Search Results - (( features extraction semantics algorithm ) OR ( java application optimized algorithm ))
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Automatic multilevel medical image annotation and retrieval
Published 2008“…Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. …”
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An automatic grading model for semantic complexity of english texts using bidirectional attention-based autoencoder
Published 2024“…This paper first analyzes the importance of automatic classification of semantic complexity in English text, and then builds an autoencoder structure based on bidirectional attention, which captures bidirectional information in text, and then uses the autoencoder structure for feature extraction and dimension reduction, which further strengthens the model’s ability to capture semantic complexity. …”
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Segmentation of pulmonary cavity in lung CT scan for tuberculosis disease
Published 2024“…The complexity of pulmonary tuberculosis (TB) lung cavity lesion features significantly increase the cost of semantic segmentation and labelling. …”
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Modelling semantic context for novelty detection in wildlife scenes
Published 2010“…Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. …”
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KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data
Published 2021“…The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. …”
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An object properties filter for multi-modality ontology semantic image retrieval
Published 2017“…Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic.The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description.In other words, there is a difference between the computational representation in machine and human natural language.In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. …”
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A deep autoencoder-based representation for Arabic text categorization
Published 2020“…It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. …”
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Enhanced Deep Learning Framework for Fine-Grained Segmentation of Fashion and Apparel
Published 2022“…We propose a clothing segmentation framework having novel feature extraction and fusion modules. The low-level feature data are extracted by the feature extraction module using Mask Region Convolutional Neural Network (RCNN) segmentation branches and Inception V3 used to extract the high-level semantic data. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition
Published 2017“…However, extracting discriminative features from multi-modal inputs, such as RGB-D images, in a unified manner is non-trivial given the heterogeneous nature of the modalities. …”
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Semantic focus fusion based on deep learning for deblurring effect
Published 2024“…The method is termed semantic focus fusion for deblurring effect. It employs deep learning architecture to extract focus and blurred features. …”
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APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN
Published 2011“…Further manual inspection during the experiments suggested that by using complete word and syntactical features or combination of these features with other features such as the semantic feature, would yield an improved result.…”
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Scene classification for aerial images based on CNN using sparse coding technique
Published 2023Article -
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Multi-level of feature extraction and classification for X-Ray medical image
Published 2018“…Specifically, this study proposed pertinent feature extraction algorithm for X-ray medical images and determined machine learning methods for automatic X-ray medical image classification. …”
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New Instances Classification Framework On Quran Ontology Applied To Question Answering System
Published 2019“…Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. …”
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Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging
Published 2021“…Secondly, these algorithms also lack the ability to capture the true (semantic) meaning of words; therefore, the context is slightly lost. …”
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