Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives
Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …
tools and data fusion strategies has recently opened new perspectives for environmental …
Application of computational biology and artificial intelligence in drug design
Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …
expense. Booming computational approaches, including computational biology, computer …
An overview of variational autoencoders for source separation, finance, and bio-signal applications
Autoencoders are a self-supervised learning system where, during training, the output is an
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
approximation of the input. Typically, autoencoders have three parts: Encoder (which …
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
Z Cai, S Apolinário, AR Baião, C Pacini… - Nature …, 2024 - nature.com
Integrating diverse types of biological data is essential for a holistic understanding of cancer
biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity …
biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity …
[PDF][PDF] Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder
HG Jung, HS Kim - Materials and Design, 2021 - researchgate.net
One of the fundamental challenges in material science and engineering is the design of
multiple performance materials by considering various microstructural features and their …
multiple performance materials by considering various microstructural features and their …
Benchmarking multi-task learning for sentiment analysis and offensive language identification in under-resourced dravidian languages
To obtain extensive annotated data for under-resourced languages is challenging, so in this
research, we have investigated whether it is beneficial to train models using multi-task …
research, we have investigated whether it is beneficial to train models using multi-task …
A survey on variational autoencoders from a green AI perspective
Abstract Variational Autoencoders (VAEs) are powerful generative models that merge
elements from statistics and information theory with the flexibility offered by deep neural …
elements from statistics and information theory with the flexibility offered by deep neural …
DVAEGMM: Dual variational autoencoder with gaussian mixture model for anomaly detection on attributed networks
A significant aspect of today's digital information is attributed networks, which combine
multiple node attributes with the basic network topology to extract knowledge. Anomaly …
multiple node attributes with the basic network topology to extract knowledge. Anomaly …
An ensemble method for investigating maritime casualties resulting in pollution occurrence: Data augmentation and feature analysis
Timely prediction of maritime casualties resulting in pollution occurrence remains unsolved
in academia, as the significant data imbalance between non-polluting and polluting …
in academia, as the significant data imbalance between non-polluting and polluting …
Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning
Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During
the diagnostic process, ophthalmologists are required to review demographic and clinical …
the diagnostic process, ophthalmologists are required to review demographic and clinical …