Results 71 to 80 of about 111,551 (360)

Inverse Engineering of Mg Alloys Using Guided Oversampling and Semi‐Supervised Learning

Advanced Intelligent Discovery, EarlyView.
End‐to‐end design of engineering materials such as Mg alloys must include the properties, structure, and post‐synthesis processing methods. However, this is challenging when destructive mechanical testing is needed to annotate unseen data, and the processing methods for hypothetical alloys are unknown.
Amanda S. Barnard
wiley   +1 more source

An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Applied Computer Systems, 2022
The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”.
Khan Isra, Emaduddin Shah Muhammad, Ullah Ashhad, Ullah A Rafi   +3 more
doaj   +1 more source

Global Data Distribution Weighted Synthetic Oversampling Technique for Imbalanced Learning

IEEE Access, 2021
Imbalanced learning is a common problem in data mining. There is a different distribution of data samples among other classes in the imbalanced datasets. It’s a challenge for standard algorithms designed for balanced class distributions.
Zhenfei Wang, Hongju Wang
doaj   +1 more source

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

, 2019
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs.
Ballesteros-Herráez, Juan Carlos, Kraiem, Mohamed S., Moreno-García, María N., Sánchez-Barba, Mercedes, Sánchez-Hernández, Fernando   +4 more
core   +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

Enhanced Skin Lesion Segmentation and Classification Through Ensemble Models

Eng
This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks.
Su Myat Thwin, Hyun-Seok Park
doaj   +1 more source

Hybrid Undersampling and Oversampling for Handling Imbalanced Credit Card Data

IEEE Access
Recent developments in the use of credit cards for a range of daily life activities have increased credit card fraud and caused huge financial losses for individuals and financial institutions. Most credit card frauds are conducted online through illegal
Maram Alamri, M. Ykhlef
semanticscholar   +1 more source

An Oversampling Method of Unbalanced Data for Mechanical Fault Diagnosis Based on MeanRadius-SMOTE

Italian National Conference on Sensors, 2022
With the development of machine learning, data-driven mechanical fault diagnosis methods have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it is a difficult problem for fault diagnosis to solve the problem of ...
Feng Duan, Shuai Zhang, Yinze Yan, Zhiqiang Cai   +3 more
semanticscholar   +1 more source

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

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source