Search Results - (( pattern classification _ algorithm ) OR ( pattern detection method algorithm ))
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Pattern Recognition for Human Diseases Classification in Spectral Analysis
Published 2022“…Typically, pattern recognition consists of two components: exploratory data analysis and classification method. …”
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Integrated combined layer algorithm of jamming detection and classification in manet / Ahmad Yusri Dak
Published 2019“…This creates the jamming detection and classification parameters. The second stage is detecting jammers by integrating both lower layers by developing Integrated Combined Layer Algorithm (ICLA). …”
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Thesis -
3
Classification and detection of intelligent house resident activities using multiagent
Published 2013“…The intelligent home research requires understanding of the human behavior and recognizing patterns of activities of daily living (ADL).However instead of understand the psychosomatic nature of human early projects in this area simply employed intelligence to the household appliance.This paper proposed an algorithm for detecting ADL.The proposed method is based on two opposite state entity extraction.The method reflects on the common data flow of smart home event sequence.The developed algorithm clusters the smart home events by isolating opposite status of home appliance. …”
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Conference or Workshop Item -
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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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HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC
Published 2014“…This project proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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Final Year Project -
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Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm
Published 2023Subjects:Conference Paper -
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Immune Multiagent System for Network Intrusion Detection using Non-linear Classification Algorithm
Published 2010“…In this work, we integrate artificial immune algorithm with non-linear classification of pattern recognition and machine learning methods to solve the problem of intrusion detection in network systems. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Recently, various techniques based on different algorithms have been developed. However, the classification accuracy and computational cost are not satisfied. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The algorithm enhances the recognition ability of the system compared to manual extraction and labeling of pattern classes. …”
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HEp-2 cell images classification based on statistical texture analysis and fuzzy logic
Published 2014“…This paper proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. …”
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12
Alternate methods for anomaly detection in high-energy physics via semi-supervised learning
Published 2020“…We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. …”
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Non-invasive pathological voice classifications using linear and non-linear classifiers
Published 2010“…In the second experiment, the detection of the specific type of voice disorders has been carried out through twoclass pattern classification problems. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA
Published 2025“…However, these algorithms often fall short in consistently detecting and classifying network intrusions, particularly when distinctions between classes are subtle or when facing evolving attack patterns. …”
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15
Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns
Published 2023“…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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Random Undersampling on Imbalance Time Series Data for Anomaly Detection
Published 2023Conference Paper -
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Performance of Levenberg-Marquardt neural network algorithm in power quality disturbances classification / Adibah I’zzah Mohamad Kasim
Published 2025“…The increasing complexity of modern power systems necessitates advanced methods for detecting and classifying power quality disturbances (PQDs), which impact system reliability and equipment performance. …”
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An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing
Published 2014“…In this research, First and Second Order Statistics Feature Extraction, Mamdani Fuzzy Logic Classification methods utilized to develop automatic detection system for RA with the help of Matlab R2012b, Fuzzy Logic Toolbox, and Image Processing Toolbox. …”
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Final Year Project -
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Artificial immune system based on real valued negative selection algorithms for anomaly detection
Published 2015“…This shows that the Negative Selection Algorithms are equipped with the capabilities of detecting changes in data, thus appropriate for anomaly detection. …”
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Hybrid honey badger algorithm with artificial neural network (HBA-ANN) for website phishing detection
Published 2024“…Artificial Neural Network (ANN) is one popular method for website phishing detection. ANN is capable of detecting phishing websites by identifying patterns and characteristics connected to phishing websites through a network training phase. …”
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