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Enhancing Predictive Process Monitoring with Conformal Prediction
This paper introduces a framework that integrates Conformal Prediction (CP) with Predictive Process Monitoring (PPM) to enhance prediction accuracy...
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Bayesian neural hawkes process for event uncertainty prediction
Event data consisting of time of occurrence of the events arises in several real-world applications. A commonly used framework to model such events...
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A hybrid-driven remaining useful life prediction method combining asymmetric dual-channel autoencoder and nonlinear Wiener process
Remaining Useful Life (RUL) prediction is an essential aspect of Prognostics and Health Management (PHM), facilitating the assessment of mechanical...
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MOOC Dropout Prediction Using Learning Process Model and LightGBM Algorithm
With the development and widespread application of Massive Open Online Courses (MOOC) platforms, the issue of reducing learners’ dropout has become a...
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Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression
In recent decades, there has been substantial population growth, leading to a higher volume of vehicles on the roadways. This has contributed to...
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Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process
In this paper, we propose a novel data-driven prediction system for Multivariate Time Series (MTS) in an industrial context, where classic relational...
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Deep learning-based cutting force prediction for machining process using monitoring data
Machining is a critical process in manufacturing industries. With the increase in the complexity and precision of machining, computer systems, such...
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Quantitative Analysis of Gradient Descent Algorithm using scaling methods for improving the prediction process based on Artificial Neural Network
The health development is one of the most important challenges in the world today. All human beings are affected by many diseases due to various...
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Prediction of Process Failure Approach Using Process Mining
Events log are a collection of events that concern a business process. In them, we may find cases where its output is different from what expected....
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Data-driven width spread prediction model improvement and parameters optimization in hot strip rolling process
The width spread is one of the key indices affecting hot rolling processes and product quality. The traditional Shibahara spread prediction model...
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Business process remaining time prediction using explainable reachability graph from gated RNNs
Gated recurrent neural networks (RNNs) are successfully applied to predict the remaining time of business processes. Existing methods typically train...
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Stock Trading Volume Prediction with Dual-Process Meta-Learning
Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading....
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GTHP: a novel graph transformer Hawkes process for spatiotemporal event prediction
The event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic...
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A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction
BackgroundDevelopers spend more time fixing bugs refactoring the code to increase the maintainability than develo** new features. Researchers...
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HGTHP: a novel hyperbolic geometric transformer hawkes process for event prediction
Event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic...
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Graph Neural Networks in PyTorch for Link Prediction in Industry 4.0 Process Graphs
Process mining constitutes an integral part of enterprise infrastructure as its adaptability and evolution potential enhance the digital awareness of...
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PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph...
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Coke Quality Prediction Based on Blast Furnace Smelting Process Data
Coke is the main material of blast furnace smelting. The quality of coke is directly related to the quality of finished products of blast furnace...
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Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling Optimization
In this study, we propose a pioneering framework for generating multi-objective counterfactual explanations in job-shop scheduling contexts,...
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A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques
Bottlenecks arise in many processes, often negatively impacting performance. Process mining can facilitate bottleneck analysis, but research has...