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Quantitative analysis and development of alopecia areata classification frameworks

Dubey et al. | Jun 03, 2024

Quantitative analysis and development of alopecia areata classification frameworks

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

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The most efficient position of magnets

Shin et al. | Mar 28, 2024

The most efficient position of magnets
Image credit: immo RENOVATION

Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.

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Scientific project in physics "Carbonated liquids and carbonation level"

Irina et al. | Jan 21, 2024

Scientific project in physics "Carbonated liquids and carbonation level"

In our work we followed the formation of gas bubbles on the surface of the vessel walls in different carbonated liquids, over different time intervals, at different temperatures and in vessels made of different materials. Our results made it possible to identify patterns affecting the process of formation and disappearance of carbon dioxide bubbles.

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