Search Results - (( data distribution detection algorithm ) OR ( parallel distribution modified algorithm ))*
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1
Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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2
A modified artificial neural network (ANN) algorithm to control shunt active power filter (SAPF) for current harmonics reduction
Published 2013“…The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. …”
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3
Parallel Diagonally Implicit Runge-Kutta Methods For Solving Ordinary Differential Equations
Published 2009“…All algorithms are written in C language and the parallel code is implemented on Sun Fire V1280 distributed memory system. …”
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4
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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5
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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6
Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer
Published 2023“…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. …”
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7
High Impedance Fault Detection on Power Distribution Feeder
Published 2012“…The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power system.…”
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8
Broken Conductor Detection on Power Distribution Feeder
Published 2013“…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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10
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model
Published 2013“…This paper presents the algorithm for HIF detection based on the amplitude of third and fifth harmonics of current, voltage and power. …”
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An eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in khyber-pakhtunkhwa, Pakistan
Published 2018“…Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. …”
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An eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in khyber-pakhtunkhwa, Pakistan
Published 2018“…Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. …”
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13
Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. …”
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Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi
Published 2020“…The system is simulated using EPANET (Environmental Prediction Agency Network) software based on actual water distribution system (WDS) data from Benghazi city, Libya. …”
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15
Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…One of the challenging issues for a grid-connected distributed generation is to find a suitable technique to detect an islanding problem. …”
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16
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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17
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System
Published 2012“…The algorithm for HIF detection based on the amplitude ratio of second and odd harmonics to fundamental is presented. …”
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Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)
Published 2013“…It achieves faster time execution and cost minimization than that other replication processes. This algorithm also introduces a multi thread based persistence layer, which supports early binding and parallel connection to the servers. …”
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Efficient Machine Learning Model for DDoS Detection System Based on Dimensionality Reduction
Published 2022“…Researchers seek to develop intelligent algorithms to detect and prevent DDoS attacks. The present study proposes an efficient DDoS attack detection model. …”
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