Results 51 to 60 of about 18,911 (296)

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

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

Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning

Journal of Multidisciplinary Healthcare, 2021
Abdulrhman Fahad Aljouie,1,2 Ahmed Almazroa,2,3 Yahya Bokhari,1,2 Mohammed Alawad,1,2 Ebrahim Mahmoud,4 Eman Alawad,4 Ali Alsehawi,5 Mamoon Rashid,1,2 Lamya Alomair,1,2 Shahad Almozaai,6 Bedoor Albesher,6 Hassan Alomaish,5 Rayyan Daghistani,5 Naif Khalaf
Aljouie AF, Almazroa A, Bokhari Y, Alawad M, Mahmoud E, Alawad E, Alsehawi A, Rashid M, Alomair L, Almozaai S, Albesher B, Alomaish H, Daghistani R, Alharbi NK, Alaamery M, Bosaeed M, Alshaalan H   +16 more
doaj  

Performance of AE-XGB-SMOTE-CGAN with and without data augmentation.

Performance of AE-XGB-SMOTE-CGAN with and without data augmentation.
HaiChao Du (18114974), Hongliang Wang (440441), Li Lv (382638), An Guo (1451287)   +3 more
core   +2 more sources

Addressing imbalanced data classification with Cluster-Based Reduced Noise SMOTE.

PLoS ONE
In recent years, the challenge of imbalanced data has become increasingly prominent in machine learning, affecting the performance of classification algorithms. This study proposes a novel data-level oversampling method called Cluster-Based Reduced Noise
Javad Hemmatian, Rassoul Hajizadeh, Fakhroddin Nazari   +2 more
doaj   +1 more source

COMBINATION OF SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) AND BACKPROPAGATION NEURAL NETWORK TO CONTRACEPTIVE IUD PREDICTION

Media Statistika, 2020
Data imbalance occurs when the amount of data in a class is more than other data. The majority class is more data, while the minority class is fewer. Imbalance class will decrease the performance of the classification algorithm. Data on IUD contraceptive
Mustaqim Mustaqim, Budi Warsito, Bayu Surarso   +2 more
doaj   +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, Karim El Moutaouakil
openaire   +2 more sources

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

A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets

Scientific Reports
Class imbalance poses a significant challenge in water quality classification, often leading to biased predictions and diminished accuracy for minority classes.
Norashikin Nasaruddin, Nurulkamal Masseran, Wan Mohd Razi Idris, Ahmad Zia Ul-Saufie   +3 more
doaj   +1 more source

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

, 2011
This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier ...
Xia Hong, Hong, Xia, Ming Gao, Sheng Chen, Gao, Ming, Chris J. Harris, Chen, Sheng, Harris, Chris J., Harris, Chris   +8 more
core   +1 more source