Search Results - (( pattern detection method algorithm ) OR ( feature selection method algorithm ))*
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1
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In comparison to different single algorithms for feature selection,experimental results show that the proposed ensemble method is able to reduce dimensionality, the number of irrelevant features and produce reasonable classifier accuracy. …”
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2
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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3
Swarm negative selection algorithm for electroencephalogram signals classification
Published 2009“…Such automated systems must rely on robust and effective algorithms for detection and prediction. Approach: The proposed detection system of epileptic seizure in EEG signals is based on Discrete Wavelet Transform (DWT) and Swarm Negative Selection (SNS) algorithm. …”
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4
Pairwise clusters optimization and cluster most significant feature methods for anomaly-based network intrusion detection system (POC2MSF) / Gervais Hatungimana
Published 2018“…The unrecognizable IDS; IDS which is neither HIDS nor NIDS is the consequence of using statistical methods for features selection. The speed, memory and accuracy of IDS are affected by inappropriate features reduction method or ignorance of irrelevant features. …”
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5
P300 detection of brain signals using a combination of wavelet transform techniques
Published 2012“…This research is proving the power of combination of discrete and continuous wavelet transform for achieving high accuracy in single trial detection and visualization of P300. Meanwhile the new approaches in channels selection methods help the algorithms for convenient online usage.…”
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Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain
Published 2019“…Second, the MFO algorithm was utilised to select the optimal discriminative features. …”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat
Published 2021“…Size was extracted using Extreme Point Detection algorithm and Hit or Miss Transformation method was used to extract the stroke formation pattern. …”
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8
Hereditary ratio of adolescent to parent based on lips analysis using canny edge detection / Nor Shamimi Kharuddin
Published 2010“…This process significantly reduces the amount of data in the image, while preserving the most important structural features of that image. Canny edge detection is considered to be the ideal edge detection algorithm for images that are corrupted with white noise. …”
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9
Non-invasive pathological voice classifications using linear and non-linear classifiers
Published 2010“…Two types of experiments are conducted using the proposed feature extraction and classification algorithms. …”
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10
Adaptive feature selection for denial of services (DoS) attack
Published 2017“…In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the most important and relevant features related DDoS attacks. …”
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11
Feature extraction: hand shape, hand position and hand trajectory path
Published 2011“…There is no algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. …”
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12
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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18
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. …”
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Heart disease prediction using artificial neural network with ADAM optimization and harmony search algorithm
Published 2025“…Complementing this, the Harmony Search Algorithm (HSA) is incorporated to augment data features, facilitating better pattern recognition and enhancing overall classification accuracy through optimized feature engineering. …”
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Seismic attribute feasibility study for fault and fracture analysis and integration with spectral decomposition: Application in Sarawak basin
Published 2019“…Seismic attributes help interpreters to highlight details focusing on the geological features of interest in seismic data. However, there are some limitations in the performance of these tools, as the algorithms are dependent on the seismic survey parameters, quality of the data and its existing patterns, and geology of the study area. …”
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