Results 161 to 170 of about 48,962 (256)

Goffman's Tempting the Devil in Stigma: A Close Rereading of Goffman's “Subversive” Construct of “the Normals” Versus “the Stigmatized”

open access: yesSymbolic Interaction, EarlyView.
This article engages in a close rereading of Stigma, relating it to Goffman's biography as a person who faced stigma. Exploring his biography helps us to recognize the ways Goffman applies stigma strategies in how he represents himself as the author in Stigma.
Thaddeus Müller
wiley   +1 more source

Distinct Nasal Microbiome Profiles and Prediction Model for Allergic Rhinitis, Nonallergic Rhinitis, and Healthy Children

open access: yesWorld Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
The alpha and beta diversity of the nasal microbiome differed among children with allergic rhinitis (AR), nonallergic rhinitis (NAR), and healthy controls (HCs). Compared to HC, AR had more Escherichia‐Shigella, Negativicoccus, and Campylobacter, while NAR had more Dolosigranulum and fewer Enterobacteriaceae.
Kantima Kanchanapoomi   +6 more
wiley   +1 more source

Hearing and Cognitive Function in Aged Adults: A Machine Learning and Mediation Analysis of NHANES

open access: yesWorld Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
ABSTRACT Objective Cognitive impairment is a major public health challenge. Early identification is crucial, especially in the often‐overlooked 60–69 age group. This study developed an efficient risk identification tool. Methods Using data from the US National Health and Nutrition Examination Survey (NHANES), three machine learning algorithms screened ...
Jia Luo   +7 more
wiley   +1 more source

Label dependency modeling in Multi-Label Naïve Bayes through input space expansion. [PDF]

open access: yesPeerJ Comput Sci
Chitra P   +3 more
europepmc   +1 more source

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam   +3 more
wiley   +1 more source

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