Search Results - (( java application tree algorithm ) OR ( data visualisation new algorithm ))

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  1. 1
    Effectiveness of silhouette rendering algorithms in terrain visualisation

    Effectiveness of silhouette rendering algorithms in terrain visualisation by Che Mat, Ruzinoor, Visvalingam, Mahes

    Published 2002
    “…This approach has been implemented in a 3D modelling system to create a new method of displaying and viewing terrain data in an artistic style. …”
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  2. 2
    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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  3. 3
    Dynamic modeling by usage data for personalization systems

    Dynamic modeling by usage data for personalization systems by Aghabozorgi, S.R., Teh, Y.W.

    Published 2009
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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach

    Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach by Ullah, S., Daud, H., Dass, S.C., Khan, H.N., Khalil, A.

    Published 2017
    “…This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. …”
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  8. 8
    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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  9. 9
    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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  10. 10
    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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  11. 11
    Mining and integration of oral cancer genomics for predictive therapeutics / Bernard Lee Kok Bang

    Mining and integration of oral cancer genomics for predictive therapeutics / Bernard Lee Kok Bang by Bernard Lee, Kok Bang

    Published 2019
    “…Technical evaluation of performance reliability of six gene signature similarity scoring algorithms showed that the Weighted Connectivity Score or the statistically significant Connectivity Map, are prime candidates for upgrading the current core algorithm of DeSigN, which is based on the Kolmogorov-Smirnov statistic. …”
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  12. 12
    Feature extraction and localisation using scale-invariant feature transform on 2.5D image

    Feature extraction and localisation using scale-invariant feature transform on 2.5D image by Suk, Ting Pui, Jacey-Lynn, Minoi, Terrin, Lim, Fradinho Oliveira, João, Fyfe Gillies, Duncan

    Published 2015
    “…In this paper, we present and discuss our new automatic landmarking method on face data using 2.5-dimensional (2.5D) range images. …”
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  13. 13
    Urban green space spatio-temporal change influences on land surface temperature in Kuala Lumpur, Malaysia

    Urban green space spatio-temporal change influences on land surface temperature in Kuala Lumpur, Malaysia by Abu Kasim, Junainah

    Published 2020
    “…The broad range of output generated from the multiple temporal of high-resolution aerial imagery could henceforth improve the reliability of collected data and develop a high-performance outcome in interpreting real visualised scenarios.…”
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  14. 14
    Modified dynamic programming model with limited order picking capacity / Noraimi Azlin Mohd Nordin

    Modified dynamic programming model with limited order picking capacity / Noraimi Azlin Mohd Nordin by Noraimi Azlin , Mohd Nordin

    Published 2021
    “…Fourthly is to test and compare the developed mathematical order picking model with secondary data and finally to simulate the data into three types of categories; small, medium and large. …”
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