Search Results - (( using adaptive method algorithm ) OR ( pattern classification based algorithm ))
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Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification
Published 2013“…Traditional training algorithms have some drawbacks such as local minima and its slowness.Therefore, evolutionary algorithms are utilized to train neural networks to overcome these issues.This research tackles the ANN training by adapting Mussels Wandering Optimization (MWO) algorithm.The proposed method tested and verified by training an ANN with well-known benchmarking problems.Two criteria used to evaluate the proposed method were overall training time and classification accuracy.The obtained results indicate that MWO algorithm is on par or better in terms of classification accuracy and convergence training time.…”
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Swarm negative selection algorithm for electroencephalogram signals classification
Published 2009“…The SNS classification model use negative selection and PSO algorithms to form a set of memory Artificial Lymphocytes (ALCs) that have the ability to distinguish between normal and epileptic EEG patterns. …”
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A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…The developed TSK type fuzzy inference engine is called modified adaptive fuzzy inference engine (MAFIE) and its parameters were then adjusted by the hybrid learning algorithm using adaptive neural network architecture towards improved performance which is called MANFIE. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA
Published 2025“…Furthermore, comparative analysis with existing methods in the field highlights the CNN-LSTM-SA model's ability to address scalability, adaptability and classification challenges. …”
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Adaptive Similarity Component Analysis in Nonparametric Dynamic Environment
Published 2011“…Data arrives from operational field in a stream model and similarity-based classification algorithms must identify them with acceptable performance. …”
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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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A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection
Published 2022“…For generating an interpretable deep architecture for identifying deep intrusion patterns, this study proposes an approach that combines ANFIS (Adaptive Network-based Fuzzy Inference System) and DT (Decision Tree) for interpreting the deep pattern of intrusion detection. …”
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Ensemble based categorization and adaptive model for malware detection
Published 2011“…Current malware detection method involved string search algorithm which based on the pattern detection. …”
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Neuro fuzzy classification and detection technique for bioinformatics problems
Published 2007“…It is very important to identify new integration of classification or clustering algorithm especially in neuro fuzzy domain as compared to conventional or traditional method. …”
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Adaptive resonance theory-based hand movement classification for myoelectric control system
Published 2014“…Five distinct hand movements are classified with an Adaptive Resonance Theory (ART) based neural network implemented, as it uses a combination of features extracted from four EMG signals. …”
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A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar
Published 2007“…This project focuses only on the development of an Artificial Neural Network (ANN) for the classification of Volatile Organic Compounds (VOCs). The ANNs that were used in this project were Multilayer Perceptron (MLP), Learning Vector Quantization (LVQ) and Adaptive Network Based Fuzzy Inference System (ANFIS). …”
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Hybrid Models Of Fuzzy Artmap And Qlearning For Pattern Classification
Published 2015“…The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. …”
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Development of electronic nose for classification of aromatic herbs using Artificial Intelligent techniques
Published 2018“…Two classification methods, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used in order to investigate the performance of classification accuracy for this E-nose system. …”
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The Potential of a Classification-based Algorithm to Calculate Calories in Real-Time Via Pattern Recognition
Published 2016“…While the algorithm helped to classify different types of wavelengths produced from the sensor, a classification-based algorithm via Pattern Recognition Method will be used to classify and match the food components. …”
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A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
Published 2010“…The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. …”
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Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…Pattern recognition/classification has received a considerable attention in engineering fields. …”
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A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. …”
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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…There are a lot of feature extraction methods and classification methods for iris classification. Classic local binary pattern (LBP) is one of the most useful feature extraction methods. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach
Published 2025“…The descriptive analysis examines purchasing behavior through correlation and regression analyses, while the predictive model uses decision trees (J48, Random Tree, REPTree), rule-based algorithms (JRip, OneR, PART), and clustering (K-Means) to identify patterns and predict trends. …”
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