Results 11 to 20 of about 779,800 (277)

Artificial neural networks [PDF]

, 2017
Nowadays we are living in the age of computers, and we are systematically making our way to creating artificial intelligence. In addition, one of possible directions to make AI come true is to find the way of generalizing ...
Korotenko, L.M., Nechai, N.M., Teslenko, Svyatoslav   +2 more
core   +4 more sources

Guidelines to Support Graphical User Interface Design for Children with Autism Spectrum Disorder: An Interdisciplinary Approach

Proceedings, 2018
The study aims to describe the guidelines to support user interface design for develop technology centered in the specific learning style, abilities and needs of children with Autism Spectrum Disorder (ASD).
Betania Groba, Nereida Canosa, Patricia Concheiro-Moscoso   +2 more
doaj   +1 more source

Dissecting the Biological Motherboard (Systems Biology and Beyond) [PDF]

, 2008
Genome-scale molecular networks, including gene pathways, gene regulatory networks and protein interactions, are central to the investigation of the nascent disciplines of systems biology and bio-complexity.
Abhay Krishna, Ajit Narayanan
core   +2 more sources

Technologies for Participatory Medicine and Health Promotion in the Elderly Population

Proceedings, 2018
The progressive aging of the population [...]
Naveira-Carro Eloy, Concheiro-Moscoso Patricia, Miranda-Duro MC   +2 more
doaj   +1 more source

Neural Network Applications [PDF]

, 1995
Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in ...
Jain, L.C., Veelenturf, L.P.J., Vonk, E.
core   +3 more sources

Intuitionistic fuzzy neural network with filtering functions. An index matrix interpretation [PDF]

Notes on IFS, 2023
Biological neurons and their connection in neural networks have motivated the creation of the architecture of artificial neural networks. In the previously considered cases, the description of the neural networks and their connections are described with ...
Sotir Sotirov, Maciej Krawczak, Diana Petkova, Krassimir Atanassov   +3 more
doaj   +1 more source

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

PLoS ONE, 2020
Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine.
Shungo Imai, Yoh Takekuma, Hitoshi Kashiwagi, Takayuki Miyai, Masaki Kobayashi, Ken Iseki, Mitsuru Sugawara   +6 more
doaj   +1 more source

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks [PDF]

, 2018
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence.
Abbott, Abraham, Abraham, Alexander, Allis, Alpaydin, Anderson, Andrea Soltoggio, Angeline, Arifovic, Arnold, Arnold, Ay, Bailey, Baldwin, Baxter, Baxter, Bear, Beer, Bengio, Bengio, Bentley, Best, Bienenstock, Birmingham, Blynel, Boers, Bourlard, Brown, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Butz, Butz, Bäck, Cabessa, Carew, Carlson, Carver, Cervier, Chklovskii, Clark, Cliff, Clutton-Brock, Coleman, Cooper, Damasio, Darwin, Dawkins, Deary, Deng, de Vladar, Di Paolo, Di Paolo, Dobzhansky, Doidge, Downing, Downing, Doya, Doya, Draganski, Dudai, Durr, Edelman, Eiben, Ellefsen, Ellefsen, Eskridge, Fellous, Fernando, Fernando, Finnie, Floreano, Floreano, Floreano, Floreano, Floreano, Floreano, Fogel, Fontanari, Friston, Fujii, Funahashi, Gerstner, Gerstner, Goldberg, Goodfellow, Graves, Greve, Grossberg, Gruau, Gu, Gustafsson, Hansen, Happel, Harrington, Harris-Warrick, Harvey, Hasselmo, Hasselmo, Hasselmo, Hawkins, Hebb, Hensch, Hinton, Hinton, Hinton, Hochreiter, Hoinville, Holland, Holland, Hopkins, Hornby, Howard, Howard, Howard, Hull, Husbands, Izhikevich, Izhikevich, Jaeger, Jo, Kaas, Kandel, Kandel, Katz, Katz, Katz, Kenneth O. Stanley, Khan, Khan, Khan, Khan, Kirkpatrick, Kiyota, Klug, Kohonen, Kohonen, Kolb, Kolb, Kondo, Koutník, Koza, Krichmar, Krizhevsky, Kumaran, Kupfermann, Køppe, Lake, Lalejini, Lamprecht, Langton, Lanzi, LeCun, LeDoux, Lee, Lehman, Lehman, Lehman, Levy, Lüders, Lüders, Maass, Maniadakis, Marder, Marder, Markram, Mattiussi, Mayley, McClelland, McQuesten, Meng, Merzenich, Michalewicz, Michalski, Miconi, Miller, Miller, Millington, Monroe, Mouret, Murre, Niv, Nogueira, Nogueira, Nolfi, Nolfi, Nolfi, Nolfi, Norouzzadeh, Offerman, Oja, Oquab, Orchard, Ormrod, Pan, Pascual-Leone, Pavlov, Pehlevan, Pigliucci, Rauschecker, Rawal, Rescorla, Risi, Risi, Risi, Risi, Risi, Roberts, Robins, Roff, Rolls, Rumelhart, Russell, Russo, Sanchez, Schmidhuber, Schmidhuber, Schmidhuber, Schmidhuber, Schreiweis, Schroll, Schultz, Schultz, Schultz, Sebastian Risi, Silva, Silva, Silver, Sims, Sims, Sipper, Skinner, Skinner, Smith, Smith, Smith, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Sporns, Staddon, Stanley, Stanley, Stanley, Stanley, Stanley, Steels, Stone, Suri, Suri, Sutton, Taylor, Tessier-Lavigne, Thorndike, Thrun, Thrun, Thrun, Tonelli, Tonelli, Urzelai, Varela, Venkatesh, Vitay, Wagner, Walters, Widrow, Willshaw, Yamauchi, Yao, Yao, Yoder, Young, Ziemke   +282 more
core   +3 more sources

Neural Network with Combined Approximation of the Surface of the Response

Наукові вісті Національного технічного університету України "Київський політехнічний інститут", 2018
Background. There are a large number of neural networks that have their advantages and disadvantages, for example, simple, fast and easy to use single-stranded perceptrons are suitable for linear and linearized regression tasks, and more complicated ...
Oleksandra S. Mishchuk, Pavlo B. Vitynskyi   +1 more
doaj   +1 more source

Artificial Intelligence to Analyze the Cortical Thickness Through Age

Frontiers in Artificial Intelligence, 2021
In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions ...
Sergio Ledesma, Sergio Ledesma, Mario-Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Pascal Fallavollita, Jason Steffener   +5 more
doaj   +1 more source