Search Results - (( variable detection based algorithm ) OR ( data validation using algorithm ))
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Fault Detection Relevant, Neural Network and Evolutionary Algorithm based Model for a Single-shaft Industrial Gas Turbine
Published 2009“…Real operation data collected in 10 seconds interval are used for training as well as validation of the model. …”
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Evaluation of outlier filtering algorithms for accurate measurement of travel time reliability incorporating lane-splitting situations / Obada M. A. Asqool
Published 2022“…Therefore, this study aims to investigate the best algorithm for data filtration and how to use it to obtain accurate data for measuring TTR. …”
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3
Chemometrics analysis for the detection of dental caries via ultraviolet absorption spectroscopy / Katrul Nadia Basri
Published 2023“…The spectra were split into calibration and validation data using stratified random sampling for each of ICDAS class by a ratio of 80:20. …”
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4
Haar cascade algorithm for microsleep detection
Published 2025“…During the development phase, the system was built in Python using OpenCV and dlib for real-time facial analysis and the Haar Cascade algorithm for efficient facial feature detection. …”
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Heartbeat Anomaly Detection Method Based on Electrocardiogram using Improved Certainty Cognitive Map
Published 2023“…This research has 3 objectives To develop algorithms for detecting heart conditions as either abnormal or normal using the modified cognitive map (CM) approach, To develop detection algorithms for anomalous heart conditions based on the enhancement of Certainty Factor (CF) technique, and To evaluate and validate the effectiveness of the new proposed model specifically the Certainty Cognitive Map (CCM) for identifying heart defects. …”
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6
A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines
Published 2017“…Two back-propagation training algorithms, namely the Levenberg–Marquardt and Bayesian regularization algorithms, and the k-fold cross-validation technique, were employed to train the optimal networks using a training data set. …”
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7
A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping
Published 2014“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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Power System State Estimation In Large-Scale Networks
Published 2010“…Hence. SE requires to use various techniques to ensure validity of the results and to detect and identify sources of errors. …”
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10
Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). …”
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11
Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients
Published 2017“…This is followed by the clustering of the liver tissues using particle swarm optimized spatial FCM algorithm. …”
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12
Identification of debris flow initiation zones using topographic model and airborne laser scanning data
Published 2017“…MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. …”
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13
Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study
Published 2019“…Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. …”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…However, the weighted score function (W) shows better result compared to the censored complete data (CC). Residual-based approaches are used to assess the validity of the two models (MW, CC) assumptions. …”
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15
Observer-based fault detection with fuzzy variable gains and its application to industrial servo system
Published 2020“…The proposed fault detection algorithm employs a fuzzy logic-based approach with the objective of finding the appropriate observer gains that could cope with the different working conditions. …”
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Comparing seabed roughness result from QPS fledermaus software, benthic trrain modeler [BTM] and developed model derived FRM slope variability algorithm for hard coral reef detecti...
Published 2018“…In this study, several models has been created which are from QPS Fledermaus model, BTM model and Slope Variability model. Slope variability model is an algorithm that is being used for detecting terrain roughness. …”
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17
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…The proposed algorithm has been applied to detect outliers in the high dimensional data. …”
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18
Minimization of Test Cases and Fault Detection Effectiveness Improvement through Modified Reduction with Selective Redundancy Algorithm
Published 2007“…Then the algorithm gathers all the test cases based on the definition occurrence and def-use pair if they cover same definition occurrence of one variable but they don’t cover def-use pair of the same variable. …”
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19
Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…In an imbalanced dataset, one of the two classes contains fewer total samples than the other class. The sampling-based method, also known as the data level method, is used to deal with this problem. …”
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20
Fault diagnostic algorithm for precut fractionation column
Published 2004“…Hazard and Operability Study (HAZOP) is used to support the diagnosis task. The algorithm has been successful in detecting the deviations of each variable by testing the data set. …”
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