[HTML][HTML] Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review
I Malashin, V Tynchenko, A Gantimurov, V Nelyub… - …, 2024 - pmc.ncbi.nlm.nih.gov
This review explores the application of Long Short-Term Memory (LSTM) networks, a
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
Next Activity Prediction: An application of shallow learning techniques against deep learning over the BPI Challenge 2020
Business Process Management is a domain that is composed by different research or
application areas. Process Mining is one of them and it is a data-driven approach to analyze …
application areas. Process Mining is one of them and it is a data-driven approach to analyze …
SuTraN: an Encoder-Decoder Transformer for Full-Context-Aware Suffix Prediction of Business Processes
B Wuyts, SV Broucke… - 2024 6th International …, 2024 - ieeexplore.ieee.org
Predictive Process Monitoring (PPM) in Process Mining (PM) uses predictive analytics to
forecast business process progression. A key challenge is suffix prediction, forecasting …
forecast business process progression. A key challenge is suffix prediction, forecasting …
Predictive process monitoring: concepts, challenges, and future research directions
Abstract Predictive Process Monitoring (PPM) extends classical process mining techniques
by providing predictive models that can be applied at runtime during the execution of a …
by providing predictive models that can be applied at runtime during the execution of a …
PROPHET: explainable predictive process monitoring with heterogeneous graph neural networks
V Pasquadibisceglie, R Scaringi… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
In this paper, we introduce PROPHET, an innovative approach to predictive process
monitoring based on Heterogeneous Graph Neural Networks. PROPHET is designed to …
monitoring based on Heterogeneous Graph Neural Networks. PROPHET is designed to …
Explainable and Effective Process Remaining Time Prediction Using Feature-Informed Cascade Prediction Model
Predictive Process Monitoring aims to predict the future information of ongoing process
executions by leveraging machine and deep learning techniques. One of the tasks is known …
executions by leveraging machine and deep learning techniques. One of the tasks is known …
Predictive process model monitoring using long short-term memory networks
J De Smedt, J De Weerdt - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
The field of predictive process monitoring focuses on case-level models to predict a single
specific outcome such as a particular objective,(remaining) time, or next activity/remaining …
specific outcome such as a particular objective,(remaining) time, or next activity/remaining …
Validation set sampling strategies for predictive process monitoring
Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent
neural networks in predictive process monitoring and their ability to capture the underlying …
neural networks in predictive process monitoring and their ability to capture the underlying …
LS-ICE: A Load State Intercase Encoding framework for improved predictive monitoring of business processes
BR Gunnarsson, S vanden Broucke, J De Weerdt - Information Systems, 2024 - Elsevier
Research on developing techniques for predictive process monitoring has generally relied
on feature encoding schemes that extract intra-case features from events to make …
on feature encoding schemes that extract intra-case features from events to make …
On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations
and 3D point clouds, to support fast analytics and learning. However, when processing large …
and 3D point clouds, to support fast analytics and learning. However, when processing large …