Search Results - boosting adversarial autoencoder~

DBN-BAAE: Enhanced Lightweight Anomaly Detection Mechanism with Boosting Adversarial Autoencoder by Chen, Yanru, Wu, Bei, Zhong, Wang, Guo, Yanru, Wu, Dizhi, Ren, Yi, Zhang, Yuanyuan

“… To address these issues, this work introduces a deep belief network-based boosting adversarial autoencoder termed DBN-BAAE, a novel lightweight anomaly detection mechanism based on boosting adversarial…”
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Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting by Demir, Sumeyra, Mincev, Krystof, Kok, Koen, Paterakis, Nikolaos G.

“…, can, however, counteract the restrictions imposed by small sample sizes: increasing a model’s robustness to overfitting and boosting out-of-sample prediction accuracies…”
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Unified multi-protocol MRI for Alzheimer’s disease diagnosis: Dual-decoder adversarial autoencoder and ensemble residual shrinkage attention network by Li, Shiyao, Lin, Shukuan, Tu, Yue, Qiao, Jianzhong, Xiao, Shenao

“… Specifically, we introduce a double decoder adversarial autoencoder (DDAAE) to align MRIs from different protocols…”
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Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection by Krishnamoorthy, Suresh Kumar, Vanitha CN

“… The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages…”
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LLM-AE-MP: Web Attack Detection Using a Large Language Model with Autoencoder and Multilayer Perceptron by Yang, Jing, Wu, Yuangui, Yuan, Yuping, Xue, Haozhong, Bourouis, Sami, Abdel-Salam, Mahmoud, Prajapat, Sunil, Por, Lip Yee

“…) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic…”
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scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing by Li, Bingrui, Zhou, Xiaobo, Kalluri, Raghu

“… (Estimating cell Proportions using single-cell RNA-seq Reference), a rigorous approach combining adversarial autoencoder and extreme gradient boosting to estimate tumor…”
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Bringing realism: Enhancing high-dimensional data for active behavior analysis in older adults by Guo, Cong, Jiang, Yaoqin, Yang, Yitong, Yuan, Zhilu, Guo, Renzhong

“… To address these issues, a data generation algorithm is proposed based on 7631 data points from a case study of the household travel survey in Xiamen, China, comparing the Variational Autoencoder (VAE…”
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WAVAE: A Weakly Augmented Variational Autoencoder for Time Series Anomaly Detection by Wu, Zhangkai, Zhang, Qi, Zhou, Junxian, Chen, Hui, Liu, Yanfang

“…In this work, we present WAVAE, a Weakly Augmented Variational Autoencoder that incorporates self-supervised learning (SSL…”
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Application of Machine Learning and Data Mining

“…The following special issue contains 14 articles accepted and published in the Special Issue "Mathematics and Computer Science, 2024" of the MDPI journal…”
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Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications by Bae, Wan D., Alkobaisi, Shayma, Horak, Matthew, Bankar, Siddheshwari, Bhuvaji, Sartaj, Kim, Sungroul, Park, Choon-Sik

“…With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields,…”
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Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set by Liu, Haiqing, Li, Zhiqiao, Li, Yuancheng

“…), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM…”
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Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction by Tseng, Yu-Hsuan, Wen, Chih-Yu

“… Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost…”
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Industrial Control System Anomaly Detection and Classification Based on Network Traffic by Jiang, Jehn-Ruey, Chen, Yan-Ting

“… First, the denoising autoencoder (DAE) is utilized to reduce data noise and extract core features from data…”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Salehiyan, Ahmad, Moghaddam, Pardis Sadatian, Kaveh, Masoud

“…The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced…”
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Multiple imputation using the average code from autoencoders by Macias, Edwar, Serrano, Javier, Lopez Vicario, Jose, Morell, Antoni

“…•The computation of the average code from an autoencoder Improves the imputation of missing values…”
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WarpDrive: Boosting Auto Loan Default Data Processing with GAN-Powered Data Augmentation and SAE-Based Rectification by Xueping, Zhu, Lin, Qin, Samuri, Suzani Mohamad, Adnan, Muhamad Hariz Muhamad

“…Accurately predicting loan defaults in automotive loan applications is crucial for financial institutions. However, imbalanced and anomalous datasets often…”
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An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems by Awad, Zeinab, Zakaria, Magdy, Hassan, Rasha

“… However, recent studies have revealed their vulnerability to adversarial attacks, where maliciously perturbed traffic samples can deceive trained DNN-based detectors, leading to incorrect…”
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Generative hybrid models for fraud detection in auto insurance with a comparative analysis of VAE, GAN, and diffusion approaches by Bekkaye, Chadia, Oukhouya, Hassan, Zari, Tarek, Guerbaz, Raby, El Bouanani, Hicham

“…), Generative Adversarial Networks (GANs), and Diffusion Models (DMs)—with an ensemble of classifiers including eXtreme Gradient Boosting (XGBoost), Random Forest (RF…”
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Practical Guide to Machine Learning, NLP, and Generative AI: Libraries, Algorithms, and Applications by Mariprasath T, Rivera Marco, Cheepati Kumar Reddy

“…This hands-on guide explores the core concepts and cutting-edge techniques in machine learning, natural language processing (NLP), and generative AI. It covers…”
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A generative design framework for passive thermal control with macroscopic metamaterials by Ignuta-Ciuncanu, Matei C., Tabor, Philip, Martinez-Botas, Ricardo F.

“… encounters limitations in controlling heat flux within confined spaces. The advent of metamaterials presents an energy-free opportunity to transform thermal control, essential to boosting system efficiency…”
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