Search Results - (( features extraction semantics algorithm ) OR ( java application using algorithm ))

Automatic multilevel medical image annotation and retrieval by Mueen, A., Zainuddin, R., Baba, M.S.

“…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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Acoustic Profiles Based On Quranic Maqamat Audio Features by Farah Hanim bt Seman @ Abd Jabar

An automatic grading model for semantic complexity of english texts using bidirectional attention-based autoencoder by Chen, Ruo Han, Ng, Boon Sim, Paramasivam, Shamala, Ren, Li

“…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 by Tan, Zhuoyi, Madzin, Hizmawati, Khalid, Fatimah, Beng, Ng Seng

“…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 by Yong, SP, Deng, JD, Purvis, MP

“…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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Facial image retrieval on semantic features using adaptive mean genetic algorithm by Shnan, Marwan Ali, Rassem, Taha H., Nor Saradatul Akmar, Zulkifli

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KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data by Aman, M., Abdulkadir, S.J., Aziz, I.A., Alhussian, H., Ullah, I.

“…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 by Sulaiman, Mohd Suffian, Nordin, Sharifalillah, Jamil, Nursuriati

“…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 by El-Alami, Fatima-Zahra, El Mahdaouy, Abdelkader, El Alaoui, Said Ouatik, En-Nahnahi, Noureddine

“…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 by Usmani, U.A., Happonen, A., Watada, J.

“…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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Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition by Mohd Zaki, Hasan Firdaus, Shafait, Faisal, Mian, Ajmal

“…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 by Ismail, .

“…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 by SARI, YUNITA SARI

“…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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Scene classification for aerial images based on CNN using sparse coding technique by Qayyum A., Malik A.S., Saad N.M., Iqbal M., Faris Abdullah M., Rasheed W., Rashid Abdullah T.A., Bin Jafaar M.Y.

Multi-level of feature extraction and classification for X-Ray medical image by Abdulrazaq, M, Alshaikhli, Imad Fakhri Taha, Mohd Noah, Shahrul Azman, Fadhil,, Moayad Al Athami

“…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 by Utomo, Fandy Setyo, Suryana, Nanna, Azmi, Mohd Sanusi

“…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 by Christian, Hans, Suhartono, Derwin, Chowanda, Andry, Kamal Z., Zamli

“…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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Multi-modality ontology semantic image retrieval with user interaction model / Mohd Suffian Sulaiman by Sulaiman, Mohd Suffian

“…Ontology can be seen as a knowledge base that can be used to improve the image retrieval process with the aim of reducing the semantic gap between visual features and high-level semantics. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm by Al-Saffar, Ahmed Ali Mohammed, Suryanti, Awang, Tao, Hai, Nazlia, Omar, Al-Saiagh, Wafaa, Al-bared, Mohammed

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