Results 211 to 220 of about 90,025 (264)

Adsorption‐Engineered Hydrocarbon Ionomers for Durable Proton‐Exchange Membrane Fuel Cells

Advanced Materials, EarlyView.
Hydrocarbon ionomers suffer from oxidation‐driven interfacial degradation in PEM fuel cell cathodes, leading to catalyst instability. Here, adsorption‐engineered poly(fluorene) ionomers decouple interfacial anchoring from oxidative degradation, enabling strong catalyst–ionomer interactions while resisting electrochemical oxidation.
Heemin Park, Kate Chen, Su Min Ahn, Hengquan Guo, Cy Fujimoto, Jong‐Ho Choi, Lynda Amichi, Danah Kim, Seung Geol Lee, Xiaojing Wang, Jacob S. Spendelow, Sun Young Kang, Panagiotis Bexis, Eun Joo S. Park, Yu Seung Kim   +14 more
wiley   +1 more source

A Universal van der Waals Tunneling Injector for Monolayer CMOS

Advanced Materials, EarlyView.
A universal van der Waals injector unlocks polarity‐flexible tunneling contacts for monolayer CMOS. SnSe2, an intrinsically degenerate 2D semiconductor, establishes polarity‐specific band alignments with both WSe2 and MoS2 channels, enabling steep switching, high on/off ratios, and a unified route to low‐power 2D logic.
Hanbin Cho, Jing Huang, Seonguk Yang, Subin Im, Sangwoo Park, Jeongin Yeo, Sungyeon Kim, Gilhwan Do, Wenxuan Zhu, Soobeom Shin, Jongwon Lee, Yongjoon Shin, Do‐Sun Lee, Hu Young Jeong, Tae‐Eon Park, Jun Kang, Kyungmin Ko, Joonki Suh   +17 more
wiley   +1 more source

A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. [PDF]

Front Cardiovasc Med
Zheng H, Sherazi SWA, Lee JY.
europepmc   +1 more source

Atomic‐Scale Detection of Néel Vector Switching in the Single‐Layer A‐Type Antiferromagnet Cr2S3‐2D

Advanced Materials, EarlyView.
Interfacial electron donation from graphene redistributes charge within single‐layer Cr2S3‐2D, preferentially accumulating at the lower Cr/S plane. The resulting minute magnetic imbalance lifts the equivalence of the two magnetic sublayers, enabling atomic‐resolution identification and field‐induced control of Néel‐vector states in a two‐dimensional A ...
Affan Safeer, Calisa Dias, Mahdi Ghorbani‐Asl, Abdallah Karaka, Pradyumna Bawankule, Weibin Li, Pierluigi Gargiani, Wouter Jolie, Arkady V. Krasheninnikov, Amilcar Bedoya‐Pinto, Thomas Michely, Jeison Fischer   +11 more
wiley   +1 more source

Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning. [PDF]

BMC Oral Health
Lee SJ, Oh HJ, Son YD, Kim JH, Kwon IJ, Kim B, Lee JH, Kim HK.   +7 more
europepmc   +1 more source

An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. [PDF]

PLoS One
Liu Y, Wang S, Sui H, Zhu L.
europepmc   +1 more source

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Learning from Imbalanced Data

IEEE Transactions on Knowledge and Data Engineering, 2009
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
Haibo He
exaly   +2 more sources

IIvotes ensemble for imbalanced data

Intelligent Data Analysis, 2012
In the paper we present IIvotes – a new framework for constructing an ensemble of classifiers from imbalanced data. IIvotes incorporates the SPIDER method for selective data pre-processing into the adaptive Ivotes ensemble. Such an integration is aimed at improving balance between sensitivity and specificity (evaluated by the G-mean measure) for the ...
Jerzy Blaszczynski, Magdalena Deckert, Jerzy Stefanowski, Szymon Wilk   +3 more
openaire   +1 more source

Protein classification with imbalanced data

Proteins: Structure, Function, and Bioinformatics, 2007
AbstractGenerally, protein classification is a multi‐class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class. The proteins in one class are seen as positive examples while those outside the class are seen as negative examples.
Xing-Ming, Zhao, Xin, Li, Luonan, Chen, Kazuyuki, Aihara   +3 more
openaire   +2 more sources

Credal Clustering for Imbalanced Data

2021
Traditional evidential clustering tends to build clusters where the number of data for each cluster fairly close to each other. However, it may not be suitable for imbalanced data. This paper proposes a new method, called credal clustering (CClu), to deal with imbalanced data based on the theory of belief functions.
Zuowei Zhang 0001, Zhunga Liu, Kuang Zhou, Arnaud Martin 0001, Yiru Zhang   +4 more
openaire   +1 more source