Search Results - (variational OR variation) autoencoder

A lightweight intrusion detection framework using focal loss variational autoencoder for internet of things / Shapla Khanam by Shapla , Khanam

“…Precisely, the CFLVAE model utilizes an efficient and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train Variational AutoEncoder (VAE) to solve the data imbalance problem. …”
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Temporal graph for fraud detection and analytics by Leong, Teng Man

“…A generative approach using graph autoencoders is employed to augment data for fraud detection. …”
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Enhanced computational methods for detection and interpretation of heart disease based on ensemble learning and autoencoder framework / Abdallah Osama Hamdan Abdellatif by Abdalla Osama , Hamdan Abdellatif

“…This approach integrates a conditional variational autoencoder (CVAE) to effectively balance the dataset and a stack predictor (SPFHD) that utilizes tree-based ensemble learning algorithms. …”
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Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors by Hong K.C., Shibghatullah A.S.B., Ling T.C., Sarsam S.M.

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Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke by Zhang , Ke

“…The Whale optimization algorithm-Variational mode decomposition is used to optimally decompose the baby cry signals. …”
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Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture by Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng

“…This study aims to: (i) develop a trigonometric-Euclidean-smoother interpolation (TESI) for continuous time-series and non-time-series data augmentation to prevent DNNs from under/overfitting; (ii) compare the TESI performance to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN); and (iii) compare the DNN performance before and after data augmentation. …”
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Generation of synthetic 5G network dataset using Generative Adversarial Network (GAN) by Azman, Muhammad Amin, Abu-samah, Asma, Khatiman, Muhammad Nur Aqmal, Nordin, Rosdiadee, Abdullah, Nor Fadzilah

“…Generation of data use two types of GAN which are the Conditional Tabular GAN (CTGAN) and Topological Variational Autoencoder (TVAE). The two algorithms were compared based on statistical analysis such as the distribution and Pearson Correlation analysis. …”
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Exploring variable observational time windows for patient–ventilator asynchrony during mechanical ventilation treatment by Ang, Christopher Yew Shuen, Chiew, Yeong Shiong, Wang, Xin, Ooi, Ean Hin, Mat Nor, Mohd Basri, Cove, Matthew E., Chase, J. Geoffrey

“…Analysis of the patient cohort also shows significant intra-patient variability in AI and Masyn,avg , while the inter-patient variation in AI is greater as compared to Masyn,avg . …”
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Video Surveillance Image Enhancement Using Deep Learning by Aminudin, Muhamad Faris Che

“…On the other hand, DLB2 utilize denoising autoencoder to obtain contrast enhancement and noise reduction before reconstructing the input image to a good quality image. …”
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Prediction of rice biomass using machine learning algorithms by Radhwane, Derraz

“…This study aims to: (i) compare the base and ensemble MLs’ model performance, variance, stability, and confidence for predicting rice biomass using collinear (multicollinearity context (MCC)) and non-collinear (non-multicollinearity context (NMCC)) VIs; (ii) compare the rice above ground biomass (TAGB) predictability from noised and Kalman filter’ denoised VIs using histogram gradient boosting regressor (HGBR); (iii) develop a trigonometric-Euclidean-smoother interpolator (TESI), including linear (LN-TESI), cubic (C-TESI), quadratic (Q-TESI), and logarithmic (L-TESI) interpolators, for continuous time-series and non-timeseries VIs data augmentation, and compare them to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN) for preventing DNN’s under/overfitting. …”
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