Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). Then, the variational mode decomposition (VMD) method is subsequently employed to execute a secondary decomposition on the reconstructed signal, with the decomposition hyperparameters optimized via crested porcupine optimization (CPO). …”
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Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model by Umkalthoom Alzubaidi

“…Furthermore, the FastText method is employed for the word embedding process. Moreover, the variational autoencoder (VAE) model is implemented for emotion classification. …”
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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

“…Four forecasting models were selected based on their ability to capture temporal dependencies and handle missing healthcare data: Transformer with Variational Autoencoder (VAE), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and AutoRegressive Integrated Moving Average (ARIMA). …”
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Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images by Mohammed Alsieni, Khaled H. Alyoubi

“…In addition, the EBSDC-AIFFT model combines the Inception-ResNet-v2 model, the convolutional block attention module-ResNet18 method, and the multi-axis vision transformer technique for feature extraction. Finally, the variational autoencoder (VAE) model is implemented for the classification process. …”
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BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. by Natalie R Davidson, Fan Zhang, Casey S Greene

“…BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Given the scarcity, extreme values, and high volatility of the sample data, a Sequence Variational Autoencoder (SeqVAE) algorithm is employed to generate numerical weather prediction data and corresponding power samples. …”
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Reconstruction of Random Structures Based on Generative Adversarial Networks: Statistical Variability of Mechanical and Morphological Properties by Mikhail Tashkinov, Yulia Pirogova, Evgeniy Kononov, Aleksandr Shalimov, Vadim V. Silberschmidt

“…Generative adversarial neural networks with a variational autoencoder (VAE-GANs) are actively used in the field of materials design. …”
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Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment by Faisal S. Alsubaei, Abdulrahman A. Alshdadi, Mohammed Rizwanullah

“…For monitoring indoor activities, an ensemble of three DL techniques such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and conditional variational autoencoder (CVAE) are employed. Experimental study is performed to underscore the importance of the SIMDP-EDLIoT method under the HAR dataset. …”
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Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…Furthermore, the bidirectional variational autoencoder with the self-attention module (BiVAE‐SAM) model forecasts disability care expenses. …”
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Predicting cell morphological responses to perturbations using generative modeling by Alessandro Palma, Fabian J. Theis, Mohammad Lotfollahi

“…However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions. …”
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Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures by Avijit Paul, Srivalleesha Mallidi

“…Through experimentation with in vitro phantoms, ex vivo mouse organs, and in vivo tumors, we compare basic convolutional autoencoder and U-Net architectures, explore hierarchical depth variations within U-Net, and evaluate advanced variants of U-Net. …”
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Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel by Michael C. Tross, Marcin W. Grzybowski, Talukder Z. Jubery, Ryleigh J. Grove, Aime V. Nishimwe, J. Vladimir Torres‐Rodriguez, Guangchao Sun, Baskar Ganapathysubramanian, Yufeng Ge, James C. Schnable

“…A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. …”
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A rolling bearing life prediction method based on multi-task gated networks by Liuyang SONG, Chuanhao ZHENG, Ye JIN, Tianjiao LIN, Changkun HAN, Huaqing WANG

“…ObjectiveTo achieve the remaining life prediction of bearings in ship mechanical equipment, a multi-task gated networks prediction model based on the Bidirectional Gated Recurrent Unit (BiGRU), Variational Autoencoder (VAE), and Multi-gate Mixture-of-Experts (MMoE) is proposed. …”
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Complying with the EU AI Act: Innovations in explainable and user-centric hand gesture recognition by Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille

“…Leveraging this real-world dataset, we enhance XentricAI’s capabilities by integrating a variational autoencoder module for improved gesture anomaly detection, incorporating user-specific dynamic thresholding. …”
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An integrated framework for multi-commodity agricultural price forecasting and anomaly detection using attention-boosted models by Eko Sediyono, Kristoko Dwi Hartomo, Christian Arthur, Intiyas Utami, Ronny Prabowo, Raymond Chiong

“…To bridge the gap, this study proposes a deep learning framework that integrates Transformer models for price prediction and an attention-boosted LSTM Variational Autoencoder (VAE) for anomaly detection. …”
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Machine Learning Model for Predicting Global Ionospheric TEC Maps Based on Constraint Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…In this context, we propose a machine learning prediction model [predictive GAN variational autoencoder-label (PGVAE-label)] using a labeled graph of image segmentation as a constraint to predict the global ionospheric TEC. …”
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Assessing marine ecosystem risks through unsupervised methods by Laura Pavirani, Pasquale Bove, Gianpaolo Coro

“…This study evaluates six unsupervised methods — four clustering algorithms (Multi K-means, Fuzzy C-means, X-means, and DBSCAN) and two machine-learning models (an Artificial Neural Network, ANN, and a Variational Autoencoder, VAE) — to assess marine ecosystem risk in the Mediterranean Sea automatically, using open-access data from 2017 to 2021. …”
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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…This study proposes a fault diagnosis method using a variational autoencoder (VAE) and domain adaptation neural network (DANN), both of which are based on unsupervised learning, to address this problem. …”
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Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent by Muhammad Omer Farooq, Thomas Kunz

“…Additionally, to enable generalization across different adversarial strategies, the framework employs a variational autoencoder (VAE) that learns compact latent representations of observations, allowing the blue agent to focus on high-level behavioral features rather than raw inputs. …”
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