scholarly journals DEEP LEARNING-BASED PREDICTION OF OBESITY LEVELS ACCORDING TO EATING HABITS AND PHYSICAL CONDITION

2021 ◽  
Author(s):  
Mehmet KIVRAK
Keyword(s):  
Deep Learning ◽  
Physical Condition ◽  
Eating Habits

scholarly journals Risky Behaviour among Nurses in Poland: An Analysis of Nurses’ Physical Condition, Mental Health, and Resilience

2021 ◽  
Vol 18 (4) ◽  
pp. 1807
Author(s):  
Lucyna Gieniusz-Wojczyk ◽  
Józefa Dąbek ◽  
Halina Kulik
Keyword(s):  
Mental Health ◽  
Chronic Stress ◽  
Physical Condition ◽  
Eating Habits ◽  
Descriptive Study ◽  
Lower Intensity ◽  
Risky Behaviour ◽  
Physical Wellbeing ◽  
Mental Resilience ◽  
Low Levels

Background: Nursing is a profession where staff are exposed to chronic stress. Mental resilience plays a significant role in the process of coping with these challenges. The aim of this study was to assess nurses’ mental and physical wellbeing, as well as resilience, by taking into account the occurrence of risky behaviour among nurses in Poland. Methods: A descriptive study was carried out between June 2017 and May 2018 among nurses (n = 1080) employed in primary healthcare or in training centres in Silesia, Poland. Data were obtained from a number of questionnaires. Results: Over half of the nurses (n = 735; 68%) had an average psychophysical mood level resulting from stress, and 179 (16.6%) nurses had a low psychophysical mood. Those with a lower psychophysical mood showed a greater tendency towards developing improper eating habits (r = −0.23; p < 0.001). Most nurses had an average (n = 649; 60.1%) or low (n = 255; 23.6%) level of resilience. Higher resilience levels were observed in nurses aged over 30 years (p = 0.004) and in those with additional employment (p = 0.008). High resilience was associated with a lower intensity of risky behaviour. Conclusion: Most nurses in Poland display average and low levels of resilience, which can have unfavourable consequences for their health.

scholarly journals Physical condition and eating habits in Physical Education students

2021 ◽  
Author(s):  
Anwar Julio Yarin-Achachagua ◽  
Lucía Mireya Soria-Villanueva ◽  
Marco Antonio Tejada-Mendoza ◽  
Manuel Jesus Arista-Huaco
Keyword(s):  
Physical Education ◽  
Physical Condition ◽  
Eating Habits ◽  
Education Students ◽  
Physical Education Students

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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