Search Results - (( data distribution methods algorithm ) OR ( data selection methods algorithm ))
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1
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…Note that the 18-bus system is originally without any capacitor. Two selection methods that are used in Genetic Algorithm are the roulette wheel and tournament selections. …”
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Thesis -
2
Technical job distribution at BSD SHARP service center using combination of naïve Bayes and K-Nearest neighbour
Published 2022“…Meanwhile, k-NN algorithm is used to classify the experimental data. …”
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Proceeding Paper -
3
Extreme air pollutant data analysis using classical and Bayesian approaches
Published 2015“…In general, both methods are performing well for analyzing extreme model but numerical results show that MTM method performs slightly better than MH method in terms of efficiency and convergency to the stationary distribution. …”
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4
Algorithm enhancement for host-based intrusion detection system using discriminant analysis
Published 2004“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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5
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The performances of the newly proposed methods are investigated extensively by real data sets and simulations study. …”
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6
Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
Published 2017“…This method showed an accuracy of 65.7. However, the proposed method predicts the novel data with improved accuracy of 79.9. …”
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Article -
7
Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…The DPA-EHD is further modified by utilizing the pipelining parallelism to reduce the computing iterations and named as data parallel and pipelining algorithm (DPPA-EHD). …”
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Transient analysis for leak signature identification based on Hilbert Huang transform and integrated kurtosis algorithm for z-notch filter technique
Published 2018“…However, this method has the difficulty in selecting the suitable IMF for the next data post-processing method, which is Hilbert Transform (HT). …”
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10
A study on advanced statistical analysis for network anomaly detection
Published 2005“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Monograph -
11
An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis
Published 2022“…When the Gaussian mixture distribution algorithm is adopted, none of the criteria can consistently select features with the least number. …”
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Book Chapter -
12
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…Secondly, by utilizing the medoid property as being an actual data item in the dataset and with the aid of the proposed indexing method, the complexity of the computation is notably decreased. …”
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13
Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…This study investigates two different issues of performance measure in data classification problem. First, this study examines the use of accuracy measure as a discriminator for building an optimized Prototype Selection (PS) algorithm. …”
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14
Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction
Published 2024“…To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). …”
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Article -
15
Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…Then, by means of the Slantlet Transform theory, the matrix data extraction for any decomposition level are computed and the best of them are selected as input data to feed to an effective classifier. …”
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16
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…Algorithmic level based methods however are based on introducing new optimization task to improve the minority class classification rate, without changing the data characteristics. …”
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17
An intelligent DDoS attack detection tree-based model using Gini index feature selection method
Published 2023“…Our system achieved an overall accuracy of 98, outperforming baseline models that used more advanced algorithms such as Random Forest and XGBoost. Our enhanced Gini index feature selection method allowed us to select only 13 out of 45 security features, significantly reducing the data dimensionality and avoiding overfitting issues. …”
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Article -
18
Prediction of Total Suspended Sediments in Penang Waters Using Remote Sensing
Published 2004“…Out of the six algorithms derived, Algorithm 6 showed the best correlation with the ground-truth data (R2 value of 0.9755 and RMSE value of 4.0107). …”
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19
Modelling of heuristic distribution algorithm to optimize flexible production scheduling in Indian industry
Published 2020“…In the present work, Two Heuristic Algorithms are modelled and the best algorithm among those two Heuristics is selected after few comparisons 3M to 5M, this can optimize the scheduling processes up to 10x10 jobs i.e. 10 machines and 10 jobs. …”
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Conference or Workshop Item -
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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids
Published 2023“…Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. …”
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