1 citations
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October 2014 in “Journal of aesthetic nursing” This article presents a facial hair removal treatment protocol from a clinical laser nurse perspective, discussing best practices but reporting no new clinical findings.
May 2026 in “International Journal of Scientific Research in Science and Technology” This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.
May 2026 in “The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy” This study conducted a comprehensive analysis of methods for assessing surface roughness, highlighting that contact methods using profilometers provide accurate results for machine parts, while non-contact optical and laser methods offer high precision and efficiency without damaging surfaces.
2 citations
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September 2023 in “JMIR. Journal of medical internet research/Journal of medical internet research” This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.
1 citations
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December 2025 in “Scientific Reports” In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.