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Evaluation of Satisfaction With Care in Paediatric Intensive Care Units: Swedish Parents' Perspective.
Rec. av Robert G. Hoyland, Seeing Islam as others saw it. A survey and evaluation of the Christian, Jewish and Zoroastrian writings on early Islam
Insights on Future Consumer Behavior- A study of what influences the Danish consumers when choosing food distribution channels in 2020
Tailoring the course of postprandial glycaemia to bread : On the importance of viscous dietary fibre for acute and semi-acute glucose tolerance and appetite
The prevalence of metabolic diseases such as type 2 diabetes mellitus (T2DM) is rapidly increasing all over the world. Frequent episodes of elevated postprandial blood glucose have been associated with oxidative stress and subclinical inflammation, and the importance of a tight glycaemic control has been identified as an important factor to maintain health and prevent T2DM, obesity and cardiovascu
Mise-en-scène and the City: En stilanalys av protagonistens lägenhet i "Sex and the City"
Corporate Citizenship and HIV/AIDS in the Workplace: What influences the implementation of workplace programmes in Botswana?
Lecture with Former President of Tunisia Dr. Mohamed Moncef Marzouki: "The Arab Democratic Revolutions Have Just Begun"
Anna Glenngård
Universitetslektor Kontaktinformation E-post: anna [dot] glenngard [at] fek [dot] lu [dot] se Telefon: +46 46 222 78 01Organisation Redovisning och finans Hämtställe: 10 WebbplatsAnna Glenngårds profil i Lunds universitets forskningsportalAndra roller Docent Redovisning och finans Publikationer Visar av publikationer. Sorterade efter år och sen titel. Filtrera efter typ AllaArtikel i tidskriftBokD
https://www.ehl.lu.se/anna-glenngard - 2026-04-27
Machine learning algorithm for classification of breast ultrasound images
Breast cancer is the most common type of cancer globally. Early detection is important for reducing the morbidity and mortality of breast cancer. The aim of this study was to evaluate the performance of different machine learning models to classify malignant or benign lesions on breast ultrasound images. Three different convolutional neural network approaches were implemented: (a) Simple convoluti