Results 111 to 120 of about 2,420,336 (400)
SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence [PDF]
, 2006 Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks.
Barbancho Concejero, Antonio +3 more
core +1 more source
Perspective: Spintronic synapse for artificial neural network
Journal of Applied Physics, 2018 While digital integrated circuits with von Neumann architectures, having exponentially evolved for half a century, are an indispensable building block of today's information society, recently growing demand on executing more complex tasks like the human ...
S. Fukami, H. Ohno
semanticscholar +1 more source
Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Postoperative delirium, a common neurocognitive complication after surgery and anesthesia, requires early detection for potential intervention. Herein, we constructed a multidimensional postoperative delirium risk‐prediction model incorporating multiple demographic parameters and blood biomarkers to enhance prediction accuracy ...
Hengjun Wan +7 more
wiley +1 more source
Visual Character Recognition using Artificial Neural Networks [PDF]
arXiv, 2005 The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In this paper, a simplified neural approach to recognition of optical or visual characters is portrayed and discussed.
arxiv
Training of photonic neural networks through in situ backpropagation [PDF]
, 2018 Recently, integrated optics has gained interest as a hardware platform for implementing machine learning algorithms. Of particular interest are artificial neural networks, since matrix-vector multi- plications, which are used heavily in artificial neural networks, can be done efficiently in photonic circuits.
arxiv +1 more source
An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems [PDF]
, 1991 Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield neural network and the feedforward neural network studied is the single layer perceptron artificial ...
Suzuki, Andrew H.
core +1 more source
Artificial Neural Network: Understanding the Basic Concepts without Mathematics
Dementia and Neurocognitive Disorders, 2018 Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron.
Su-Hyun Han +3 more
semanticscholar +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez +2 more
wiley +1 more source
Developing Artificial Neural Network Based on Visual Studio for Dance Assessment
Jurnal Pendidikan Teknologi dan Kejuruan, 2017 The dance assessment test still uses a manual system that tend to have frequent errors in the calculation for the final results thus it requires a system that accelerate the assessment process with an accurate result.
Febri Suci Rahmahwati, Fatchul Arifin
doaj +1 more source
Advanced Biology, EarlyView.Synthetic cells (SCs) hold great promise for biomedical applications, but manual production limits scalability. This study presents an automated method for large‐scale SC synthesis, integrating robotic liquid handling and machine learning‐driven high‐throughput characterization.
Noga Sharf‐Pauker +7 more
wiley +1 more source