Multi-corpus-Based Model for Measuring the Semantic Relatedness in Short Texts (SRST)

El-Deeb, Reem; Al-Zoghby, Aya M.; Elmougy, Samir

doi:10.1007/s13369-018-3232-0

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fusion of learning models: to integrate the multiple learning models into a classifier, we have considered two approaches: (1) model fusion, which has been finally used in the proposed model, and (2) data fusion, which have been used in the existing studies for other problems: measuring the semantic relevance of short texts [63] and detection of the adverse drug reaction [64]. We have also considered data fusion for the model fusion.…”

Section: Discussionmentioning

confidence: 99%

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

2020

View full text Add to dashboard Cite

Detecting cybersecurity intelligence (CSI) on social media such as Twitter is crucial because it allows security experts to respond cyber threats in advance. In this paper, we devise a new text classification model based on deep learning to classify CSI-positive and -negative tweets from a collection of tweets. For this, we propose a novel word embedding model, called contrastive word embedding, that enables to maximize the difference between base embedding models. First, we define CSI-positive and -negative corpora, which are used for constructing embedding models. Here, to supplement the imbalance of tweet data sets, we additionally employ the background knowledge for each tweet corpus: (1) CVE data set for CSI-positive corpus and (2) Wikitext data set for CSI-negative corpus. Second, we adopt the deep learning models such as CNN or LSTM to extract adequate feature vectors from the embedding models and integrate the feature vectors into one classifier. To validate the effectiveness of the proposed model, we compare our method with two baseline classification models: (1) a model based on a single embedding model constructed with CSI-positive corpus only and (2) another model with CSI-negative corpus only. As a result, we indicate that the proposed model shows high accuracy, i.e., 0.934 of F1-score and 0.935 of area under the curve (AUC), which improves the baseline models by 1.76∼6.74% of F1-score and by 1.64∼6.98% of AUC.

show abstract

Section: Discussionmentioning

confidence: 99%

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

2020

View full text Add to dashboard Cite

show abstract

“…The results of this study revealed that selenium nanoparticles in concentrations of 10 and 15 μg/ml created an inhibition diameter of 25 and 31 mm, respectively, and in the investigation of synergistic effects with antibiotics norfloxacin, ampicillin, sulfamethoxazole, penicillin V, penicillin G, and gentamicin had 0%, 24%, 14.3%, 100%, 100%, and 30.7% increase in inhibition diameter, respectively. 41 In a study, Fozouni et al 42 examined the effects of gold nanoparticles in combination with the antibiotic erythromycin against erythromycin-resistant S. agalactia isolated from pregnant women. In this study, the well diffusion method was used to investigate the antibacterial effects of gold nanoparticles together with erythromycin.…”

Section: Chemical Nanoparticlementioning

confidence: 99%

Nanotechnology, a novel strategy in detectingStreptococcus agalactiae, and its antibacterial, antibiofilm, and antiquorum sensing properties

Dousari,

Amirheidari,

Dousari

et al. 2023

Applied Organom Chemis

View full text Add to dashboard Cite

Streptococcus agalactiae or group B streptococcus is the cause of various infections in pregnant women and non‐pregnant adults with immunodeficiency, especially diabetics and infants. This bacterium can cause serious infections such as endocarditis, septicemia, meningitis, pneumonia, and urinary tract infections with a high mortality rate. Nowadays, the use of nanoparticles can play a central role in the management of such diseases due to their remarkable biocompatibility and distinct physicochemical characteristics. Nanoparticles are synthesized by various methods such as biological, chemical, and physical, and each has its own unique properties. Nanoparticles have antibacterial, anti‐biofilm, and antiquorum sensing properties in in vitro and in vivo conditions on human pathogens through different pathways. Different nanoparticles displayed that in addition to detecting S. agalactiae, it has antibacterial, antibiofilm, and antiquorum sensing effects on this bacterium. Therefore, the purpose of this review is to investigate different nanoparticles for the detection and effects of antibacterial, anti‐biofilm, and anti‐infestation assays against the human pathogen S. agalactiae.

show abstract

Sentence Embedding and Convolutional Neural Network for Semantic Textual Similarity Detection in Arabic Language

Mahmoud

Zrigui

2019

Arab J Sci Eng

View full text Add to dashboard Cite

Multi-corpus-Based Model for Measuring the Semantic Relatedness in Short Texts (SRST)

Cited by 4 publications

References 8 publications

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

A New Text Classification Model Based on Contrastive Word Embedding for Detecting Cybersecurity Intelligence in Twitter

Nanotechnology, a novel strategy in detectingStreptococcus agalactiae, and its antibacterial, antibiofilm, and antiquorum sensing properties

Sentence Embedding and Convolutional Neural Network for Semantic Textual Similarity Detection in Arabic Language

Contact Info

Product

Resources

About