Results 51 to 60 of about 13,513 (170)
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
, 2020 Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature ...
Ger, Stephanie, Klabjan, Diego
core
Neural Network Models for Solar Irradiance Forecasting in Polluted Areas: A Comparative Study
Energy Science &Engineering, EarlyView.Pollution‐aware hybrid ensemble model is proposed to forecast solar irradiance across eight diverse cities. The model integrates MLP, RNN, and NARX to handle varying atmospheric pollution levels. The model outperforms state‐of‐the‐art methods with enhanced accuracy and interpretability on standard solar irradiance data set.
Mujtaba Ali +6 more
wiley +1 more source
A model‐driven robust deep learning wireless transceiver
IET Communications, 2021 Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention.
Sirui Duan, Jingyi Xiang, Xiang Yu
doaj +1 more source
Conditional Generative Modeling for Enhanced Credit Risk Management in Supply Chain Finance
Naval Research Logistics (NRL), EarlyView.ABSTRACT The rapid expansion of cross‐border e‐commerce (CBEC) has created significant opportunities for small‐ and medium‐sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third‐party logistics (3PL)‐led supply chain finance (SCF) has emerged as a promising solution, leveraging in‐transit inventory as ...
Qingkai Zhang, L. Jeff Hong, Houmin Yan
wiley +1 more source
Autoencoders for strategic decision support
, 2020 In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available.
Baesens, Bart +4 more
core
Propellants, Explosives, Pyrotechnics, EarlyView.Physics‐Aware Recurrent Convolutional Neural Networks (PARC) can reliably learn the thermomechanics of energetic materials as a function of morphology. This work introduces LatentPARC, which accelerates PARC by modeling the dynamics in a low‐dimensional latent space.
Zoë J. Gray +5 more
wiley +1 more source
Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants
Sensors, 2023 As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring ...
Liliana Maria Ghinea +2 more
doaj +1 more source
Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou +4 more
wiley +1 more source
Applied Sciences, 2023 Recently, neural network technology has shown remarkable progress in speech recognition, including word classification, emotion recognition, and identity recognition. This paper introduces three novel speaker recognition methods to improve accuracy.
Young-Long Chen +3 more
doaj +1 more source
SVD-AE: Simple Autoencoders for Collaborative Filtering
Accepted by IJCAI ...
Hong, Seoyoung +4 more
openaire +2 more sources