Search Results - (( variable detection based algorithm ) OR ( variable generation using algorithm ))
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1
Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
Published 2011“…Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) and variable-strength testing strategies. …”
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Thesis -
2
Artificial immune system based on real valued negative selection algorithms for anomaly detection
Published 2015“…The Real-Valued Negative Selection Algorithms, which are the focal point of this research, generate their detector sets based on the points of self data. …”
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3
Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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4
An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
Published 2018“…The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. …”
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5
MotionSure: a cloud-based algorithm for detection of injected object in data in motion
Published 2017“…Total 1600 different type of files were used for evaluation of the proposed algorithm, results show that 87% of injected objects were correctly detected…”
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Proceeding Paper -
6
Optimized differential evolution algorithm for linear frequency modulation radar signal denoising
Published 2013“…We propose an optimized crossover scheme that changes the crossover operation from being fixed-length to random-length, which has been designed to fit for the proposed variable length DE. We refer to the new DE algorithm as random variable length crossover DE (rvlx- DE) algorithm. …”
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7
Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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8
Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
Published 2011“…Multivariate analysis technique i.e., Principal Component Analysis (PCA) and Partial Correlation analysis (PCorrA) are used to determine the correlation coefficients between the process variables and quality variables while control chart with the calculated correlation coefficients are used to facilitate the Fault Detection and Diagnosis (FDD) algorithm. …”
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9
Damage visualization based on frequency shift of single-mode ultrasound-guided wavefield
Published 2022“…Last but not least, the comparison of accuracy between frequency analysis algorithm and variable time window amplitude mapping (VTWAM) algorithm is performed. …”
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10
Survey on input output relation based combination test data generation strategies
Published 2015“…Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR.…”
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11
Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The first algorithm locates interest points in food images using an MSER. …”
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12
HABC: Hybrid artificial bee colony for generating variable T-way test sets
Published 2020“…This paper proposed a hybrid artificial bee colony (HABC) strategy based on the hybrid artificial bee colony algorithm and practical swarm optimization to generate optimal test suite of variable strength interaction. …”
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13
HABC: Hybrid artificial bee colony for generating variable T-way test sets
Published 2020“…This paper proposed a hybrid artificial bee colony (HABC) strategy based on the hybrid artificial bee colony algorithm and practical swarm optimization to generate optimal test suite of variable strength interaction. …”
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14
Survey on input output relation based combination test data generation strategies
Published 2015“…Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR.…”
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15
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…This improved algorithm, known as the edge-template Contour Delineation (etCD), is based on the fusion of edge detection with corner-template detection and dynamic thresholding to produce enhanced edge map. …”
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16
Fault detection and diagnosis using correlation coefficients
Published 2005“…This research is to formulate a FDD algorithm based on MSPC via correlation coefficients. …”
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17
Fault detection and diagnosis using rule-based support system on fatty acid fractionation column
Published 2003“…Identification of causes, consequences and suggested actions for each deviation assisted by Hazard and Operability Study (HAZOP) analysis are generated into rule-based algorithm. Forward chaining strategy is used to interpret the rule-based system. …”
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18
DEVELOPMENT OF OE-BASED BROWN-FORSYTHE TEST ALGORITHM FOR CONTROL VALVE STICTION DETECTION
Published 2018“…Therefore, the main objective of the project is to develop an OE-based Brown-Forsythe test algorithm to effectively detect the presence of control valve stiction.In this study, the proposed OE model is developed using System Identification in MATLAB, where it is used to simulate the process output (PV). …”
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Final Year Project Report / IMRAD -
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Vibration analysis for early detection of bearing failures
Published 2024“…Bearing failures are a leading cause of rotating machinery failures in industries such as oil and gas, manufacturing, and power generation. Early detection of bearing failures using Predictive Maintenance (PdM) is critical for minimizing downtime and optimizing maintenance strategies. …”
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Final Year Project / Dissertation / Thesis -
20
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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