Results 251 to 260 of about 111,551 (360)

Design of low power energy efficient sigma-delta ADC for biomedical IoT applications. [PDF]

Sci Rep
Sharma BP, Gupta A, Shekhar C, Chauhan R, Chamola V.   +4 more
europepmc   +1 more source

Hypertension distribution in the original dataset, and balanced class label distribution after applying the Synthetic Minority Oversampling Technique (SMOTE) for training and test data.

Probir Kumar Ghosh (6947282), Md. Aminul Islam (6750650), Md. Ahshanul Haque (10525794), Md. Tariqujjaman (7038959), Novel Chandra Das (19742953), Mohammad Ali (73095), Md. Rasel Uddin (21648057), Md. Golam Dostogir Harun (7208396)   +7 more
openalex   +1 more source

AI and Measurement Concerns: Dealing with Imbalanced Data in Autoscoring

Journal of Educational Measurement, Volume 63, Issue 1, Spring 2026.
Abstract Unbiasedness for proficiency estimates is important for autoscoring engines since the outcome might be used for future learning or placement. Imbalanced training data may lead to certain biases and lower the prediction accuracy for classification algorithms.
Yunting Liu, Yijun Xiang, Xutao Feng, Mark Wilson   +3 more
wiley   +1 more source

Sentiment Analysis Of Ijen Crater Reviews Using Naïve Bayes Classification And Oversampling Optimization

Fadhel Akhmad Hizham, Hasyim Asy’ari, Maysas Yafi Urrochman   +2 more
openalex   +2 more sources

SKALE: An Interpretable Multiscale Machine Learning Model for Decoding Phase‐Specific Protein Aggregation in Neurodegenerative Proteinopathies

Aggregate, Volume 7, Issue 2, February 2026.
Protein aggregation drives diverse degenerative diseases, yet its molecular origins are difficult to predict. SKALE uses interpretable machine learning to link sequence, structure, and dynamics, revealing how local structural weakening triggers aggregation.
Wei Xuan Wilson Loo, Jia Shen Sio, Keyin Yap, Yan Shan Loo, Hui Xuan Lim, Shuangyue Zhang, Huitao Liu, Chen Seng Ng   +7 more
wiley   +1 more source

Data oversampling and feature selection for class imbalanced datasets

V Krishnakuma, V Sangeetha
openalex   +2 more sources

Dynamic Memory‐Augmented Whale Optimization Algorithm (DMA‐WOA) as Feature Descriptor for Polycystic Ovary Syndrome Detection

Applied AI Letters, Volume 7, Issue 1, February 2026.
Proposed approach for this study. ABSTRACT This study introduces a dynamically memory‐adjusted whale optimization algorithm (DMA‐WOA) for feature selection in polycystic ovary syndrome (PCOS) diagnosis. To overcome the standard WOA's limitations in balancing exploration and exploitation, DMA‐WOA incorporated adaptive memory control to improve ...
Daniel Kwame Amissah, Leonard Mensah Boante, Solomon Mensah, Ebenezer Owusu, Justice Kwame Appati   +4 more
wiley   +1 more source

Machine learning methods for predicting adverse drug events: A systematic review

British Journal of Clinical Pharmacology, Volume 92, Issue 2, Page 422-444, February 2026.
Abstract Predicting adverse drug events (ADEs) in outpatient settings is crucial for improving medication safety, identifying high‐risk patients and reducing health‐care costs. While traditional methods struggle with the complexity of health‐care data, machine learning (ML) models offer improved prediction capabilities; however, their effectiveness in ...
Niaz Chalabianloo, Fatemeh Ahmadi, Mohammad Ali Omrani, Sheikh S. Abdullah, Neda Rostamzadeh, Atefeh Jafari, Lujain Izzedin, Kamran Sedig, Flory T. Muanda   +8 more
wiley   +1 more source

Mitigating Disparities in Prostate Cancer Survival Prediction Through Fairness‐Aware Machine Learning Models

Cancer Medicine, Volume 15, Issue 2, February 2026.
ABSTRACT Purpose Prediction models can contribute to disparities in care by performing unequally across demographic groups. While fairness‐aware methods have been explored for binary outcomes, applications to survival analysis remain limited. This study compares two fairness‐aware deep learning survival models to mitigate racial disparities in ...
Hyungrok Do, Rajesh Ranganath, Katie Murray, Madhur Nayan   +3 more
wiley   +1 more source

Why did individuals seeking COVID-19 advice return to internet hospitals for follow-up in the early pandemic: Insights from machine learning. [PDF]

BMC Public Health
Zhang X, Wang Q, Yang L, Su T.
europepmc   +1 more source