Search Results - (( data normalization based algorithm ) OR ( parameter realization path algorithm ))
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All-pass filtered x least mean square algorithm for narrowband active noise control
Published 2018“…The results also show that the proposed method outperforms other LMS algorithm without secondary path modelling. The proposed narrowband LMS algorithm would benefit in the design of efficient feedforward ANC system that can realize noise control in air intake duct applications.…”
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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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Surface defect detection and polishing parameter optimization using image processing for G3141 cold rolled steel
Published 2016“…For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. …”
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Classification of Cardiac Disorders Based on Electrocardiogram Data with Fuzzy Cognitive Map (FCM) Algorithm Approach
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The effect of dose calculation algorithms on the normal tissue complication probability values of thoracic cancer
Published 2015“…Purpose: To identify the effect of dose calculation algorithms on the Normal Tissue Complication Probability values of thoracic cancer. …”
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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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Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Published 2014“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma
Published 2017“…In addition, the algorithm was relatively easy to understand compare to the state of the art of normal parameter reduction algorithm. …”
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…Even a normal people using clustering to grouping their data. …”
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An improved recommender system based on normalization of matrix factorization and collaborative filtering algorithms
Published 2015“…The hypothesis is that the tendency of normalization technique to simplify the data combined with the accuracy of the neighborhood models can improve the accuracy of the RS. …”
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Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream
Published 2023“…Existing clustering algorithms for outlier detection encounter significant challenges due to insufficient data pre-processing methods and the absence of a suitable data summarization framework for effective data stream clustering. …”
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Pengkelasan Sel Kanser Pangkal Rahim Kepada Sel Normal Dan Tidak Normal Menggunakan Analisis Pembezalayan Dan Rangkaian Neural
Published 2006“…The system is built to classify some certain data into two classes, which are normal or abnormal cells. …”
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A clustering-based method for outlier detection under concept drift
Published 2024“…However, challenges may arise from assuming the majority of data comprises normal instances, particularly during sudden spikes in attack data, potentially diminishing algorithm effectiveness. …”
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Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Network "bursting" is a normal event in internet traffic engineering, but the bustiness of throughput data will violent the committed rate that provided by ISP. …”
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Short term forecasting based on hybrid least squares support vector machines
Published 2018“…This study assesses the performance of each hybrid algorithms based on three statistical indices viz. Mean Square Error (MSE), Root Mean Square Percentage Error (RMSPE) and Theil’s U which is realized on raw and normalized data set. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…The obtained results were compared with the BH and previous optimization algorithms for both test functions as well as data clustering in terms of normal and high dimensional datasets. …”
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