Process mining techniques and applications–A systematic map** study
C dos Santos Garcia, A Meincheim, ERF Junior… - Expert Systems with …, 2019 - Elsevier
Process mining is a growing and promising study area focused on understanding processes
and to help capture the more significant findings during real execution rather than, those …
and to help capture the more significant findings during real execution rather than, those …
A systematic literature review on state-of-the-art deep learning methods for process prediction
Process mining enables the reconstruction and evaluation of business processes based on
digital traces in IT systems. An increasingly important technique in this context is process …
digital traces in IT systems. An increasingly important technique in this context is process …
Enhancing prediction of student success: Automated machine learning approach
H Zeineddine, U Braendle, A Farah - Computers & Electrical Engineering, 2021 - Elsevier
Students' success has recently become a primary strategic objective for most institutions of
higher education. With budget cuts and increasing operational costs, academic institutions …
higher education. With budget cuts and increasing operational costs, academic institutions …
Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
Recurrent neural networks, and in particular long short-term memory (LSTM) networks, are a
remarkably effective tool for sequence modeling that learn a dense black-box hidden …
remarkably effective tool for sequence modeling that learn a dense black-box hidden …
Deep learning for affective computing: Text-based emotion recognition in decision support
Emotions widely affect human decision-making. This fact is taken into account by affective
computing with the goal of tailoring decision support to the emotional states of individuals …
computing with the goal of tailoring decision support to the emotional states of individuals …
Comprehensive review of deep reinforcement learning methods and applications in economics
The popularity of deep reinforcement learning (DRL) applications in economics has
increased exponentially. DRL, through a wide range of capabilities from reinforcement …
increased exponentially. DRL, through a wide range of capabilities from reinforcement …
Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud
Y Wang, W Xu - Decision Support Systems, 2018 - Elsevier
Automobile insurance fraud represents a pivotal percentage of property insurance
companies' costs and affects the companies' pricing strategies and social economic benefits …
companies' costs and affects the companies' pricing strategies and social economic benefits …
Decision support from financial disclosures with deep neural networks and transfer learning
Company disclosures greatly aid in the process of financial decision-making; therefore, they
are consulted by financial investors and automated traders before exercising ownership in …
are consulted by financial investors and automated traders before exercising ownership in …
From Robotic Process Automation to Intelligent Process Automation: – Emerging Trends –
In this survey, we study how recent advances in machine intelligence are disrupting the
world of business processes. Over the last decade, there has been steady progress towards …
world of business processes. Over the last decade, there has been steady progress towards …
Effective long short-term memory with differential evolution algorithm for electricity price prediction
Electric power, as an efficient and clean energy, has considerable importance in industries
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …