Search Results - (( pattern extraction method algorithm ) OR ( learning classification based algorithm ))
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1
Herbs recognition based on chemical properties using machine learning algorithm
Published 2023“…This method has demonstrated promising results in identifying herb species, and the classification method based on machine learning algorithms has proven successful in recognizing and distinguishing herb species…”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The RNN was used to detect patterns present in satellite image. A novel feature extraction algorithm was developed to extract the feature vectors. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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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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Identifying melanoma characteristics using directional imaging algorithm and convolutional neural network on dermoscopic images / Mohammad Asaduzzaman Rasel
Published 2024“…Multiple deep-learning models are proposed for segmentation. Several imaging, computer vision, and pattern recognition algorithms are employed to describe five dermoscopic features. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Systematic review for phonocardiography classification based on machine learning
Published 2023“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
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Plant recognition based on identification of leaf image using image processing / Nor Silawati Sha’ari
Published 2018“…In this paper, by using the database available in the internet and using Neural Network (NN) as training algorithm, plant recognition based on leaves image would be developed. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…Classification is a data analysis that extracts a model which describes an important data classes. …”
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A review on classifying and prioritizing user review-based software requirements
Published 2024“…Investigating the potential of emerging machine learning models and algorithms to improve classification and prioritization accuracy is crucial. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. …”
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The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN
Published 2021“…In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. …”
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STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION
Published 2021“…A boost ensemble learning algorithm using Support Vector Machines (SVM) as initial classifiers and Artificial Neural Networks (ANN) as a final classifier is employed to learn the patterns of different skin lesion class features. …”
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Systematic Review for Phonocardiography Classification Based on Machine Learning
Published 2023“…This systematic review aims to examine the existing literature on phonocardiography classification based on machine learning, focusing on algorithms, datasets, feature extraction methods, and classification models utilized. …”
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Classification of stock market index based on predictive fuzzy decision tree
Published 2005“…In particular, predictive FDT algorithm is based on the concept of degree of importance of attribute contributing to the classification. …”
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…The existing CNN based approaches can only capture the similar region-wise patterns within an image but not the structural patterns of a plant composed of varying number of plant views images composed of one or more organs. …”
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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VGG16-based deep learning architectures for classification of lung sounds into normal, crackles, and wheezes using Gammatonegrams
Published 2023“…In this study, we conducted a comparison of two versions of the VGG16-based deep learning model for breathing sound classification using Gammatonegrams as input. …”
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Ontology enrichment with causation relations
Published 2014“…The proposed framework extracts initial semantic patterns for causation relation from the input samples, then filters these patterns using two novel algorithms, namely, the “Purpose Based Word Sense Disambiguation” which helps in determining the causation senses for input pair of words and the “Graph Based Semantics” which determines the existence of the causation relations in the sentence and to extract their cause-effect parts. …”
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