Search Results - (( java implementation modified algorithm ) OR ( event detection method algorithm ))

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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
    An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

    An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents by Al-Dyani, Wafa Zubair Abdullah

    Published 2022
    “…This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. …”
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  3. 3
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. The objectives of this work are: i) to provide a comprehensive review of the cloud and scheduling process; ii) to classify the scheduling strategies and scientific workflows; iii) to implement our proposed algorithm with various scheduling algorithms (i.e., Min-Min, Round-Robin, Max-Min, and Modified Max-Min) for performance comparison, within different cloudlet sizes (i.e., small, medium, large, and heavy) in three scientific workflows (i.e., Montage, Epigenomics, and SIPHT); and iv) to investigate the performance of the implemented algorithms by using CloudSim. …”
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  4. 4
    Classification and detection of intelligent house resident activities using multiagent

    Classification and detection of intelligent house resident activities using multiagent by ,, Mohd. Marufuzzaman, M. B. I., Raez, M. A. M., Ali, Rahman, Labonnah F.

    Published 2013
    “…The intelligent home research requires understanding of the human behavior and recognizing patterns of activities of daily living (ADL).However instead of understand the psychosomatic nature of human early projects in this area simply employed intelligence to the household appliance.This paper proposed an algorithm for detecting ADL.The proposed method is based on two opposite state entity extraction.The method reflects on the common data flow of smart home event sequence.The developed algorithm clusters the smart home events by isolating opposite status of home appliance. …”
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  5. 5
    Fatigue Features Extraction of Road Load Time Data Using the S-Transform

    Fatigue Features Extraction of Road Load Time Data Using the S-Transform by S., Abdullah, Che Ku Eddy Nizwan, Che Ku Husin, M. F. M., Yunoh, M. Z., Nuawi, Z. M., Nopiah

    Published 2013
    “…Three types of road load fatigue data were used for simulation purpose, pave track, highway and country road. In this study, an algorithm was developed, to detect the damaging events in the original fatigue signal. …”
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  6. 6
    Methods of intrusion detection in information security incident detection: a comparative study

    Methods of intrusion detection in information security incident detection: a comparative study by Tan, Fui Bee, Yau, Ti Dun, M. N. M., Kahar

    Published 2018
    “…These algorithms and methods provide fast and high rate of detection. …”
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  7. 7
    Prevention And Detection Mechanism For Security In Passive Rfid System

    Prevention And Detection Mechanism For Security In Passive Rfid System by Khor, Jing Huey

    Published 2013
    “…The proposed protocol is designed with lightweight cryptographic algorithm, including XOR, Hamming distance, rotation and a modified linear congruential generator (MLCG). …”
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  8. 8
    Automatic generation of content security policy to mitigate cross site scripting

    Automatic generation of content security policy to mitigate cross site scripting by Mhana, Samer Attallah, Din, Jamilah, Atan, Rodziah

    Published 2016
    “…It can be extended to support generating CSP for contents that are modified by JavaScript after loading. Current approach inspects the static contents of URLs.…”
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  9. 9
    Sleep arousal events detection using PNN-GBMO classifier based on EEG and ECG signals: A hybrid-learning model

    Sleep arousal events detection using PNN-GBMO classifier based on EEG and ECG signals: A hybrid-learning model by Afsoon Badiei, Saeed Meshgini, Ali Farzamnia

    Published 2020
    “…The notion is very effective in the detection of sleep disorders. In this paper, the detection of arousal events is performed using an automatic analysis of EEG and ECG signals. …”
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  10. 10
    Analysis of artificial neural network and viola-jones algorithm based moving object detection

    Analysis of artificial neural network and viola-jones algorithm based moving object detection by Rashidan, M. Ariff, Mohd Mustafah, Yasir, Zainal Abidin, Zulkifli, Zainuddin, N. Afiqah, A. Aziz, Nor Nadirah

    Published 2014
    “…Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. …”
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    Proceeding Paper
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  11. 11
    Data redundancy reduction scheme for data aggregation in wireless sensor network

    Data redundancy reduction scheme for data aggregation in wireless sensor network by Adawy, Mohammad Ibrahim

    Published 2020
    “…Also, the results show that the proposed Anomaly Detection (AD) outperforms FTDA in terms of aggregated data accuracy in which the AD conserved approximately, 59.5% of aggregated data accuracy for event compared with FTDA algorithm which conserved 54.25% of aggregated data accuracy for event. …”
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  12. 12
    Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals

    Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals by Ahmed, Ahmed Elsayed Elmahdy

    Published 2014
    “…This project aims at developing an automated epileptic seizure event detection algorithm. The proposed algorithm depends on using five features which are singular values, total power, delta band power, variance and mean. …”
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    Final Year Project
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  13. 13
    Friendship Degree and Tenth Man Strategy: A new method for differentiating between erroneous readings and true events in wireless sensor networks

    Friendship Degree and Tenth Man Strategy: A new method for differentiating between erroneous readings and true events in wireless sensor networks by Adday, Ghaihab Hassan, Subramaniam, Shamala K., Zukarnain, Zuriati Ahmad, Samian, Normalia

    Published 2023
    “…The results were compared to the baseline algorithm, highlighting key parameters like false alarms and event detection accuracy. …”
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  14. 14
    Temporal video segmentation using squared form of Krawtchouk-Tchebichef moments

    Temporal video segmentation using squared form of Krawtchouk-Tchebichef moments by Abdulhussain, Sadiq H.

    Published 2018
    “…TVS algorithm design is still challenging because most of the recent methods are unable to achieve robust detection for different types of transitions: hard transition (HT) and soft transition (ST). …”
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  15. 15
    Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach

    Automated QT interval measurement using modified Pan-Tompkins algorithm with independent isoelectric line approach by Jumahat, Shaliza, Gan, Kok Beng, Misran, Norbahiah, Islam, Mohammad Tariqul, Mahri, Nurhafizah, Ja'afar, Mohd. Hasni

    Published 2020
    “…This method uses an improved Pan-Tompkins algorithm from the previous work with independent of the isoelectric line approach for detecting the QRS onset and the T offset. …”
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  16. 16
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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  17. 17
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    “…Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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  18. 18
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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  19. 19
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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  20. 20
    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

    A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Suboh, Syahirah, Abdul Aziz, Izzatdin, Shaharudin, Shazlyn Milleana, Ismail, Saidatul Akmar, Mahdin, Hairulnizam

    Published 2023
    “…Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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