Deep learning techniques are based on neural networks, sometimes referred to as artificial neural networks (ANNs) or simulated neural networks (SNNs), which are a subset of machine learning. Their structure and nomenclature are modelled after the human brain, mirroring the communication between organic neurons. A node layer, which includes an input layer, one or more hidden layers, and an output layer, makes up artificial neural networks. Each node, or artificial neuron, is connected to others and has a weight and threshold that go along with it. Any node whose output exceeds the defined threshold value is activated and begins providing data to the network's uppermost layer. Otherwise, no data is sent to the network's next tier.
Title : Gamification and enabling technologies in preventative healthcare
David John Wortley, International Society of Digital Medicine (ISDM), United Kingdom
Title : Aidiet intervention vs. Hormonal and immune-metabolic health in normal and overweight adolescent girls with polycystic ovary syndrome
Malgorzata Mizgier, Poznan University of Physical Education, Poland
Title : Migration: A major challenge to health and safety at work
Mark Fullemann, Practice & Experience GmbH, Switzerland
Title : Principles and standards for designing and managing intelligent and ethical health and social care ecosystems
Habil Bernd Blobel, University of Regensburg, Germany
Title : Trends in the epigenetics human longevity: Sorting hope from hype
Kenneth R Pelletier, University of California, United States
Title : Occupational health and safety of Hong Kong nursing students during clinical placement: A study tool development
Wong Yat Cheung Maggie, Saint Francis University, Hong Kong