Results 181 to 190 of about 7,675,115 (371)

Deep learning in neural networks: An overview [PDF]

Neural Networks, 2014
J. Schmidhuber
semanticscholar   +1 more source

Ultra‐Shallow Flat Heat Waves on Low Thermal Conductivity Flexible Polyimide Substrate for Millisecond Thermal Analysis of Materials and Bioelectronic Applications

Advanced Engineering Materials, EarlyView.
An electronic system is presented consisting of a silver thin‐film electrode on a polyimide substrate working both as a heating element and a resistive temperature sensor, generating millisecond heat pulses with very fast heating and cooling rates and a flat peak temperature with a tunable duration.
Bahman K. Boroujeni, Jan Frede, Simon T. Herrmann, Natalie Heinen, Hans Kleemann, Stephanie Pfaender   +5 more
wiley   +1 more source

NEURAL NETWORK FORMATION OF EDUCATIONAL QUALIFICATION CHARACTERISTICS IN THE SYSTEM OF DISTANCE LEARNING MOODLE

, 1970
Olena О. Arsirii, Olena H. Zhylenko
openalex   +2 more sources

The neural network as a prototype in the design of nonconventional computer architecture for artificial intelligence applications (abstract) [PDF]

, 1986
Massoud Omidvar, J.Y. Cheung
openalex   +1 more source

Dermatologist-level classification of skin cancer with deep neural networks

Nature, 2017
A. Esteva, Brett Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau, S. Thrun   +6 more
semanticscholar   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Introduction to Neural Networks. [PDF]

, 1992
Nanette S. Holder
openalex   +1 more source

Multimaterial Approach to Improve the Mechanical Properties of a Novel Modified Auxetic Reentrant Honeycomb Structure

Advanced Engineering Materials, EarlyView.
A multimaterial approach is introduced to improve upon auxetic structures by combining two different polymers into the same reentrant honeycomb structure via additive manufacturing. The deformation behavior as well as the resulting Poisson's ratio are thereby improved significantly.
Alexander Engel, Oraib Al‐Ketan, Anne Jung   +2 more
wiley   +1 more source

Holographic implementation of a fully connected neural network [PDF]

, 1990
Ken Y. Hsu, H.-Y. Li, Demetri Psaltis
openalex   +1 more source