Results 111 to 120 of about 12,640,042 (291)

Bayesian multitrait kernel methods improve multienvironment genome-based prediction. [PDF]

G3 (Bethesda), 2022
Montesinos-López OA, Montesinos-López JC, Montesinos-López A, Ramírez-Alcaraz JM, Poland J, Singh R, Dreisigacker S, Crespo L, Mondal S, Govidan V, Juliana P, Espino JH, Shrestha S, Varshney RK, Crossa J.   +14 more
europepmc   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Parsimonious Kernel Fisher Discrimination

, 2007
By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution.
Harrison, Robert F., Willett, Peter, Robert F. Harrison, Pasupa, Kitsuchart, Kitsuchart Pasupa, Peter Willett   +5 more
core   +1 more source

Electroencephalography-Based Pain Detection Using Kernel Spectral Connectivity Network with Preserved Spatio-Frequency Interpretability

Applied Sciences
Chronic pain leads to not only physical discomfort but also psychological challenges, such as depression and anxiety, which contribute to a substantial healthcare burden.
Santiago Buitrago-Osorio, Julian Gil-González, Andrés Marino Álvarez-Meza, David Cardenas-Peña, Alvaro Orozco-Gutierrez   +4 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Correction: Kernel methods and their derivatives: Concept and perspectives for the earth system sciences. [PDF]

PLoS One, 2021
Johnson JE, Laparra V, Pérez-Suay A, Mahecha MD, Camps-Valls G.   +4 more
europepmc   +1 more source

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

Making Indefinite Kernel Learning Practical [PDF]

In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems ...
Mierswa, Ingo
core  

Polyphenol‐Inspired Materials for Agricultural Applications

Advanced Materials, EarlyView.
This review outlines the use of polyphenol‐inspired materials for sustainable agriculture, highlighting their molecular design, interfacial assembly, structure–property relationships, and prospects toward precision agriculture, climate resilience, ecosystem protection, and circular bioeconomy strategies.
Haofu Liu, Omid Mazaheri, Zhixing Lin, Tianzheng Wang, Jingqu Chen, Yuang Gu, Xia Liang, Deli Chen, Frank Caruso   +8 more
wiley   +1 more source

Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods. [PDF]

Sci Prog, 2021
Chen YJ, Liou YC, Ho WH, Tsai JT, Liu CC, Hwang KS.   +5 more
europepmc   +1 more source