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  1. 1
    The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

    The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model by Nur Faraidah, Muhammad Di, Siti Zanariah, Satari

    Published 2017
    “…In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. …”
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  2. 2
    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model

    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model by Sulaiman , Marizan, Adnan, Tawafan, Ibrahim, Zulkifilie

    Published 2013
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  3. 3
    Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network

    Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network by Karimi, Abbas, Abedini, S. M., Zarafshan, Faraneh, Syed Mohamed, Syed Abdul Rahman Al-Haddad

    Published 2013
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  4. 4
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  5. 5
    Cluster-based spectrum sensing scheme in heterogeneous network

    Cluster-based spectrum sensing scheme in heterogeneous network by Hasan, Mohammad Kamrul, Ismail, Ahmad Fadzil, Hassan Abdalla Hashim, Aisha, Mohd. Ramli, Huda Adibah, Hashim, Wahidah, Islam, Shayla, Badron, Khairayu

    Published 2014
    “…A cluster formation algorithm is also proposed in where, cluster head (CH) and signaling node (SN) will detect implies the multi-channel SS technique. …”
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  6. 6
    Cluster-based spectrum sensing scheme in heterogeneous network

    Cluster-based spectrum sensing scheme in heterogeneous network by Hasan M.K., Ismail A.F., Abdalla A.-H., Ramli H.A.M., Islam S., Hashim W., Badron K.

    Published 2023
    Conference Paper
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  7. 7
    Cluster-based spectrum sensing scheme in heterogeneous network

    Cluster-based spectrum sensing scheme in heterogeneous network by Hasan, Mohammad Kamrul, Ismail, Ahmad Fadzil, Mohd. Ramli, Huda Adibah, Hassan Abdalla Hashim, Aisha, Islam, Shayla, Hashim, Wahidah, Badron, Khairayu

    Published 2015
    “…A cluster formation algorithm is also proposed in where, cluster head (CH) and signaling node (SN) will detect implies the multi-channel SS technique. …”
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  8. 8
    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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  9. 9
    Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes

    Classification Analysis Of High Frequency Stress Wave For Autonomous Detection Of Defect In Steel Tubes by Abd Halim, Zakiah, Jamaludin, Nordin, Junaidi, Syarif, Syed Yahya, Syed Yusaini

    Published 2014
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  10. 10
    Application of clustering in managing unstructured textual data in relational database / Wael Mohamed Shaher Yafooz

    Application of clustering in managing unstructured textual data in relational database / Wael Mohamed Shaher Yafooz by Yafooz, Wael Mohamed Shaher

    Published 2014
    “…Three experiments have been conducted on textual Reuters corpus, Classic and WAP dataset. The clustering results have been validated using the F-measure, Entropy and Purity methods of measurement and compared with two common methods, which are information extraction and textual document clustering, for example, K-means, Frequent Item-Set, Hierarchical Clustering Algorithms and Oracle Text. …”
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  11. 11
    Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation

    Defect Detection And Classification Of Silicon Solar Wafer Featuring Nir Imaging And Improved Niblack Segmentation by Mahdavipour, Zeinab

    Published 2016
    “…The classification combines the analysis of defect intensity features, the application of unsupervised k-mean clustering and multi-class SVM algorithms. The methods have been applied for detecting, clustering and classification polycrystalline solar wafer images, corresponding to defects such as micro cracks, stain, and fingerprints. …”
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  12. 12
    Fault Detection Relevant Modeling of an Industrial Gas Turbine based on Neuro-Fuzzy Approach

    Fault Detection Relevant Modeling of an Industrial Gas Turbine based on Neuro-Fuzzy Approach by Alemu Lemma, Tamiru, Mohd Hashim, Fakhruldin, Rangkuti, Chalillullah

    Published 2010
    “…Structure and network weights for the NF model are determined by a synergetic approach – Data clustering and Gradient Descent algorithm. Operation data collected in 10 seconds interval and for one day is used for model training and validation. …”
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  13. 13
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  14. 14
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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  15. 15
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…In this paper, we propose an improvement of pattern growth-based PrefixSpan algorithm, called I-PrefixSpan. The general idea of I-PrefixSpan is to use the efficient data structure for general tree-like framework and separator database to reduce the execution time and memory usage. …”
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  16. 16
    On Clustering Algorithm Of Coverage Area Problems In Wireless Sensor Networks

    On Clustering Algorithm Of Coverage Area Problems In Wireless Sensor Networks by Ismail Abdullah, Kalid Abdlkader Marsal

    Published 2024
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  17. 17
    AI powered asthma prediction towards treatment formulation: an android app approach

    AI powered asthma prediction towards treatment formulation: an android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Md Muzahid, Abu Jafar, Sarker, Md Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md

    Published 2022
    “…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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  18. 18
    i-ManGoeS

    i-ManGoeS by Ahmad, Khairul Adilah, Abdul Malik, Anis Faradella

    Published 2017
    “…In this paper, an activity recognition system based on edge detection from a digital camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic gestures. …”
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  19. 19
    AI powered asthma prediction towards treatment formulation : An android app approach

    AI powered asthma prediction towards treatment formulation : An android app approach by Murad, Saydul Akbar, Adhikary, Apurba, Muzahid, Abu Jafar Md, Sarker, Md. Murad Hossain, Khan, Md. Ashikur Rahman, Hossain, Md. Bipul, Bairagi, Anupam Kumar, Masud, Mehedi, Kowsher, Md.

    Published 2022
    “…We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. …”
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