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Sentiment analysis for Arabizi in social media. (c2015)
Published 2015“…Informal Arabic lacks standardization and has no grammar, thus sentimental analysis in this area is considered a complex process. Sentiment Analysis for Arabic has been studied for MSA (Modern Standard Arabic) but rarely for informal Arabic, and non-existent for Arabizi; whereas most of the youth in Lebanon text in Arabizi claiming that it is easier than texting in Arabic. …”
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masterThesis -
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Machine Learning–Based Approach for Identifying Research Gaps: COVID-19 as a Case Study
Published 2024“…</p><h3>Methods</h3><p dir="ltr">We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. …”
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An enhanced k-means clustering algorithm for pattern discovery in healthcare data
Published 2015“…The huge amounts of data generated by media sensors in health monitoring systems, by medical diagnosis that produce media (audio, video, image, and text) content, and from health service providers are too complex and voluminous to be processed and analyzed by traditional methods. …”
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Exploring Sentiment Analysis using Different Machine Learning Algorithms on Dialectal Arabic
Published 2021“…The study explores sentiment analysis using different machine learning algorithms on dialectal Arabic text dataset. In this study, we used twitter as our data source. …”
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XBeGene: Scalable XML Documents Generator by Example Based on Real Data
Published 2012“…Inspired by the query-by-example paradigm in information retrieval, Our generator system i)allows the user to provide her own sample XML documents as input, ii) analyzes the structure, occurrence frequencies, and content distributions for each XML element in the user input documents, and iii) produces synthetic XML documents which closely concur, in both structural and content features, to the user's input data. …”
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conferenceObject -
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Artificial intelligence-based methods for fusion of electronic health records and imaging data
Published 2022“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”
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A Multi-Channel Convolutional Neural Network approach to automate the citation screening process
Published 2021“…This study aims to automate the citation screening process using Deep Learning algorithms. With this, it is aimed to reduce the time and costs of the citation screening process and increase the precision and recall of the relevant primary studies. …”
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Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
Published 2021“…The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate between health and cancerous patient samples. …”
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Random Forest Bagging and X‐Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data
Published 2021“…<p dir="ltr">In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. …”
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Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis
Published 2023“…Deep Learning (DL) algorithms have shown wonders in many Natural Language Processing (NLP) tasks such as language-to-language translation, spam filtering, fake-news detection, and comprehension understanding. …”
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Brain Source Localization in the Presence of Leadfield Perturbations
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doctoralThesis -
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Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval
Published 2024“…In this paper, the proposed algorithm selects relevant observations from the original data set by utilizing a class interval technique (i.e. histogram) to maintain a bunch of representative samples from each bin. …”