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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  4. 4
    Mining association rules from structured XML data

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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    Final Year Project / Dissertation / Thesis
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  6. 6
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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  7. 7
    Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition

    Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition by Mohd Zaki, Hasan Firdaus, Shafait, Faisal, Mian, Ajmal

    Published 2017
    “…This technique motivates bilateral transfer learning between the modalities by taking the outer product of each feature extractor output. …”
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  8. 8
    Real time electrocardiogram identification with multi-modal machine learning algorithms

    Real time electrocardiogram identification with multi-modal machine learning algorithms by Waili, Tuerxun, Mohd Nor, Rizal, Sidek, Khairul Azami, Abdul Rahman, Abdul Wahab, Guven, Ghokan

    Published 2017
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    Proceeding Paper
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  9. 9
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  10. 10
    Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification

    Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification by Hakim, S.J.S., Abdul Razak, H.

    Published 2013
    “…However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. …”
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  11. 11
    Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei

    Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei by Chen , Shilei

    Published 2021
    “…Meanwhile, the unsupervised learning method using PCA-WCC features is good at detecting unknown damage, and is sensitive to low-severity damage. …”
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  12. 12
    Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)

    Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS) by Hakim, S.J.S., Razak, H.A.

    Published 2012
    “…The adaptive neuro-fuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuro-adaptive learning algorithms. …”
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  13. 13
    Development of damage identification scheme using de-noised modal frequency response function data with artificial neural network / Mohamad Izzudin Hussein Shah

    Development of damage identification scheme using de-noised modal frequency response function data with artificial neural network / Mohamad Izzudin Hussein Shah by Mohamad Izzudin , Hussein Shah

    Published 2018
    “…Multilayer Perceptron (MLP) with backpropagation learning algorithm ANN is used in this study. Moreover, this study needs to minimize the number of samples used by reducing number of sensors and frequency range used without affecting the performance accuracy. …”
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  14. 14
    Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan

    Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan by Meisam , Gordan

    Published 2020
    “…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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  15. 15
    Multi-modal association learning using spike-timing dependent plasticity (STDP)

    Multi-modal association learning using spike-timing dependent plasticity (STDP) by Ibrahim, Mohammed Fadhil

    Published 2014
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  16. 16
    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
    “…The idea was incorporated into a new algorithm called, k-Approximate Modal Haplotypes (&-AMH) algorithm. …”
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  17. 17
    Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

    Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Al-Garadi, Mohammed Ali

    Published 2019
    “…These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. …”
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  18. 18
    Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

    Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review by Khan, Z., Yahya, N., Alsaih, K., Al-Hiyali, M.I., Meriaudeau, F.

    Published 2021
    “…This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. …”
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  19. 19
    Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

    Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review by Khan, Z., Yahya, N., Alsaih, K., Al-Hiyali, M.I., Meriaudeau, F.

    Published 2021
    “…This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. …”
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  20. 20
    Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

    Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm by Molla, M. M. Imran, Jui, Julakha Jahan, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid

    Published 2020
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