Search Results - (( developing event detection algorithm ) OR ( java application testing algorithm ))
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1
Real time power quality event detection using continuous wavelet transform
Published 2023“…The proposed algorithm is also capable to perform voltage sag and voltage transient classification for event count upon event detection. …”
Conference paper -
2
An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents
Published 2022“…Event Detection (ED) works on identifying events from various types of data. …”
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Thesis -
3
RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Final Year Project -
4
Fatigue Features Extraction of Road Load Time Data Using the S-Transform
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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5
Leveraging Web Scraping in Predictive Modelling of Supply Risk Detection
Published 2022“…For these purposes, a web scraping program is developed to identify the supply risks caused by these temporary events. …”
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Final Year Project Report / IMRAD -
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Classification and detection of intelligent house resident activities using multiagent
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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7
Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications
Published 2017“…The optimization of peak detection algorithms for electroencephalogram (EEG) signal analysis is an ongoing project; previously existing algorithms have been used with different models to detect EEG peaks in various applications. …”
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8
Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
Published 2020“…This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. …”
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Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: a comprehensive review
Published 2020“…This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. …”
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10
Wearable based-sensor fall detection system using machine learning algorithm
Published 2021“…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
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Proceeding Paper -
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Walking gait event detection based on electromyography signals using artificial neural network
Published 2019“…In many gait applications, the focal events are the stance and swing phases. Although detecting gait events using electromyography signals will help the development of assistive devices such as exoskeleton, orthoses, and prostheses, stance and swing phases have yet to be observed using electromyography signals. …”
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12
Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Methods of intrusion detection in information security incident detection: a comparative study
Published 2018“…These algorithms and methods provide fast and high rate of detection. …”
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14
Development of a wearable human fall detection system
Published 2022“…The accuracy of the fall detection algorithm was determined to be at 98.41%. …”
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15
Epileptic Seizure Detection Using Singular Values And Classical Features Of EEG Signals
Published 2014“…This project aims at developing an automated epileptic seizure event detection algorithm. …”
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Final Year Project -
16
Abnormal event detection in video surveillance / Lim Mei Kuan
Published 2014“…The third contribution aims to provide an integrated solution to detect multiple events in different regions-of-interest of a given scene. …”
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Thesis -
17
Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System
Published 2019“…Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. …”
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Conference or Workshop Item -
18
Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System
Published 2019“…Therefore, this paper aims to develop an effective and efficient network anomaly detection system by using distributed online averaged one dependence estimator (DOAODE) classification algorithm for multi-class network data to overcome these issues. …”
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19
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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20
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. …”
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