Performance analysis of image retrieval system using deep learning techniques.

The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results.

Myelin oligodendrocyte glycoprotein antibody-associated disease mimicking neuromyelitis optica spectrum disorder.

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders (NMOSD) are two rare autoimmune inflammatory demyelinating diseases involving the central nervous system, which are often seen with combined involvement of the optic nerve and spinal cord. MOGAD can be confused with multiple sclerosis or NMOSD, due to its clinical presentation that may be similar and its characteristic to progress with habitual attacks. Although the clinical course of the above-mentioned three diseases is similar, their diagnosis and management are different.

An Interventional Health Education Study to Transition the General Population's Opinion on Organ Donation From Reluctance to Acceptance.

Introduction: Organ donation refers to the collection of a human organ from a living or deceased donor and its transplantation into a recipient. An organ transplant recipient is a patient with organ failure who will not survive unless he receives a new organ. Although the benefits of organ transplantation are undeniable, there is a significant gap between the number of donors and recipients, as the demand for organs greatly surpasses the available supply.

Exploring emotional patterns in social media through NLP models to unravel mental health insights.

This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts. The research question was: can an ensemble of fine-tuned transformer models (XLNet, RoBERTa, and ELECTRA) with Bayesian hyperparameter optimization improve the accuracy of mental health disorder classification in social media text. Three transformer models (XLNet, RoBERTa, and ELECTRA) were fine-tuned on a dataset of social media posts labelled with 15 distinct mental health disorders.

Comprehensive characterization of tobacco-induced changes in enamel surface topography.

Introduction: Enamel translucency, essential for the aesthetic appeal of teeth, is primarily determined by its thickness, quality, and refractive index. Several factors, including age, genetics, diet, oral hygiene practices, fluoride exposure, and acidic challenges, can influence enamel translucency. Tobacco use, in particular, leads to significant alterations in enamel appearance by penetrating its micropores, causing yellowing and browning.