HYBRID EVENT: Join us in person in Singapore or attend virtually from anywhere.

6th Edition of

International Public Health Conference

March 15-17, 2027 | Singapore

Neural Networks

Neural Networks

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.

Committee Members
Speaker at IPHC 2027 - Kenneth R Pelletier

Kenneth R Pelletier

University of California, United States
Speaker at IPHC 2027 - Thomas J Webster

Thomas J Webster

School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China
Speaker at IPHC 2027 - Bernd Blobel

Bernd Blobel

University of Regensburg, Germany
IPHC 2027 Speakers
Speaker at IPHC 2027 - Bernd Blobel

Bernd Blobel

University of Regensburg
Speaker at IPHC 2027 - Iuliana Vintila

Iuliana Vintila

Dunarea de Jos University, Galati
Speaker at IPHC 2027 - Sergey Suchkov

Sergey Suchkov

N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences
Speaker at IPHC 2027 - Wan Rosli Wan Ishak

Wan Rosli Wan Ishak

University Science Malaysia

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