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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
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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
    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
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    Mining Sequential Patterns Using I-PrefixSpan

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

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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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
    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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  7. 7
    Improved static and dynamic FBG sensor system for real-time monitoring of composite structures

    Improved static and dynamic FBG sensor system for real-time monitoring of composite structures by Vorathin, Epin

    Published 2017
    “…On the other hand, due to the presence of noise in the signal spectrum, the estimation of time of arrival (TOA) through peak detection is pin-pointed as the drawback in dynamic strain sensing. Thus, the designation of this research study is to improve the current FBG based real-time monitoring system with the use of certain functions and algorithms, that are the instant mesh-grid function, voltage normalization algorithm, CC-LSL algorithm, and FFT function. …”
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  8. 8
    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
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  9. 9
    Speech enhancement based on compressive sensing algorithm

    Speech enhancement based on compressive sensing algorithm by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Chebil, Jalel

    Published 2013
    “…A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. …”
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    Proceeding Paper
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  10. 10
    Speech enhancement based on compressive sensing algorithm

    Speech enhancement based on compressive sensing algorithm by Sulong, Amart, Gunawan, Teddy Surya, Khalifa, Othman Omran, Chebil , Jalel

    Published 2013
    “…A novel speech enhancement by using compressive sensing (CS) is a new paradigm of acquiring signals, fundamentally different from uniform rate digitization followed by compression, often used for transmission or storage. …”
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  11. 11
    A development of a damage monitoring system using an embedded fiber Bragg grating sensors

    A development of a damage monitoring system using an embedded fiber Bragg grating sensors by Hafizi, Z. M., Nizwan, Che Ku Eddy, Ghazali, M. F., Idris, Daing Mohamad Nafiz, Sani, M. S.M.

    Published 2019
    “…From the conclusion of the study, it is truly believed that with this reputable sensing system, it is is one step closer to achieving the key concept of smart structure.…”
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    Research Report
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  12. 12
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  13. 13
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  14. 14
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  15. 15
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  16. 16
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  17. 17
    A study of Time-Of-Flight applications using a TOF camera sensor board

    A study of Time-Of-Flight applications using a TOF camera sensor board by Muhamad Alif Bin Mohd Jaffery

    Published 2023
    “…ABSTRACT Range sensing cameras are often used to obtained depth information similarly on how bats sense its surroundings. …”
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  18. 18
    Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems

    Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems by Uwaechia, Anthony Ngozichukwuka

    Published 2018
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  19. 19
    Integrated multichannel Young’s interferometer sensor based on long-range surface plasmon waveguides

    Integrated multichannel Young’s interferometer sensor based on long-range surface plasmon waveguides by Wong, Wei Ru, Berini, Pierre

    Published 2019
    “…A detection limit of ∼ 1 × 10−6 RIU was obtained for both LRSPP based YIs, a significant improvement over measurements from similar structures using attenuation-based sensing. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement…”
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  20. 20
    High-throughput and energy-efficient Contiki MAC layer scheme in IEEE 802.15.4 for structural health monitoring

    High-throughput and energy-efficient Contiki MAC layer scheme in IEEE 802.15.4 for structural health monitoring by Al-Mekhlafi, Mohamed Abdulkarem Taher

    Published 2021
    “…Finally, a case study of a WSN-based SHM system using developed embedded data filtering and transmission algorithms to reduce data communication is performed and taken place on a concrete beam at Civil Engineering Structure Laboratory, UPM. …”
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