Results 131 to 140 of about 46,171 (307)

Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN

IEEE Access
The healthcare fraud detection field is constantly evolving and faces significant challenges, particularly when addressing imbalanced data issues. Previous studies mainly focused on traditional machine learning (ML) techniques, often struggling with ...
Rayene Bounab, Karim Zarour, Bouchra Guelib, Nawres Khlifa   +3 more
doaj   +1 more source

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset. [PDF]

Sensors (Basel), 2022
Alaba SY, Nabi MM, Shah C, Prior J, Campbell MD, Wallace F, Ball JE, Moorhead R.   +7 more
europepmc   +1 more source

Low Cycle Repetitive Loading of Ti‐6Al‐4V‐Epoxy Composite Lattice Structures for Enhanced Energy Dissipation and Damage Tolerance

Advanced Engineering Materials, EarlyView.
Composite Ti–6Al–4V–epoxy lattice structures are additively manufactured and epoxy infiltrated for cyclic loading. At low lattice volume fractions, hybridization produces synergistic gains in stiffness and energy dissipation. At higher volume fractions, synergy diminishes, although composites still exceed metallic lattices in specific energy ...
Joey Tallon, Jordan Noronha, Justin Dirrenberger, Andrey Molotnikov   +3 more
wiley   +1 more source

Tomek Link and SMOTE Approaches for Machine Fault Classification with an Imbalanced Dataset. [PDF]

Sensors (Basel), 2022
Swana EF, Doorsamy W, Bokoro P.
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Proposal of self and semi-supervised learning for imbalanced classification of coronary heart disease tabular data

Tecnología en Marcha
Triple Mixup is an augmentation policy in the hidden latent space we introduced in the Contrastive Mixup Self-Semi Supervised learning framework, to address the imbalanced data problem, for Cardiovascular Heart Diseases tabular dataset. Medical tabular
Danny Xie-Li, Manfred González-Hernández   +1 more
doaj   +1 more source

Applying Support Vector Machines to Imbalanced Datasets [PDF]

, 2004
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in gene profiling and detecting credit card fraud)
Rehan Akbani, Stephen Kwek, Nathalie Japkowicz   +2 more
openaire   +1 more source

Transparent and Robust LiCl–Organohydrogel Triboelectric Nanogenerator With Deep Learning Assisted Sensing

Advanced Functional Materials, EarlyView.
Develop a LiCl–PEI–PAM hydrogel with 3000% stretchability and excellent optical transparency. Through comparative studies of various salts, confirm that LiCl is the most suitable salt for high TENG output. Achieve excellent freeze‐resistant, dry‐resistant, and rapid self‐healing (10 s) properties even in extreme environments. Balance ionic conductivity,
Hai Anh Thi Le, Thien Trung Luu, Duc‐Manh Dinh, Kyehan Rhee, Kyungwho Choi, Dukhyun Choi, Yong Tae Park   +6 more
wiley   +1 more source

The Imbalanced Target Classification Method Based on Mixed Learning of Virtual and Real Data

IEEE Access
We proposes a category imbalance classification model based on mixed feature enhancement between virtual and real domains to address the class imbalance problem in maritime target classification applications.
Fengyu Yang, Peng Wang, Wutao Qin, Zhangze Liao   +3 more
doaj   +1 more source

Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem. [PDF]

Medicina (Kaunas), 2021
Qasim HM, Ata O, Ansari MA, Alomary MN, Alghamdi S, Almehmadi M.   +5 more
europepmc   +1 more source