Search Results - (( java application using algorithm ) OR ( event detection using algorithm ))

Real time power quality event detection using continuous wavelet transform by Tan R.H.G., Ramachandaramurthy V.K.

“…The proposed algorithm is also capable to perform voltage sag and voltage transient classification for event count upon event detection. …”

High impedance fault detection and identification based on pattern recognition of phase displacement computation by Ali, Mohd Syukri, Bakar, Ab Halim Abu, Tan, Chia Kwang, Arof, Hamzah, Mokhlis, Hazlie

“…There are two stages in this algorithm. In the first stage, the pattern of the PDC is analyzed to detect the occurrence of an event. …”
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Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis by Kabir Ahmad, Farzana, Kamaruddin, Siti Sakira, Yusof, Yuhanis, Yusoff, Nooraini

“…For learning and predicting the event patterns, dynamic Bayesian network (DBN) with Hidden Markov Model (HMM) and heuristic search learning algorithms have been a popular technique used in which structure learning is trained to classify complex events pattern. …”
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High Impedance Fault Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

“…This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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Broken Conductor Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

“…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model by Sulaiman , Marizan, Adnan, Tawafan, Ibrahim, Zulkifilie

“…This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. …”
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Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System by Tawafan, Adnan, Sulaiman , Marizan, Ibrahim, Zulkifilie

“…This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

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Improving EEG Signal Peak Detection Using Feature Weight Learning of a Neural Network with Random Weights for Eye Event-Related Applications by Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin

“…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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Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

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Test Data Generation for Event Driven System Using Bees Algorithm by Mohammed Zabil, Mohd H., Kamal Z., Zamli

“…For an event driven system, the order of the event sequence should also be tested to detect failure in any possible sequences of the event. …”
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Walking gait event detection based on electromyography signals using artificial neural network by Nazmi, Nurhazimah, Abdul Rahman, Mohd Azizi, Yamamoto, Shin Ichiroh, Ahmad, Siti Anom

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

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Leveraging Web Scraping in Predictive Modelling of Supply Risk Detection by Ng, Shi Ya

“…By collecting data from news sites, this system is aimed to provide data for predictive modelling so it can be used to detect patterns in supply risks to create and predict the probability of a temporary event occurring in the future…”
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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

“…In this study, an algorithm was developed, to detect the damaging events in the original fatigue signal. …”
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Wearable based-sensor fall detection system using machine learning algorithm by Ishak, Anis Nadia, Habaebi, Mohamed Hadi, Yusoff, Siti Hajar, Islam, Md. Rafiqul

“…In this project, a wearable sensor-based fall detection system using a machine-learning algorithm had been developed. …”
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Data redundancy reduction scheme for data aggregation in wireless sensor network by Adawy, Mohammad Ibrahim

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

“…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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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

“…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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