Showing items with similar title or keywords.

• Using deep neural networks for kinematic analysis : challenges and opportunities 

  Cronin, Neil J. (Elsevier BV, 2021)

  Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such ...

• The Impact of Regularization on Convolutional Neural Networks 

  Zeeshan, Khaula (2018)

  Syvä oppiminen (engl. deep learning) on viime aikoina tullut suosituimmaksi koneoppimisen menetelmäksi. Konvoluutio(hermo)verkko on yksi suosituimmista syvän oppimisen arkkitehtuureista monimutkaisiin ongelmiin kuten kuvien ...

• Evolutionary Algorithms and Metaheuristics : Applications in Engineering Design and Optimization 

  Greiner, David; Periaux, Jacques; Quagliarella, Domenico; Magalhaes-Mendes, Jorge; Galván, Blas (Hindawi Publishing Corporation, 2018)

• Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks 

  Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti (Springer, 2020)

  In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. ...

• Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network 

  Annala, Leevi; Neittaanmäki, Noora; Paoli, John; Zaar, Oscar; Pölönen, Ilkka (IEEE, 2020)

  In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural ...