Search Results - (( gram extraction methods algorithm ) OR ( parallel estimation path algorithm ))
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1
Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
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2
Effective query structuring with ranking using named entity categories for XML retrieval
Published 2016“…The method employs Semantic Tags Extraction (STSE) algorithm to extract semantic tags of an element and Element Enrichment (EERM) algorithm to enrich the elements. …”
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3
Reduced Set Kernel Principal Component Analysis (Rskpca) Algorithm for Palm Print Based Mobile Biometric System
Published 2015“…A new approach in feature extraction called Reduced-Set Kernel Principal Component Analysis (RSKPCA) is proposed to speed up the processing in feature extraction. …”
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4
Intelligent deep machine learning cyber phishing URL detection based on BERT features extraction
Published 2022“…Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. …”
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5
Magnetic resonance imaging sense reconstruction system using FPGA / Muhammad Faisal Siddiqui
Published 2016“…Parallel imaging is a robust method for accelerating the data acquisition in Magnetic Resonance Imaging (MRI). …”
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6
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Therefore, the LiDAR derived data were combined with WV-3 image using different fusion methods such as layer stacking (LS), Gram–Schmidt (GS), and PC spectral sharpening (PCSS). …”
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7
Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study
Published 2018“…Methods: For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. …”
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Semi-supervised learning for sentiment classification with ensemble multi-classifier approach
Published 2022“…The research went through pre-processing, vectorization, and feature extraction using TF-IDF and n-grams. Support Vector Machine (SVM) or Random Forest for tokenization was used to separate unigram, bigram, and trigram in model generation. …”
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