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. …”
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    Article
  10. 10

    The effect of different crossing angles on similarity and stability of target spectra in forward scattering micro radar (FSMR) using graphical user interface (GUI) / Hanis Adiba Mo... by Mohamad, Hanis Adiba

    Published 2012
    “…Besides that, new software in producing the target signatures is developed by using Graphical User Interface (GUI) in MATLAB which can be used as a learning material in universities. …”
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  11. 11

    Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network by Bujal, Noor Ropidah

    Published 2022
    “…The AHP was also found to yield accurate weights of the coefficient factors for each objective function compared to the weight-sum method generally used in studies. …”
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  12. 12

    Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering by Hengda, Wang, Mohamad Mohsin, Mohamad Farhan, Mohd Pozi, Muhammad Syafiq

    Published 2024
    “…To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). …”
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    Article
  13. 13

    Multi-objective Binary Clonal Selection Algorithm In The Retrieval Phase Of Discrete Hopfield Neural Network With Weighted Systematic Satisfiability by Romli, Nurul Atiqah

    Published 2024
    “…Therefore, this thesis proposes a new systematic Satisfiability logical rule namely Weighted Systematic 2 Satisfiability that uses a weighted feature to control the distribution of the negative literals. …”
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  14. 14

    A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

    Published 2020
    “…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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  15. 15

    Application Of The Differential Quadrature Method To Problems In Engineering Mechanics by Fakir, Md.Moslemuddin

    Published 2003
    “…This method is a simple and direct technique, which can be applied in a large number of cases to circumvent the difficulties of programming complex algorithms for the computer, as well as excessive use of storage and computer time. …”
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  16. 16

    Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting by YAXING, WEI, HUZAIFA, HASHIM, Lai, Sai Hin, CHONG, KAI LUN, HUANG, YUK FENG, ALI NAJAH, AHMED, MOHSEN, SHERIF, AHMED, EL-SHAFIE

    Published 2024
    “…Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. …”
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    Article
  17. 17

    Inversion of 2D and 3D DC resistivity imaging data for high contrast geophysical regions using artificial neural networks / Ahmad Neyamadpour by Neyamadpour, Ahmad

    Published 2010
    “…These results show that,for all the arrays (2D and 3D) except 3D pole - dipole data, resilient propagation is the most efficient algorithm for training the DC resistivity data. In the case of 3D study of pole - dipole data, the gradient descent with momentum and an adaptive learning rate algorithm is found to be the most efficient paradigm. …”
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  18. 18

    Selection of access network using cost function method in heterogeneous wireless network by Mohamad Tahir, Hatim, Al-Ghushami, Abdullah Hussein, Yahya, Zainor Ridzuan

    Published 2014
    “…This method covers the weight distribution and cost factor techniques.The weight distribution is used to measure different weights for existing wireless network based on the user's preference and mobile terminal power.The cost factor technique is also used to identify the cost for performing handover target by considering every network parameters and its weight.Results obtained showed that the algorithm has the ability to increase user's satisfaction compared to other algorithms, which consistently choose one accessible network.…”
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    Conference or Workshop Item
  19. 19

    Comparative analysis of spatio/spectro-temporal data modelling techniques by Abdullah, Mohd Hafizul Afifi, Othman, Muhaini, Kasim, Shahreen

    Published 2017
    “…Therefore, this paper presents the comparative analysis between various techniques used to process information from SSTD. Section 2 overviews two different inference-based techniques for SSTD modelling which includes global modelling, local modelling, and personalized modelling; and data modelling for SSTD classifier including, support vector machines (SVM), Evolving Classification Function (ECF), k-Nearest Neighbor (kNN), weighted k-Nearest Neighbor (wkNN), and weighted-weighted k-Nearest Neighbor (wwkNN). …”
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    Book Section
  20. 20

    Design and development of prototype robot gripper for object weight measurement by Almassri, Ahmed M. M.

    Published 2014
    “…Therefore, this study has proposed a robotic gripper prototype with a new configuration of pressure sensor distribution, based on development of grasping algorithm for object’s weight measurement. …”
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    Thesis