Results 31 to 40 of about 40,856 (264)

Separation of pulsar signals from noise with supervised machine learning algorithms

, 2018
We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ), Adaboost, Gradient Boosting Classifier (GBC), XGBoost, for the separation of pulsars from radio frequency ...
Bethapudi, Suryarao, Desai, Shantanu
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

, 2018
We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results ...
Bennin Kwabena Ebo, Chiha I., Ghotra Baljinder, Menzies Tim, Omran M., Pedregosa Fabian, Refaeilzadeh Payam, Tan Ming   +7 more
core   +5 more sources

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source

FADA-SMOTE-Ms: Fuzzy Adaptative Smote-Based Methods

IEEE Access
The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known methods to solve the unequal class distribution problem in imbalanced datasets. However, it has three shortcomings: (1) it may cause the over-generalization problem due to oversampling of noisy samples, (2) over-sampling of uninformative samples, and (3) increasing the
Roudani Mohammed, El Moutaouakil Karim
openaire   +2 more sources

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

, 2018
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets.
Cheung, Ngai-Man, Elovici, Yuval, Lim, Swee Kiat, Loo, Yi, Roig, Gemma, Tran, Ngoc-Trung   +5 more
core   +1 more source

Data‐Driven Design of Bimodal Networked Dielectric Elastomers for High‐Performance Artificial Muscles

Advanced Intelligent Systems, EarlyView.
A data‐efficient artificial intelligence‐assisted framework, which integrates experimental data with machine learning, is developed for the design of bimodal networked dielectric elastomers (DEs) as advanced artificial muscles. It adopts neural networks to predict DEs’ mechanical properties and support vector machines to classify electromechanical ...
Ofoq Normahmedov, Hanzhi Ma, Junbo Peng, Huifeng Dong, Jiangshan Zhuo, Mengke Shi, Lvting Wang, Shengchao Jiang, Ye Shi   +8 more
wiley   +1 more source

SMOTE for high-dimensional class-imbalanced data [PDF]

BMC Bioinformatics, 2013
Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The problem can be attenuated by undersampling or oversampling, which produce class-balanced data.
Blagus, Rok, Lusa, Lara
openaire   +2 more sources

MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification

, 2017
Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced.
Ahmed, Sajid, Farid, Dewan Md., Jani, Md. Rafsan, Mahbub, Asif, Rahman, Chowdhury Mofizur, Rayhan, Farshid, Shatabda, Swakkhar   +6 more
core   +1 more source