Search Results - (( java application based algorithm ) OR ( parameter classification approach algorithm ))

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  1. 1

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. …”
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    Thesis
  2. 2

    Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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    Article
  3. 3

    Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…Eight benchmark datasets from UCI were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy. …”
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    Article
  4. 4

    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection.…”
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    Article
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    Optimizing support vector machine parameters using continuous ant colony optimization by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…Support Vector Machines are considered to be excellent patterns classification techniques.The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and may be done experimentally through time consuming human experience.To overcome this difficulty, an approach such as Ant Colony Optimization can tune Support Vector Machine parameters.Ant Colony Optimization originally deals with discrete optimization problems. …”
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    Conference or Workshop Item
  7. 7

    Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. …”
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    Article
  8. 8

    Hyper-Heuristic Evolutionary Approach for Constructing Decision Tree Classifiers by Kumar, Sunil, Ratnoo, Saroj, Vashishtha, Jyoti

    Published 2021
    “…Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. …”
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    Article
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    A Conceptual Framework of Bacterial Foraging Optimization Algorithm for Data Classification by Hossin, M., Mohd Suria, F.

    Published 2016
    “…Most previous works on Bacterial Foraging Optimization Algorithm (BFOA) for data classification were integrated BFOA as a feature selection algorithm and parameters optimizer for other classifiers. …”
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    Proceeding
  14. 14

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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    Final Year Project
  15. 15

    Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A... by Hamsani, Aqil Khairy, Mohd Bahrin, Ummu Fatihah, Wan Abdul Manan, Wan Dorishah

    Published 2023
    “…To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. The classification model is trained and tested using various split ratios, and the optimal depth for the DT is determined through parameter tuning to achieve the best accuracy. …”
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    Article
  16. 16

    Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

    Published 2006
    “…Support vector machine algorithm is developed for classification of rice plantation data. …”
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    Article
  17. 17

    Improvement on rooftop classification of worldview-3 imagery using object-based image analysis by Norman, Masayu

    Published 2019
    “…Meanwhile, the DT classification using the LS approach produced the highest overall accuracy of 87% and a kappa coefficient of 0.80. …”
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    Thesis
  18. 18

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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    Thesis
  19. 19

    Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images by Adil Humayun, Khan

    Published 2024
    “…This proposed classifier achieved 97.9% classification accuracy on the ISIC dataset. In the third classification algorithm, hybrid features are extracted using AlexNet and VGG-16 through a transfer learning approach where parameter manipulation is implemented to simplify the network. …”
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    Thesis
  20. 20

    Modified word representation vector based scalar weight for contextual text classification by Abbas Saliimi, Lokman

    Published 2024
    “…This experiment aims to determine the effectiveness of the modified word vectors in contextual text classification tasks, utilizing a more computationally efficient approach. …”
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    Thesis