Results 71 to 80 of about 602,374 (329)

Nuevas tendencias en redes neuronales artificiales: extreme learning machine [PDF]

, 2009
Las redes neuronales artificiales han sido ampliamente utilizadas para resolver problemas de diagnosis médica, reconocimiento de voz, predicción de índices bursatiles, etc.
García Laencina, Pedro José, Larrey Ruiz, Jorge, Morales Sánchez, Juan, Sancho Gómez, José Luis, Verdú Monedero, Rafael   +4 more
core   +1 more source

Illuminance prediction through Extreme Learning Machines

2012 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), 2012
Planning, managing, and operating power grids using mixed traditional and renewable energy sources requires a reliable forecasting of the contribution of the renewable sources, due to their variable nature. Besides, the short-term prediction of the climatic conditions finds application in other fields (e.g., Climate Sensitive Buildings). In particular,
S. Ferrari, M. Lazzaroni, V. Piuri, A. Salman, L. Cristaldi, M. Rossi, T. Poli   +6 more
openaire   +4 more sources

A Fast Reduced Kernel Extreme Learning Machine [PDF]

Neural Networks, 2016
In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the ...
Deng, Wan-Yu, Ong, Yew-Soon, Zheng, Qing-Hua   +2 more
openaire   +3 more sources

Patterning the Void: Combining L‐Systems with Archimedean Tessellations as a Perspective for Tissue Engineering Scaffolds

Advanced Functional Materials, EarlyView.
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou, Kenny A. van Kampen, Pierpaolo Fucile, Carlos Mota, Lorenzo Moroni   +4 more
wiley   +1 more source

Deep information-extreme machine learning for autonomous UAV based on decursive data structure for semantic segmentation of digital image of a region

Радіоелектронні і комп'ютерні системи
The subject of the research is functional categorical models of deep information-extreme machine learning based on linear and hierarchical data structures, methods for optimizing machine learning parameters based on information criteria and constructing ...
Valerii Cheranovskyi, Mykyta Myronenko, Serhii Kovalevskyi, Roman Kraskovskyi, Mykhailo Otroshchenko   +4 more
doaj   +1 more source

Інтелектуалізація процесу діагностування онкопатологій [PDF]

, 2013
Одним із ефективних способів вирішення проблеми боротьби з онкозахворюваннями є інтелектуалізація процесу діагностування. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/31771Under extreme intellectual ...
Дрофа, В.О.
core  

Biosupercapacitors for Human‐Powered Electronics

Advanced Functional Materials, EarlyView.
Biosupercapacitors are emerging as biocompatible and integrative energy systems for next‐generation bioelectronics, offering rapid charge–discharge performance and mechanical adaptability. This review systematically categorizes their applications from external to organ‐level systems and highlights their multifunctional roles in sensing, actuation, and ...
Suhyeon Kim, Seungho Baek, Woojung Jung, Hayeon Bae, Junyoung Mun, Yong Ho Kim, Sunkook Kim, Jung Ho Kim   +7 more
wiley   +1 more source

Extreme Learning Machine for Multi-Label Classification

Entropy, 2016
Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers.
Xia Sun, Jingting Xu, Changmeng Jiang, Jun Feng, Su-Shing Chen, Feijuan He   +5 more
doaj   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

R-ELMNet: Regularized extreme learning machine network

Neural Networks, 2020
Principal component analysis network (PCANet), as an unsupervised shallow network, demonstrates noticeable effectiveness on datasets of various volumes. It carries a two-layer convolution with PCA as filter learning method, followed by a block-wise histogram post-processing stage. Following the structure of PCANet, extreme learning machine auto-encoder
Guanghao Zhang, Yue Li, Dongshun Cui, Shangbo Mao, Guang-Bin Huang   +4 more
openaire   +4 more sources