Results 61 to 70 of about 141,868 (281)

An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection

Applied Sciences, 2023
The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance.
Ji-Na Lee, Ji-Yeoun Lee
doaj   +1 more source

RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu, Xin Duan, Fatemeh Peymani, Jia Wang, Chengjia Bao, Chaolong Xu, Ying Zou, Zixuan Zhang, Yunxi Zhang, Tongyue Li, Martin Pavlov, Junling Wang, Minhan Song, Tianyu Song, Xiaodi Han, Mingxi Sun, Danmin Shen, Ruoyu Duan, Huafang Jiang, Manting Xu, Holger Prokisch, Fang Fang   +21 more
wiley   +1 more source

A Systematic Comparison of Alpha‐Synuclein Seed Amplification Assays for Increasing Reproducibility

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Seed amplification assays (SAAs) enable ultrasensitive detection of misfolded α‐synuclein across biofluids and tissues. Yet, heterogeneity in protocols limits cross‐study comparability and clinical translation. Here, we review α‐synuclein SAA methods and their performance across various biological matrices.
Manuela Amaral‐do‐Nascimento, Daniela F. Santos, Tuane C. R. G. Vieira, Tiago F. Outeiro   +3 more
wiley   +1 more source

Enhancing Feature Selection for Imbalanced Alzheimer’s Disease Brain MRI Images by Random Forest

Applied Sciences, 2023
Imbalanced learning problems often occur in application scenarios and are additionally an important research direction in the field of machine learning. Traditional classifiers are substantially less effective for datasets with an imbalanced distribution,
Xibin Wang, Qiong Zhou, Hui Li, Mei Chen
doaj   +1 more source

Integrative machine learning approach for multi-class SCOP protein fold classification [PDF]

, 2003
Classification and prediction of protein structure has been a central research theme in structural bioinformatics. Due to the imbalanced distribution of proteins over multi SCOP classification, most discriminative machine learning suffers the well-known ‘
Deville, Y, Gilbert, D, Tan, A C
core  

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning

IEEE Access
Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly.
Haiou Qin
doaj   +1 more source

A Highly Adaptive Oversampling Approach to Address the Issue of Data Imbalance

Computers, 2022
Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling.
Szilvia Szeghalmy, Attila Fazekas
doaj   +1 more source

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

A systematic study of the class imbalance problem in convolutional neural networks

, 2018
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue.
Buda, Mateusz, Maki, Atsuto, Mazurowski, Maciej A.   +2 more
core   +1 more source