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An Enhanced Deep Autoencoder for Flight Delay Prediction

Journal of Aviation/Aerospace Education & Research
Accurate and timely flight delay prediction cannot be overemphasized because of the ever-increasing demand for air travel and its importance in deploying intelligent transportation systems.
Desmond B Bisandu +2 more
doaj +1 more source

Stacked-autoencoder neural network model.

, 2018
Stacked-autoencoder neural network model.
Yongjun Jia (6091283) +3 more
core +1 more source

A Generative Neuro‐Symbolic AI for Protein Sequence Design

Advanced Science, EarlyView.
We introduce EffieDes, a neuro‐symbolic framework coupling deep learning‐based fitness landscape parameterization with exact automated reasoning. Unlike greedy sampling, EffieDes identifies sequences that globally optimize fitness while satisfying intricate design constraints.
Marianne Defresne +12 more
wiley +1 more source

Autoencoder-based detector for distinguishing process anomaly and sensor failure

Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring
Chia-Yen Lee (4389754) +2 more
core +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi +7 more
wiley +1 more source

The Variational InfoMax AutoEncoder [PDF]

, 2020
The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative ...
Bruce Graham +3 more
core +1 more source

A Site‐Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design

Advanced Science, EarlyView.
MolDBG is a site‐aware, sequence‐only framework that unifies drug‐target affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation for structured proteins. Guided by multi‐task binding‐site supervision, it aligns interaction‐critical residues before learning drug‐target representations and simultaneously infers ...
Gang Luo +6 more
wiley +1 more source

Pemetaan Lintasan Karir Alumni Berdasarkan Analisis Cluster: Kombinasi K-Means dan Reduksi Dimensi Autoencoder

Edumatic
Alumni career mapping is a crucial aspect of evaluating and developing higher education programs. Cluster analysis, particularly the integration of k-means and autoencoder methods, has emerged as an effective solution for grouping complex and multi ...
Daru Prasetyawan, Agus Mulyanto, Rahmadhan Gatra +2 more
doaj +1 more source

Comparison of the Effects of Different Dimensional Reduction Algorithms on the Training Performance of Anfis (Adaptive Neuro-Fuzzy Inference System) Model

Cumhuriyet Science Journal, 2017
Adaptive Neuro-Fuzzy Inference Systems (ANFIS)is a hybrid artificial neural network (intelligence) approach that utilizes theability of artificial neural networks to learn, generalize, paralyze and toderive fuzzy logic.
Halil Arslan +4 more
doaj +1 more source

Autoencoder-based fault diagnosis for grinding system

, 2017
At present, most fault diagnosis for grinding system is based on artificial judgments, which is inefficient, low accurate, high cost and easy to cause casualties.
Fu, Dongdong +3 more
core

deep learning
machine learning
anomaly detection