Results 111 to 120 of about 3,760 (232)

Detecting Imbalanced Credit Card Fraud via Hybrid Graph Attention and Variational Autoencoder Ensembles

AppliedMath
Credit card fraud detection remains a major challenge due to severe class imbalance and the constantly evolving nature of fraudulent behaviors. To address these challenges, this paper proposes a hybrid framework that integrates a Variational Autoencoder (
Ibomoiye Domor Mienye, Ebenezer Esenogho, Cameron Modisane   +2 more
doaj   +1 more source

RGCVAE: relational graph conditioned variational autoencoder for molecule design

Machine Learning
Abstract Identifying molecules that exhibit some pre-specified properties is a difficult problem to solve. In the last few years, deep generative models have been used for molecule generation. Deep Graph Variational Autoencoders are among the most powerful machine learning tools with which it is possible to address this problem.
Davide Rigoni 0001, Nicolò Navarin, Alessandro Sperduti   +2 more
openaire   +2 more sources

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks

Applied Sciences
B-cell epitope prediction is crucial for advancing immunology, particularly in vaccine development and antibody-based therapies. Traditional experimental techniques are hindered by high costs, time consumption, and limited scalability, making them ...
Feng Wang, Xiangwei Dai, Liyan Shen, Shan Chang   +3 more
doaj   +1 more source

Fault Detection of Electric Motors via Symmetrized Dot Pattern‐Based Features

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT This study proposes a novel symmetrized dot pattern (SDP) approach using designed SDP‐based features, extracted from transformed vibration signals, for fault detection. These features describe the compactness, inclination, and shape of the “snowflake” diagram distributions.
Mario Spirto, Pierangelo Malfi, Armando Nicolella, Francesco Melluso, Chiara Cosenza, Sergio Savino, Vincenzo Niola   +6 more
wiley   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

SFK: Shape‐ and Function‐Grounded Keypoint Representation for Sequential Manipulation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Sequential manipulation is the process by which robots perform multiple interdependent steps to accomplish composite tasks, demanding tight integration of perception, planning and execution. Existing methods incorporate explicit features such as category, semantics, 6D pose or affordance to enhance consistency, yet single‐feature ...
Yaxin Liu, Yan Liu, Xinyu Song, Ruiqing Han, Haiyong Liu, Bo Huang, Yufeng Yao, Ming Zhong   +7 more
wiley   +1 more source

stDyer enables spatial domain clustering with dynamic graph embedding

Genome Biology
Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains.
Ke Xu, Yu Xu, Zirui Wang, Xin Maizie Zhou, Lu Zhang   +4 more
doaj   +1 more source

Efficient Masked Autoencoder for Birdsong Representation with Applications on Wild Bird Species Classification

Integrative Zoology, EarlyView.
Research on mosquito feeding preferences and the malaria parasites they transmit is essential for understanding the interactions between hosts, vectors, and parasites. In this study, vertebrate hosts were identified in 72 mosquitoes. Most blood meals (58.7%) came from birds, representing 25 species, while 40.0% came from mammals (13 species), and 1.3 ...
Qin Zhang, Shipeng Hu, Hengrui Wang, Jizheng Yi, Guoxiong Zhou, Xiangyu Shen, Weixiong Peng, Zhishu Xiao, Aibin Chen   +8 more
wiley   +1 more source

Reusing Geospatial Data of Invasive Alien Insect Species From the Literature: Significance, Challenges, and Potential

Integrative Zoology, EarlyView.
We use bibliometric analysis to evaluate geospatial data reuse for invasive alien insect species (IAIS). Of 1032 relevant publications, 51.0% lacked downloadable raw data. Integrating cross‐regional, temporal, and multi‐species data could address single‐study limitations, but data scarcity remains a barrier.
Shuhao Tan, Yiqi Xu, Qiaoling Lin, Muzi Ge, Yuting Wu, Jianshi Jin   +5 more
wiley   +1 more source