AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language

Habib, Maria; Faris, Mohammad; Alomari, Alaa; Faris, Hossam

doi:10.1109/access.2021.3115617

Cited by 21 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Word embedding is a method employed to map words from vocabulary to vectors of real numbers [23]. It represents words that encode semantic, statistic, or context information [24]. This method takes the corpus of text as input through the pre-processing stage and then produces a vector representation of each word in the word corpus as output.…”

Section: Word Embedding Methodsmentioning

confidence: 99%

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

Murdiyanto

Habibi

2022

Compiler

View full text Add to dashboard Cite

The volume of digital documents available online is growing exponentially due to the increasing use of the internet. Categorization of information obtained online is needed to make it easier for recipients of information to determine and filter which information is needed. Classification of web pages can be based on titles and descriptions, which are text data that can be done by utilizing deep learning technology for text classification. This study aimed to conduct data training and analysis experiments to determine the accuracy of the proposed deep learning architecture in classifying web page titles and descriptions. In this research, we proposed a Convolution Neural Network (CNN) architecture that generates few parameters. The training and evaluation set was conducted on the web page dataset provided by DMOZ. As a result, the proposed CNN architecture with the number of N (Dropout + 1D Convolution + ReLU activation) equal to 1 achieves the best validation accuracy. It achieves 79.51% with only generates 825,061 parameters. The proposed CNN architecture achieved outperformed performance on the accuracy of the five other technologies in the state-of-the-art.

show abstract

Section: Word Embedding Methodsmentioning

confidence: 99%

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

Murdiyanto

Habibi

2022

Compiler

View full text Add to dashboard Cite

show abstract

“…Another two publications analyzed Q-CHAT [77], [78], one study VABS (Vineland Adaptive Behavior Scales) [76] and [75] used customized ASD assessment dataset to classify ASD and TD children. Word2Vec algorithms [109] convert words to vectors, evaluate similarities, and group words logically, allowing processing of sizeable unstructured text repositories. In addition, LDA (Latent Dirichlet Allocation) [110] uses a prior Dirichlet distribution [111] matching word distributions with logical topics.…”

Section: ) Assessments Datasets and Emr Analysismentioning

confidence: 99%

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

2022

View full text Add to dashboard Cite

Background: Two-year delay is reported between the first developmental concern raised by the parents and the diagnosis of ASD (Autism Spectrum Disorder), delaying the start of early intervention programs most beneficial within the first three years. Aim: Evaluate the role of technology in ASD detection by answering four research questions analyzing 1) evolution of technology, 2) use of various bio-behavioral data sources, 3) demographic categories, databases, controls, comparators, and assessment instruments, and 4) data collection, processing, and outcomes of the technology-based methods in ASD detection. Methods: Scoping review included behavioral-based ASD screening and diagnostic studies, published between 1st January 2011 to 31st December 2021 in PUBMED, SCOPUS, and IEEE Xplore databases for children under six years. The studies were assessed using the Critical Appraisal Skills Programm (CASP) and the PRISMA scoping review checklist (PRISMA-ScR). Results: The shortlisted 35 studies were categorized into seven bio-behavioral categories. The review suggested extensive use of machine learning (ML) and Deep Learning (DL) technologies with multimodal structured and unstructured data to detect infants at risk of ASD and Other developmental delays (ODD) as early as 9 to 12 months. However, the review reported various internal and external validity threats. Conclusion: Technology can significantly improve the current ASD detection process. The validation and adoption of technology can be fast-tracked by 1) designing robust study protocols, 2) executing multicultural field trials, 3) standardizing datasets, data quality and feature engineering methods, 4) recruiting statistically significant participants from ASD, typically developing (TD) and other developmental disorders (ODD) groups to ensure technological generalization, validation, and adoption outside laboratory settings.

show abstract

“…In both CBOW and Skip-Gram, the predicted output is compared with the actual target. The loss function (e.g., Softmax) is computed followed by backpropagation with each epoch to update the embedding layer in the process [54].…”

Section: Feature Extractionmentioning

confidence: 99%

Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings

et al. 2022

Self Cite

View full text Add to dashboard Cite

Social networks facilitate communication between people from all over the world. Unfortunately, the excessive use of social networks leads to the rise of antisocial behaviors such as the spread of online offensive language, cyberbullying (CB), and hate speech (HS). Therefore, abusive\offensive and hate detection become a crucial part of cyberharassment. Manual detection of cyberharassment is cumbersome, slow, and not even feasible in rapidly growing data. In this study, we addressed the challenges of automatic detection of the offensive tweets in the Arabic language. The main contribution of this study is to design and implement an intelligent prediction system encompassing a two-stage optimization approach to identify and classify the offensive from the non-offensive text. In the first stage, the proposed approach fine-tuned the pre-trained word embedding models by training them for several epochs on the training dataset. The embeddings of the vocabularies in the new dataset are trained and added to the old embeddings. While in the second stage, it employed a hybrid approach of two classifiers, namely XGBoost and SVM, and a genetic algorithm (GA) to mitigate the drawback of the classifiers in finding the optimal hyperparameter values to run the proposed approach. We tested the proposed approach on Arabic Cyberbullying Corpus (ArCybC), which contains tweets collected from four Twitter domains: gaming, sports, news, and celebrities. The ArCybC dataset has four categories: sexual, racial, intelligence, and appearance. The proposed approach produced superior results, in which the SVM algorithm with the Aravec SkipGram word embedding model achieved an accuracy rate of 88.2% and an F1-score rate of 87.8%.

show abstract

AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language

Cited by 21 publications

References 33 publications

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings

Contact Info

Product

Resources

About