Results 41 to 50 of about 5,065 (216)

Anomaly Detection for Agricultural Vehicles Using Autoencoders

Sensors, 2022
The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this
Esma Mujkic, Mark P. Philipsen, Thomas B. Moeslund, Martin P. Christiansen, Ole Ravn   +4 more
doaj   +1 more source

Orthogonal Matrix-Autoencoder-Based Encoding Method for Unordered Multi-Categorical Variables with Application to Neural Network Target Prediction Problems

Applied Sciences
Neural network models, such as BP, LSTM, etc., support only numerical inputs, so data preprocessing needs to be carried out on the categorical variables to convert them into numerical data.
Yiying Wang, Jinghua Li, Boxin Yang, Dening Song, Lei Zhou   +4 more
doaj   +1 more source

Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

IEEE Access, 2020
In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied.
Kyungnam Park, Jaeik Jeong, Dongjoo Kim, Hongseok Kim   +3 more
doaj   +1 more source

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

Remote Sensing, 2023
Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis.
Nan Wang, Yuetian Shi, Haiwei Li, Geng Zhang, Siyuan Li, Xuebin Liu   +5 more
doaj   +1 more source

Deep Unsupervised Learning for Indoor Fire Detection Using Wi-Fi Signals

Fire
This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality.
Sara Mostofi, Fatih Yesevi Okur, Ahmet Can Altunişik, Ertugrul Taciroğlu   +3 more
doaj   +1 more source

AE SemRL: Learning Semantic Association Rules with Autoencoders

CoRR
Association Rule Mining (ARM) is the task of learning associations among data features in the form of logical rules. Mining association rules from high-dimensional numerical data, for example, time series data from a large number of sensors in a smart environment, is a computationally intensive task.
Erkan Karabulut, Victoria Degeler, Paul Groth   +2 more
openaire   +2 more sources

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

An Efficient Intrusion Detection Method Based on LightGBM and Autoencoder

, 2020
Due to the insidious characteristics of network intrusion behaviors, developing an efficient intrusion detection system is still a big challenge, especially in the era of big data where the number of traffic and the dimension of each traffic feature are ...
Chaofei Tang, Yuxin Zhao, Nurbol Luktarhan   +2 more
core   +1 more source

ResNet Autoencoders for Unsupervised Feature Learning From High-Dimensional Data: Deep Models Resistant to Performance Degradation

IEEE Access, 2021
Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual
Chathurika S. Wickramasinghe, Daniel L. Marino, Milos Manic   +2 more
doaj   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source