Results 231 to 240 of about 2,374,745 (297)

RPLP2 Mediates the Beneficial Effects of Exercise on Stress Resistance Through Muscle–Brain Communication

Advanced Science, EarlyView.
A novel exercise‐inducible myokine acidic ribosomal protein P2 (RPLP2), initially identified from human trials, is presented here, whose circulating levels negatively correlate with clinical anxiety severity. Muscle‐derived RPLP2 enhances hippocampal ribosomal assembly and adult neurogenesis to rescue stress‐induced anxiety deficits.
Peiyu Luo, Wei Wu, Huan Peng, Dan He, Yuxi Guo, Boyue Zhao, Xiaodan Wang, Li Ma, Yuhang Qin, Yifang Zhai, Lixia Zhuo, Ying Zhang, Yijie Guo, Linlin Jing, Fangyao Chen, Erfei Zhang, Wei Wang, Xiancang Ma, Yan Li   +18 more
wiley   +1 more source

Prediction of polycystic ovary syndrome using machine learning models: Addressing class imbalance and high dimensionality. [PDF]

J Educ Health Promot
Acharya S, Poojari S, Kamath A.
europepmc   +1 more source

Applying supervised machine learning algorithms and ensemble models to enhance credit card fraud detection. [PDF]

Front Artif Intell
Al-Bulushi A, Shaikh AK, Adhikari N.
europepmc   +1 more source

Strategies for Class-Imbalanced Learning in Multi-Sensor Medical Imaging. [PDF]

Sensors (Basel)
Zhou D, Gao S, Huang X.
europepmc   +1 more source

A dual granular balanced deep forest model for effective drug combination prediction. [PDF]

iScience
Gong Z, Li R, Liu K, Xu Y, Bo X, He S.
europepmc   +1 more source

A synthetic oversampling-based customized ResNet51-Conv1D framework for early colorectal cancer prediction using structured clinical data from the PLCO screening trial. [PDF]

BMC Med Inform Decis Mak
Prasath ST, Asha MM, Nagarajan B, Yesuraj R, Prathapchandran K, Sreeraman Y.   +5 more
europepmc   +1 more source

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Assessing the data complexity of imbalanced datasets

Information Sciences, 2021
Imbalanced datasets are an important challenge in supervised Machine Learning (ML). According to the literature, class imbalance does not necessarily impose difficulties for ML algorithms.
André DE CARVALHO, Ana Carolina Lorena, Marcilio C P De Souto   +2 more
exaly   +3 more sources

An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets

Applied Soft Computing Journal, 2019
Learning and mining from imbalanced datasets gained increased interest in recent years. One simple but efficient way to increase the performance of standard machine learning techniques on imbalanced datasets is the synthetic generation of minority ...
György Kovacs
exaly   +2 more sources

An Investigation of SMOTE Based Methods for Imbalanced Datasets With Data Complexity Analysis

IEEE Transactions on Knowledge and Data Engineering, 2023
Many binary class datasets in real-life applications are affected by class imbalance problem. Data complexities like noise examples, class overlap and small disjuncts problems are observed to play a key role in producing poor classification performance ...
Nurulnur Athirah Azhar, Muhammad Syafiq Mohd Pozi, A. M. Din, A. Jatowt   +3 more
semanticscholar   +1 more source

Imbalanced Learning in Massive Phishing Datasets

2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020
Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling
Ali Azari, Josephine M. Namayanja, Navneet Kaur, Vasundhara Misal, Suraksha Shukla   +4 more
openaire   +1 more source