Results 271 to 280 of about 4,590,312 (339)

Self‐Adaptive Anhydrous Passivation Mitigates Open‐Circuit Voltage Loss in Pb‐Sn Mixed Perovskite Solar Cells

Advanced Functional Materials, EarlyView.
In this report, a self‐adaptive anhydrous passivation strategy is introduced by incorporating trimellitic anhydride (TMAH) into the perovskite precursor. In situ hydrolysis of TMAH yields trimellitic acid (TMA); ‐C═O/‐COO− groups of TMAH/TMA form a chelate with undercoordinated Pb2+/Sn2+, regulate nucleation, promote (100) orientation, passivate ...
Md. Ataur Rahman, Md. Abdul Karim, Md. Emrul Kayesh, Siliang Cao, Shamim Ahmmed, Kiyoto Matsuishi, Ashraful Islam   +6 more
wiley   +1 more source

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients. [PDF]

Diagnostics (Basel)
Kivrak M, Avci U, Uzun H, Ardic C.
europepmc   +1 more source

Resolving the Cu(bdc) Conundrum: Identifying Non‐Porous Packing of Prototypical Coordination‐Network Thin Films Combining Advanced Diffraction Techniques and Computational Modelling

Advanced Functional Materials, EarlyView.
Solution‐processed Cu(bdc) forms prototypical MOF thin films for which a multitude of not fully satisfactory structural models have been suggested. Combining rotating grazing‐incidence diffraction and X‐ray reflectivity on two complementary samples with density‐functional theory, we first discard the previously suggested models and then identify a non ...
Narges Taghizade, Mario Fratschko, Robbin Steentjes, Mercedes Linares‐Moreau, Paolo Falcaro, Christof Wöll, Roland Resel, Egbert Zojer   +7 more
wiley   +1 more source

Imbalance: A comprehensive multi-interface Julia toolbox to address class imbalance

Journal of Open Source Software
Essam Wisam, Anthony D. Blaom
openaire   +1 more source

Multi-Imbalance: An open-source software for multi-class imbalance learning

Knowledge-Based Systems, 2019
Abstract Imbalance classification is one of the most challenging research problems in machine learning. Techniques for two-class imbalance classification are relatively mature nowadays, yet multi-class imbalance learning is still an open problem. Moreover, the community lacks a suitable software tool that can integrate the major works in the field ...
Chongsheng Zhang, Shixin Xu, Enislay Ramentol   +2 more
exaly   +4 more sources

Simplifying Neural Network Training Under Class Imbalance

Neural Information Processing Systems, 2023
Real-world datasets are often highly class-imbalanced, which can adversely impact the performance of deep learning models. The majority of research on training neural networks under class imbalance has focused on specialized loss functions, sampling ...
Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. B. Bruss, Andrew Gordon Wilson   +4 more
semanticscholar   +3 more sources

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Multi-Class Imbalance Classification Based on Data Distribution and Adaptive Weights

IEEE Transactions on Knowledge and Data Engineering
AdaBoost approaches have been used for multi-class imbalance classification with an imbalance ratio measured on class sizes. However, such ratio would assign each training sample of the same class with the same weight, thus failing to reflect the data ...
Shuxian Li, Liyan Song, Xiaoyu Wu
exaly   +2 more sources

The class imbalance problem: a systematic study

Intell. Data Anal., 2002
Summary: In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions.
Nathalie Japkowicz, Shaju Stephen
openaire   +4 more sources

Class-overlap undersampling based on Schur decomposition for Class-imbalance problems

Expert Systems With Applications, 2023
Qi Dai, Anfeng Liu, Jian-Wei Liu
exaly   +2 more sources