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Posted: 01 Feb 2022 02:00

“Natural Language Processing” January 2022 — summary from Zenodo and PubMed

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“Natural Language Processing” January 2022 — summary from Zenodo and PubMed main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.

Zenodo - summary generated by Brevi Assistant

Phishing is the most-used malicious attempt in which opponents, generally using emails, pose relied on entities or persons to obtain personal info from a target. Although phishing email assaults have been a well-known cybercriminal method for years, their usage has been increased over the last number of years due to the COVID-19 pandemic, where opponents manipulate people's consternation to lure victims. Recent phishing email discovery services that extract representational text-based functions from the e-mail's body have shown to be an ideal approach to dealing with these threats. The most effective combination in the balanced dataset was shown to be the Word2Vec with the Random Forest formula, while in the imbalanced dataset the Word2Vec with the Logistic Regression algorithm.

In this digital globe, experience sharing, understanding expedition, instructed posting and various other relevant social exploitations prevail for every individual along with social media/network such as FaceBook, Twitter, and so on play a crucial function in such types of activities. Many social media schemes give a capability to the users to push voice tweets and voice messages, to ensure that the voice messages may have some hazardous along with normal and essential contents. The association of such Deep Learning and Natural Language Processing supplies an effective technique to develop the powerful data processing model to recognize the emotional functions of the social networking medium. The voice tweets will be taken care by the NLP concepts and the text allowed tweets will be taken care of using deep learning principles, in which the voice tweets are extracted and made sure by the deep learning concept only.

The popularity of social media has been rising greatly in current times and hence, cyberbullying towards people has also increased at an alarming rate. Many cyberbullying texts can be discovered in the remark sections of many widely known Bangladeshi social media sites individualities YouTube videos. In this research study, we used natural language processing strategies and various machine learning classifiers and provided a model for cyberbullying detection in Bangla and Romanized Bangla texts obtained from YouTube video remarks. We accumulated 5000 Bangla remarks, as well as 7000 Romanized Bangla remarks from videos of various widely known social networks personals.

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PubMed - summary generated by Brevi Assistant

Integration of medical care records into a solitary application is still a challenging process. There are added concerns when data ends up being heterogeneous, and its application based on users does not seem the exact same. Finally, the monitoring results are sent out to the front end application and the worried customer mobile with sms message in their own native language for which translation package is being used.

We talked to 6 clinicians to find out about their live experience using electronic wellness records using a semi-structured meeting guide in an academic medical center in New York City from October to November 2016. The application of subject modeling to qualitative meeting information gives significant research insights into the medical professionals' live experience of EHR and future ideal EHR style to resolve human-computer interaction concerns in a severe treatment setting.

Social components of wellness are non-medical factors that can greatly impact patient health and wellness end results. Previous work on using natural language processing to automate removal of SDOH from text typically concentrates on an ad hoc choice of SDOH, and does not make use of the most recent breakthroughs in deep learning.

Background: Perceptions of cigarette, cannabis, and digital nicotine delivery systems are consistently advancing in the United States. In cannabis-specific subreddits, individual experiences with marijuana, cannabis legislation, health and wellness results of cannabis usage, methods and types of marijuana, and the cultivation of marijuana were typically reviewed subjects.

Extreme intricacy in the Human Leukocyte Antigens system and its nomenclature makes it challenging to translate and integrate appropriate info for HLA organizations with diseases, Adverse Drug Reactions and Transplantation. Recap information on scientifically relevant biomarkers connected to HLA disease associations with mapped susceptible/risk alleles is readily retrievable from HLASPREAD.

This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.

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The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents/contents.


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