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  1. 1
    A hardware implementation of Rivest-Shamir-Adleman co-processor for resource constrained embedded systems

    A hardware implementation of Rivest-Shamir-Adleman co-processor for resource constrained embedded systems by Paniandi, Arul

    Published 2006
    “…The designed RSA coprocessor core is actually a modular exponentiation hardware engine, which is the basic arithmetic operation in implementing a RSA public key encryption and decryption algorithm. …”
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  2. 2
    An analysis of the AAḃ asymmetric encryption scheme on embedded devices for IoT environment / Syed Farid Syed Adnan

    An analysis of the AAḃ asymmetric encryption scheme on embedded devices for IoT environment / Syed Farid Syed Adnan by Syed Adnan, Syed Farid

    Published 2019
    “…It is also not known how the algorithm would perform against the widely used RSA on resource-constrained platforms. …”
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  3. 3
    A qualitative study of families of Edwards Curves and their applications in cryptography / Nurul Azwa Kamaruddin and Nurul Ain Mustakim

    A qualitative study of families of Edwards Curves and their applications in cryptography / Nurul Azwa Kamaruddin and Nurul Ain Mustakim by Kamaruddin, Nurul Azwa, Mustakim, Nurul Ain

    Published 2012
    “…Among those cryptosystems, there are three types of public key cryptographic systems that are currently considered both secure and efficient, classified according to the mathematical problems upon which they are based: the Integer Factorization Systems (of which the RSA algorithm is the most well known example), the Discrete logarithm Systems (such as the US Government’s Digital Signature Algorithm) and the Elliptic Curve Cryptosystem (ECC). …”
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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
    Simulation of quantum growing network / Ong Boon Teong

    Simulation of quantum growing network / Ong Boon Teong by Ong , Boon Teong

    Published 2003
    “…Over the past 40 years there has been a dramatic miniaturization in computer technology. If current trends continue, by the year 2020 the basic memory components of a computer will be the size of individual atoms. …”
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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    Dynamic force-directed graph with weighted nodes for scholar network visualization

    Dynamic force-directed graph with weighted nodes for scholar network visualization by Mohd. Aris, Khalid Al-Walid, Ramasamy, Chitra, Mohd Aris, Teh Noranis, Zolkepli, Maslina

    Published 2022
    “…The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. …”
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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider

    Computer remote monitoring via mobile phones using socket programming / Samih Omer Fadlelmola Elkhider by Omer Fadlelmola Elkhider, Samih

    Published 2011
    “…The studies show beyond technical algorithms, physical aspects plays a big rule in client server model. …”
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