Search Results - (( parallel classification problem algorithm ) OR ( using vectorization matching algorithm ))

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  1. 1

    Block based motion vector estimation using fuhs16 uhds16 and uhds8 algorithms for video sequence by S. S. S. , Ranjit

    Published 2011
    “…In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. …”
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    Book Chapter
  2. 2

    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network by Sefidgar, Seyed Mohammad Hossein

    Published 2014
    “…This method resulted around 99% of classification rate. To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate.…”
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    Thesis
  3. 3

    Partial fingerprint recognition using support vector machine by Vijayaprasad, Perumal, Sulaiman, Md. Nasir, Mustapha, Norwati, O. K. Rahmat, Rahmita Wirza

    Published 2010
    “…Global minutiae-based matching algorithm is used to record the matching pairs and their feature vectors are used to generate a model file which is used for classification. …”
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    Article
  4. 4

    Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm by Nurcahyawati, Vivine, Mustaffa, Zuriani

    Published 2023
    “…This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. …”
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    Article
  5. 5

    The forecasting of poverty using the ensemble learning classification methods by Zamzuri, Muhammad Haziq Adli, Nadilah, Sofian, Hassan, Raini

    Published 2023
    “…The results of the algorithms showed the poverty trend, which helped to determine the poverty classification. …”
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    Article
  6. 6

    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms by Loo, C.K., Liew, W.S., Seera, M., Lim, Einly

    Published 2015
    “…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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    Article
  7. 7

    Text classification using Naive Bayes: An experiment to conference paper by Sainin, Mohd Shamrie

    Published 2005
    “…In the problem of document text classification for conference paper, various papers themes will normally be classified manually by the conference management.Once the classification of the papers is ready, the parallel sessions for presentation according to the themes will be scheduled. …”
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    Conference or Workshop Item
  8. 8

    A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

    Published 2002
    “…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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    Thesis
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  10. 10

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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    Article
  11. 11

    Human computer interaction using isolated-words speech recognition technology by Abu Shariah, Mohammad Abd-Alrahman Mahmoud, Ainon, Raja Noor, Zainuddin, Roziati, Khalifa, Othman Omran

    Published 2007
    “…To extract features from speech signals, Mel-Frequency Cepstral Coefficients (MFCC) algorithm was applied. Subsequently, Vector Quantization was used for all feature vectors generated from the MFCC. …”
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    Proceeding Paper
  12. 12

    Investigation of block matching algorithm for video coding by Faizul Hadi Mohamad Jamil

    Published 2013
    “…The temporal model deals with motion estimation (ME) and motion compensation (MC) algorithm with the matching technique called “Block Matching Algorithm” (BMA) to produce the next encoded video frame with motion vector. …”
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    Thesis
  13. 13

    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2011
    “…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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    Conference or Workshop Item
  14. 14

    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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    Article
  15. 15

    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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    Article
  16. 16

    Signature verification system using support vector machine by Fauziyah, Salehuddin, Azlina , Othman

    Published 2009
    “…The general on-line verification procedures are preprocessing, features extraction, detail matching and post processing. The common verification algorithm is one of the Global Feature Vector Machine called Support Vector Machine (SVM). …”
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    Conference or Workshop Item
  17. 17

    Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO by Naffouti, S.E., Aouissaoui, I., Fougerolle, Y., Sakly, A., Meriaudeau, F.

    Published 2017
    “…This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. …”
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    Article
  18. 18

    Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms by Bundak, Caceja Elyca

    Published 2021
    “…A matching algorithm is used to match between the top 10 ranked RPs with the nearest Euclidean distance to the TP with the RPs clustered. …”
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    Thesis
  19. 19

    Training data selection for record linkage classification by Zaturrawiah Ali Omar, Zamira Hasanah Zamzuri, Noratiqah Mohd Ariff, Mohd Aftar Abu Bakar

    Published 2023
    “…Random forest and support vector machine classification algorithms were compared, and random forest with the top and imbalanced construction produced an F1 -score comparable to probabilistic record linkage using the expectation maximisation algorithm and EpiLink. …”
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    Article
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