Search Results - (( data selection method algorithm ) OR ( data application testing algorithm ))

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  1. 1
    Application of the bees algorithm to the selection features for manufacturing data

    Application of the bees algorithm to the selection features for manufacturing data by Pham, D.T, Mahmuddin, Massudi, Otri, S., Al-Jabbouli, H.

    Published 2007
    “…Some of the features may contain irrelevant information caused by data redundancy or by noise. A “wrapper” feature selection method using the Bees Algorithm and Multilayer Perception (MLP) networks is described in this paper. …”
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  2. 2
    ANALYSIS AND INTERPRETATION OF PRESSURE TRANSIENT TEST DATA BY RECENT ROBUST DECONVOLUTION METHODS

    ANALYSIS AND INTERPRETATION OF PRESSURE TRANSIENT TEST DATA BY RECENT ROBUST DECONVOLUTION METHODS by Mohd Mustafa, Muhammad Izzatullah

    Published 2013
    “…However, there are some algorithmic parameters that need to be carefully selected to produce meaningful constantrate (or deconvolved) responses from these algorithms to avoid misinterpretation of the data leading to misidentification of the unknown system. …”
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  3. 3
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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  4. 4
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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  5. 5
    Network reconfiguration and control for loss reduction using genetic algorithm

    Network reconfiguration and control for loss reduction using genetic algorithm by Jawad, Mohamed Hassan Izzaldeen

    Published 2010
    “…Note that the 18-bus system is originally without any capacitor. Two selection methods that are used in Genetic Algorithm are the roulette wheel and tournament selections. …”
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  6. 6
    A clonal selection algorithm model for daily rainfall data prediction

    A clonal selection algorithm model for daily rainfall data prediction by Noor Rodi, Nur Syazwani, Abdul Malik, Marlinda, Sie Chun, Ting, Ismail , Amelia Ritahani, Tang, Chao-Wei

    Published 2014
    “…This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. …”
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  7. 7
    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…This method is used to reduce the stochastic of data generation as the second iteration will have influence of the first iteration data. …”
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  8. 8
    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System

    Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System by Hormozi, Shahram Golzari

    Published 2011
    “…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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  9. 9
    Classification of basal stem rot disease in oil palm using dielectric spectroscopy

    Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

    Published 2018
    “…Two data reduction methods were used 1) feature selections methods and 2) principal component analysis (PCA). …”
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  10. 10
    Fault diagnostic algorithm for precut fractionation column

    Fault diagnostic algorithm for precut fractionation column by Heng, H. Y., Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki

    Published 2004
    “…The fault diagnostic algorithm is supported by the process history based method and developed by using Borland C++ Builder 6.0. …”
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  11. 11
    Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour

    Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour by Pebrianti, Dwi, Ariawan, Angga, Bayuaji, Luhur, Mahdiana, Deni, ,, Rusdah

    Published 2022
    “…Meanwhile, k-NN algorithm is used to classify the experimental data. …”
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    Proceeding Paper
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  12. 12
    Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman

    Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman by Seman, Ali

    Published 2013
    “…Out of six data sets, the &-AMH algorithm obtained the highest mean accuracy scores for the five data sets and one data set was at equal performance. …”
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  13. 13
    Gene selection for high dimensional data using k-means clustering algorithm and statistical approach

    Gene selection for high dimensional data using k-means clustering algorithm and statistical approach by Ahmad, Farzana Kabir, Yusof, Yuhanis, Othman, Nor Hayati

    Published 2014
    “…Thus, selection of relevant genes is a challenging issue in microarray data analysis and has been a central research focus.This study proposed kmeans clustering algorithm to groups the relevant genes. …”
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  14. 14
    Hybrid binary whale with harris hawks for feature selection

    Hybrid binary whale with harris hawks for feature selection by Alwajih, R., Abdulkadir, S.J., Al Hussian, H., Aziz, N., Al-Tashi, Q., Mirjalili, S., Alqushaibi, A.

    Published 2022
    “…As a result, feature selection is offered as a method for eliminating unwanted characteristics. …”
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  15. 15
    Prediction of Total Suspended Sediments in Penang Waters Using Remote Sensing

    Prediction of Total Suspended Sediments in Penang Waters Using Remote Sensing by Tan, Sek Aun

    Published 2004
    “…Out of the six algorithms derived, Algorithm 6 showed the best correlation with the ground-truth data (R2 value of 0.9755 and RMSE value of 4.0107). …”
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  16. 16
    Sports tournament scheduling using genetic algorithm / Hafeezur Syakir Abdul Motok@Mohd Ridzuan

    Sports tournament scheduling using genetic algorithm / Hafeezur Syakir Abdul Motok@Mohd Ridzuan by Abdul Motok@Mohd Ridzuan, Hafeezur Syakir

    Published 2020
    “…In genetic algorithm, there are steps include such as initialize population, selection, crossover, mutation and calculate fitness. …”
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  17. 17
    A Naïve-Bayes classifier for damage detection in engineering materials

    A Naïve-Bayes classifier for damage detection in engineering materials by Addin, O., Salit, Mohd Sapuan, Mahdi Ahmad Saad, Elsadig, Othman, Mohamed

    Published 2007
    “…The Naïve-Bayes classifier and the feature sub-set selection method were analyzed and tested on two sets of data. …”
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  18. 18
    An ensemble feature selection method to detect web spam

    An ensemble feature selection method to detect web spam by Oskouei, Mahdieh Danandeh, Razavi, Seyed Naser

    Published 2018
    “…An ensemble feature selection method has been proposed in this paper to detect web spam. …”
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  19. 19
    Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif

    Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif by Abdalla Osama , Hamdan Abdellatif

    Published 2024
    “…This thesis presents two innovative methods that holistically address these challenges at algorithmic and data levels to enhance heart disease detection. …”
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  20. 20
    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network

    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

    Published 2014
    “…Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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