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 : The impact of AI on the future of public health and preventative healthcare
David John Wortley, International Society of Digital Medicine (ISDM), United Kingdom
Title : Personalized and Precision Medicine (PPM) as a unique healthcare model to secure the human healthcare, wellness and biosafety through the view of public health, network-driven healthcare services and lifestyle management
Sergey Suchkov, National Center for Human Photosynthesis, Mexico
Title : Managing integration and interoperability of intelligent and ethical transformed health and social care ecosystems
Habil Bernd Blobel, University of Regensburg, Germany
Title : Study scalp electroacupuncture therapy for autism spectrum disorder
Zhenhuan Liu, University of Chinese Medicine, China
Title : Environmental Public Health Impact Assessment (EHIA) process for tobacco processing plants
Vijayan Gurumurthy Iyer, Techno-Economic- Environmental Study and Check Consultancy Services, India
Title : Therapeutic potential of Benincasa hispida extract in regulating metabolic markers among patients with type 2 diabetes
Wan Rosli Wan Ishak , University Science Malaysia, Malaysia