Search Results - (( process classification methods algorithm ) OR ( process classification based algorithm ))
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1
Knowledge base processing method based on text classification algorithm
Published 2023“…The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. …”
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Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…A method was developed to improve the cluster generation and clusters labelling processes in unsupervised classification of the Landsat TM and MASTER datasets. …”
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4
Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…If the feature extraction process fails to capture the correct information, the performance or accuracy of the classification algorithm will be negatively impacted. …”
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Overview of metaheuristic: classification of population and trajectory
Published 2010“…The algorithm techniques can be characterized based on the criteria of the operation of the search process. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…This process consists of four steps: pre-processing, segmentation, feature extraction, and classification. …”
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Image classification using two dimensional wavelet coefficients with parallel computing
Published 2020“…This method is capable to shorten the process time of the computation-intensive program in this research to process the live images frame by frame, which received from a laptop webcam to achieve real-time video processing. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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An extended oversampling method for imbalanced Quranic text classification based on a genetic algorithm
Published 2023“…However, the process of Quranic text classification is not without its challenges. …”
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Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…However, there is a need to explore more algorithms that can yield better classification performance. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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12
Breast cancer disease classification using fuzzy-ID3 algorithm based on association function
Published 2022“…This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.…”
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Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…The proposed method integrates colour and texture feature-based image analysis with machine learning algorithms for classification. …”
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14
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. …”
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15
Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. …”
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Lexicon-based and immune system based learning methods in Twitter sentiment analysis
Published 2016“…The aim of this article attempts to study the potential of this method in text classification for sentiment analysis.This study consists of three phases; data preparation; classification model development using three selected Immune System based algorithms i.e. …”
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Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network
Published 2020“…Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…These factors indirectly upset the disease prediction and classification accuracy of any ML model. To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. …”
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Mussels wandering optimization algorithmn based training of artificial neural networks for pattern classification
Published 2013“…Training an artificial neural network (ANN) is an optimization task since it is desired to find optimal neurons‘ weight of a neural network in an iterative training process. 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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