Search Results - normal distribution ((((using algorithm) OR (clustering algorithm))) OR (mining algorithm))
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1
An enhanced cluster head selection algorithm for routing in mobile AD-HOC network
Published 2017“…This thesis proposes a cluster based routing protocol, the Enhanced Cluster Routing Protocol (ECRP), which uses a modified cluster formation algorithm to build the cluster structure and select one of the nodes to be the cluster head. …”
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2
A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Furthermore, the results revealed a high convergence rate, upon which the algorithm’s performance was subjected to data clustering problems and investigated using six real datasets. …”
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3
Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation
Published 2020“…Next, samples are generated from Normal Distribution. To test the effectiveness of the proposed algorithm, the real and generated samples is added to training phase to build a prediction model using M5 Model Tree. …”
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4
Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model
Published 2013“…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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5
Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System
Published 2012“…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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6
Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods
Published 2025“…Five tables of summary for choosing appropriate clustering algorithms according to data distribution were produced. …”
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7
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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8
High Impedance Fault Detection on Power Distribution Feeder
Published 2012“…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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9
An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis
Published 2016“…However, frequent pattern mining (FPM) using Apriori-like algorithms and support-confidence framework suffers from the myth of rare item problem in nature. …”
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10
ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversarial WSNs
Published 2021“…A novel and efficient cluster head selection algorithm (ECHSA) is employed to improve the performance of the cluster head (CH) selection process in clustered WSN. …”
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11
Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. …”
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12
Clustering autism spectrum disorder student’s system based on intelligence, skills and behavior using agglomerative clustering algortihm / Daarin Nadia Nordin
Published 2020“…Data cleaning and data transformation is first carried out, followed by normalization through the Z-score method before being processed in the clustering model. …”
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13
Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems
Published 2021“…Such approach is highly dependable on the clustering algorithm parameterization, and is not capable to deal with the normal system’s specification changes. …”
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14
Dense-cluster based voting approach for license plate identification
Published 2018“…This paper presents a new method called Dense Cluster based Voting (DCV) for identifying an input license plate image as normal or taxi such that suitable recognition algorithms can be used to achieve better recognition rate. …”
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15
Evaluation of Clustering and Multi-hop Routing Protocols in Mobile Ad-hoc Sensor Networks
Published 2015“…One of the techniques to conserve more energy is through topology management using clustering network. A HEED (Hybrid, Energy-Efficient, Distributed) is one of the clustering algorithm for sensor networks. …”
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16
Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation
Published 2024“…Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. …”
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17
Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…For this purpose, the normal distributions are applied to each class. The parameters of this distribution are optimized by applying the proposed MOHA. …”
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18
Unsupervised chest X-ray opacity classification using minimal deep features
Published 2022“…A total of 3,504 CXRs were processed using a pre-trained deep learning convolutional neural network to output ten discriminatory features which were then used in the k-mean algorithm to find underlying similarities between the features for further clustering. …”
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19
Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. …”
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20
Improved performance in distributed estimation by convex combination of DNSAF and DNLMS algorithms
Published 2022“…Diffusion normalized least mean square (DNLMS) algorithm has low misadjustment error, but it is slow in convergence. …”
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