Results 131 to 140 of about 90,025 (264)

Laser‐Induced Graphene from Waste Almond Shells

Advanced Functional Materials, EarlyView.
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova, Anna Chiara Bressi, Marina Galliani, Attilio Marino, Gianni Ciofani, Eduardo Machado‐Charry, Hilda Gomez Bernal, Alessandra Francini, Luca Sebastiani, Francesco Greco   +9 more
wiley   +1 more source

PET-TURTLE: Deep Unsupervised Support Vector Machines for Imbalanced Data Clusters. [PDF]

IEEE Signal Process Lett
Salazar Cavazos J.
europepmc   +1 more source

Irradiation‐Induced Spin Bath Evolution and as‐Grown Hydrogen Defects in CVD Diamond Revealed by NV‐Based DEER Spectroscopy

Advanced Functional Materials, EarlyView.
Here, we provide the reader with the current state of the art in the fabrication of high‐T2‐coherence diamond, optimized through the use of double electron–electron resonance (DEER) spectroscopy. We reveal the main as well as yet unidentified spin defects, the reduction of which is essential for reducing the spin bath and fabricating materials for ...
Olga Rubinas, Jeroen Prooth, Michael Petrov, Remy Vandebosch, Emilie Bourgeois, David Chvátil, Milos Nesladek   +6 more
wiley   +1 more source

Enhancing Model Generalizability in Medical Artificial Intelligence: Systematic Comparison of Categorical Encoding and Sampling Techniques for Imbalanced Data. [PDF]

JMIR Med Inform
Chuang CW, Wu CK, Wu CH, Shia BC, Chen M.   +4 more
europepmc   +1 more source

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets. [PDF]

J Phys Chem Lett, 2023
Borislavov L, Nedyalkova M, Tadjer A, Aydemir O, Romanova J.   +4 more
europepmc   +1 more source

Fully Additive Vertical Organic Electrochemical Transistors on Renewable and Compostable Substrates Enable Ultrasensitive Bioelectronics

Advanced Functional Materials, EarlyView.
Fully additive vertical organic electrochemical transistors (vOECTs) with channel‐length of 226 nm on a 50 µm compostable substrate achieve >106 current modulation and ∼36 mS transconductance. Ion‐selective integration enables 1.1 mA dec−1 sensitivity and 36 µm detection limit, highlighting a scalable platform for sustainable electronics and ...
Giulia Frusconi, Samuele Lavelli, Zsolt M. Kovács‐Vajna, Fabrizio Torricelli   +3 more
wiley   +1 more source

Risk-Sensitive Machine Learning for Financial Decision Modeling Under Imbalanced Data: Evidence from Bank Telemarketing. [PDF]

Entropy (Basel)
Dong B, Zhang X, Liu Y, Zhang T, Liu X, Hou L, Meng L, Guo Z, Mulati A.   +8 more
europepmc   +1 more source

Predicting asthma using imbalanced data modeling techniques: Evidence from 2019 Michigan BRFSS data. [PDF]

PLoS One, 2023
Budhathoki N, Bhandari R, Bashyal S, Lee C.   +3 more
europepmc   +1 more source

Tuning Redox Dynamics in Cu‐Based Electrocatalysts: Effect of Secondary Metals

Advanced Functional Materials, EarlyView.
Advanced analysis of operando X‐ray absorption spectroscopy data reveals that secondary metals affect the redox processes in Cu‐based catalysts for electrocatalytic CO2 reduction. This influences the amount of oxides formed under pulsed reaction conditions and the distribution of reaction products.
Martina Rüscher, Andrea Martini, Eduardo Ortega, Lichen Bai, Eric Liberra, Daniel Escalera‐López, Hyo Sang Jeon, Antonia Herzog, Clara Rettenmaier, Dorottya Hursán, David Koshy, Mariana Cecilio de Oliveira Monteiro, Timon Wagner, Leon Jacobse, Felix Haase, Arno Bergmann, Janis Timoshenko, Beatriz Roldan Cuenya   +17 more
wiley   +1 more source

Imbalanced data stream classification method with limited labels

大数据
Data stream classification is a crucial research area within data stream mining, with the core task of swiftly capturing concept drifts from real-time incoming data stream and promptly adjusting classification models.
LI Yanhong, LI Zhihua, ZHENG Jianxing, BAI Hexiang, GUO Xin   +4 more
doaj