Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

During the operation of modern technical systems, the use of the LSTM model for the prediction of process variable values and system states is commonly widespread. The goal of this paper is to expand the application of the LSTM-based models upon obtaining information based on prediction. In this met...

Full description

Saved in:

Bibliographic Details
Main Authors:	János Abonyi, Richárd Károly, Gyula Dörgö
Format:	Article
Language:	English
Published:	Wiley 2021-01-01
Series:	Complexity
Online Access:	http://dx.doi.org/10.1155/2021/7887159
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management
by: László Bántay, et al.
Published: (2022-01-01)

Online identification of cascading event sequences in power systems using deep learning
by: Georgios A. Nakas, et al.
Published: (2025-08-01)

Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
by: Elias Tabane, et al.
Published: (2025-07-01)

Data Reconciliation-Based Hierarchical Fusion of Machine Learning Models
by: Pál Péter Hanzelik, et al.
Published: (2024-11-01)

Simulate Congestion Prediction in a Wireless Network Using the LSTM Deep Learning Model
by: Nada Badr Jarah
Published: (2019-12-01)

Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models
by: Wang Yuxin
Published: (2025-01-01)

Solar and Wind 24 H Sequenced Prediction Using L-Transform Component and Deep LSTM Learning in Representation of Spatial Pattern Correlation
by: Ladislav Zjavka
Published: (2025-07-01)

Sentiment analysis of pilgrims using CNN-LSTM deep learning approach
by: Aisha Alasmari, et al.
Published: (2024-12-01)

Prediction of InSAR deformation time-series using improved LSTM deep learning model
by: Rupika Soni, et al.
Published: (2025-02-01)

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications
by: Tamás Kegyes, et al.
Published: (2021-01-01)

An optimized LSTM-based deep learning model for anomaly network intrusion detection
by: Nitu Dash, et al.
Published: (2025-01-01)

A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning
by: Elysee Nsengiyumva, et al.
Published: (2025-12-01)

Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model
by: Yang He, et al.
Published: (2020-01-01)

Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production
by: László Nagy, et al.
Published: (2021-01-01)

Deep learning for network security: an Attention-CNN-LSTM model for accurate intrusion detection
by: Abdullah Mujawib Alashjaee
Published: (2025-07-01)

Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model
by: Haili Chen, et al.
Published: (2025-01-01)

Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning
by: Ming-xin Jiang, et al.
Published: (2018-01-01)

Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM
by: Widya Utama, et al.
Published: (2025-06-01)

Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction
by: Jayaraman Kumarappan, et al.
Published: (2024-10-01)

LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques
by: Sunitha Sabbu, et al.
Published: (2022-01-01)

Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning
by: Jean Max T. Habib, et al.
Published: (2023-11-01)

Technology-enabled cognitive resilience: what can we learn from military operation to develop Operator 5.0 solutions?
by: Tuan-Anh Tran, et al.
Published: (2024-12-01)

The Detection and Counting of Olive Tree Fruits Using Deep Learning Models in Tacna, Perú
by: Erbert Osco-Mamani, et al.
Published: (2025-02-01)

Version [1.0]- HAT-VIS — A MATLAB-based hypergraph visualization tool
by: Tímea Czvetkó, et al.
Published: (2024-12-01)

A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction
by: Wei Chen, et al.
Published: (2021-01-01)

A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE
by: Vanlalruata Hnamte, et al.
Published: (2023-01-01)

Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids
by: Ahmad N. Alkuwari, et al.
Published: (2025-01-01)

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators
by: Joko Siswanto, et al.
Published: (2024-04-01)

Sentiment analysis of telecom official micro-blog users based on LSTM deep learning model
by: Xin CAI, et al.
Published: (2017-12-01)

Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting
by: Anna Pölz, et al.
Published: (2024-04-01)

LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT
by: Farouk Noumich, et al.
Published: (2025-06-01)

LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting
by: Md R. Kabir, et al.
Published: (2025-01-01)

Advancing enterprise risk management with deep learning: A predictive approach using the XGBoost-CNN-BiLSTM model.
by: Hui Zhang, et al.
Published: (2025-01-01)

Advanced video anomaly detection using 2D CNN and stacked LSTM with deep active learning-based model
by: ANOOPA S, et al.
Published: (2022-06-01)

Anomaly Detection for GOOSE Spoofing Attacks in Digital Substations Using Deep Learning Models: A DNN and LSTM Approach
by: Trinal Fernando, et al.
Published: (2025-01-01)

The problem with two-event sequence learning by pigeons
by: Thomas R. Zentall, et al.
Published: (2024-10-01)

DeepInvesting: Stock market predictions with a sequence-oriented BiLSTM stacked model – A dataset case study of AMZN
by: Ashkan Safari, et al.
Published: (2024-12-01)

Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure
by: Ezgi Çakmak, et al.
Published: (2022-06-01)

Predicting sport event outcomes using deep learning
by: Jianxiong Gao, et al.
Published: (2025-07-01)

Surrogate-based flowsheet model maintenance for Digital Twins
by: Balázs Palotai, et al.
Published: (2025-06-01)