A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs by Ping Wang, Hao-Cyuan Li, Hsiao-Chung Lin, Wen-Hui Lin, Nian-Zu Xie

“…This study proposes a Transductive Zero-Shot Learning (TZSL) model based on the Vector Quantized Variational Autoencoder (VQ-VAE) architecture, integrated with a malware knowledge graph constructed from sandbox behavioral analysis of ransomware families. …”
Get full text

A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin by Haonan Jiang, Yanbo Zhao, Qiao Zhu, Yuanli Cai

“…Therefore, a novel variational autoencoder model based on a parallel reference network and neural controlled differential equation (PRN-NCDE) is proposed in this article to solve the problem of reconstructing irregular series under sparse measurements and high noise levels. …”
Get full text

Dataset Construction Using Item Response Theory for Educational Machine Learning Competitions by Takeaki Sakabe, Yuko Sakurai, Emiko Tsutsumi, Satoshi Oyama

“…Additionally, we utilize a conditional variational autoencoder (CVAE) that generates images with specific parameter values. …”
Get full text

Multiscale Dissection of Spatial Heterogeneity by Integrating Multi‐Slice Spatial and Single‐Cell Transcriptomics by Yuqi Chen, Caiwei Zhen, Yuanyuan Mo, Juan Liu, Lihua Zhang

“…The results show SMILE's capability not only in simultaneously dissecting spatial variations at different scales but also in unraveling altered cellular microenvironments in diseased conditions. …”
Get full text

CREATE: cell-type-specific cis-regulatory element identification via discrete embedding by Xuejian Cui, Qijin Yin, Zijing Gao, Zhen Li, Xiaoyang Chen, Hairong Lv, Shengquan Chen, Qiao Liu, Wanwen Zeng, Rui Jiang

“…Here, we present CREATE, a multimodal deep learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE identification and characterization. …”
Get full text

Accelerated inverse design of broadband microperforated panel absorbers based on probabilistic generative model by Zhenyang Huang, Zhongpeng Li, Jinshun Hu, Yongshui Lin, Weiguo Wu, Xiaofei Cao

“…To address the ill-posed nature of inverse problems, particularly when dealing with non-physical or user-defined target spectra, a probabilistic generative model—conditional Variational Autoencoders (cVAEs)—is employed. This model constructs a disentangled latent space, facilitating the generation of multiple feasible geometric solutions for a single user-defined spectras. …”
Get full text

Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach by Waqar Khan, Muhammad Usama, Muhammad Shahbaz Khan, Oumaima Saidani, Hussam Al Hamadi, Noha Alnazzawi, Mohammed S. Alshehri, Jawad Ahmad

“…The model integrates a Transformer-based encoder, Convolutional Neural Networks (CNNs), and Variational Autoencoder-Long Short-Term Memory (VAE-LSTM) networks to enhance anomaly detection capabilities. …”
Get full text

FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model by Yunxiang Li, Hua-Chieh Shao, Xiaoxue Qian, You Zhang

“…The results show that FDDM outperforms generative adversarial network (GAN)-based, variational autoencoder (VAE)-based, and diffusion-based models. …”
Get full text

A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion by Nishu Gupta, Jan Kubicek, Marek Penhaker, Mohammad Derawi

“…Feature extraction was performed using Disentangled Variational Autoencoder (D-VAE), capturing critical texture features. …”
Get full text

Generative AI for Analog Integrated Circuit Design: Methodologies and Applications by Danial Noori Zadeh, Mohamed B. Elamien

“…We provide a methodological review of state-of-the-art machine learning (ML) approaches, including graph neural networks (GNNs), large language models (LLMs), and variational autoencoders (VAEs), which have been successfully applied to analog circuit sizing tasks. …”
Get full text

A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. …”
Get full text

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil by Ralph E Peterson, Aman Choudhri, Catalin Mitelut, Aramis Tanelus, Athena Capo-Battaglia, Alex H Williams, David M Schneider, Dan H Sanes

“…Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. …”
Get full text

Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm by Ghada Moh. Samir Elhessewi, Mohammed Yahya Alzahrani, Mohammad Alamgeer, Abdulbasit A. Darem, Da’ad Albalawneh, Mohammed Alqahtani, Mutasim Al Sadig, Sultan Alanazi

“…Furthermore, the conditional variational autoencoder (CVAE) method is implemented for skin lesion classification. …”
Get full text

Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…In response to the problem of low accuracy in fault diagnosis of mine hoist main bearings under small samples, a VAE-WGAN based mine hoist main bearing sample augmentation model was constructed by fusing variational autoencoder and Wasserstein to generate adversarial networks. …”
Get full text

Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics by Fengda Li, Zeyi Li, Hong Xu, Gang Kong, Ze Zhang, Kaiyuan Cheng, Longyuan Gu, Lei Hua

“…This method, consisting of Variational Autoencoder (VAE), Information Gain (IG) and Convolutional Neural Network (CNN), is compared with four machine learning algorithms (Random Forest, Decision Tree, K-Nearest Neighbour, Gaussian Neff Bayes). …”
Get full text

DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification by Jiecheng Wu, Zhaoliang Chen, Shunxin Xiao, Genggeng Liu, Wenjie Wu, Shiping Wang

“…However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions. …”
Get full text

Urban Thermal Archetype Classification in the Context of Urban Development Transformation Using Machine Learning Techniques by Yan Deng, Huimin Liu

“…The derived urban thermal archetypes demonstrated improved spatial coherence and enhanced interpretation of LST variations, with an increase in <italic>R</italic>&#x00B2; from 0.31 to 0.48 as compared to LCZ. …”
Get full text

Exploring Deep Clustering Methods in Vibro-Acoustic Sensing for Enhancing Biological Tissue Characterization by Robin Urrutia, Diego Espejo, Montserrat Guerra, Karin Vio, Thomas Suhn, Nazila Esmaeili, Axel Boese, Patricio Fuentealba, Alfredo Illanes, Christian Hansen, Victor Poblete

“…We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy (<inline-formula> <tex-math notation="LaTeX">$92~\%$ </tex-math></inline-formula> vs. …”
Get full text

ImproveYourVideos: Architectural Improvements for Text-to-Video Generation Pipeline by Vladimir Arkhipkin, Zein Shaheen, Viacheslav Vasilev, Elizaveta Dakhova, Konstantin Sobolev, Andrey Kuznetsov, Denis Dimitrov

“…However, the architectural aspects of these models remain largely unexplored, as prior studies have used well-known approaches that offer minimal variation between models. This study aims to address this gap by systematically exploring alternative options for text-to-video architecture building blocks, specifically focusing on the temporal layer, frame interpolation model, and autoencoder. …”
Get full text

Domain Generalization Using Maximum Mean Discrepancy Loss for Remaining Useful Life Prediction of Lithium-Ion Batteries by Wenbin Li, Yue Yang, Stefan Pischinger

“…However, diverse aging mechanisms, changing working conditions and cell-to-cell variation lead to the inhomogeneous cell lifespan and complicated life prediction. …”
Get full text