Application of Machine Learning for Post Process Mining Analysis and Problem Detection in Bank

2021 ◽  
pp. 417-427
Author(s):  
Andrey A. Bugaenko
Keyword(s):  
Machine Learning ◽  
Process Mining ◽  
Post Process ◽  
Problem Detection

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals Model-enforced post-process correction of satellite aerosol retrievals

2021 ◽  
Vol 14 (4) ◽  
pp. 2981-2992
Author(s):  
Antti Lipponen ◽  
Ville Kolehmainen ◽  
Pekka Kolmonen ◽  
Antti Kukkurainen ◽  
Tero Mielonen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Data Retrieval ◽  
Post Processing ◽  
Correction Model ◽  
Post Process ◽  
Spectral Bands ◽  
Processing Step ◽  
Retrieval Algorithms ◽  
Data Products

Abstract. Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

scholarly journals Machine Learning and Process Mining applied to Process Optimization: Bibliometric and Systemic Analysis

2019 ◽  
Vol 38 ◽  
pp. 84-91
Author(s):  
Ederson Carvalhar Fernandes ◽  
Barry Fitzgerald ◽  
Liam Brown ◽  
Milton Borsato
Keyword(s):  
Machine Learning ◽  
Process Optimization ◽  
Process Mining ◽  
Systemic Analysis

Prediction of Ventilation Effectiveness for LM9000 Package With Machine Learning

2020 ◽  
Author(s):  
Alessandro Corsini ◽  
Giovanni Delibra ◽  
Marco Giovannelli ◽  
Gabriele Lucherini ◽  
Stefano Minotti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Cfd Simulation ◽  
Ventilation System ◽  
Computational Time ◽  
Suggested Approach ◽  
Gas Leakage ◽  
Post Process ◽  
Artificial Neural ◽  
Cfd Analyses

Abstract Gas turbines usually are installed inside an enclosure, which is used as protection from the external environment and to provide an acoustic insulation. A ventilation system is required to control the temperature inside the enclosed volume and to dilute any potential gas leakage that may come from faulty pipes or flanges. The system has to be properly designed to avoid any unexpected explosion which would generate an overpressure not contained by enclosure walls. The most common approach to predict the effectiveness of the ventilation system requires to perform CFD analyses, which are very expensive in computational terms. A new approach has been proposed by authors, using machine learning and artificial neural networks (ANN) to identify the poorly ventilated zones. This methodology has been further developed, optimized and applied to a real gas turbine packages of new generation. In the present paper the authors will show the application of this procedure to the LM9000 package and the comparison with the results predicted using conventional CFD techniques. The tangible improvement introduced by this methodology is that the computational time is reduced from about three weeks with the common CFD approach to few minutes. The artificial neural network is developed in a Python environment that is applied during the CFX post-process phase of a steady state CFD simulation, providing results equivalent to unsteady CFD simulation. Besides the immediate benefits of this particular application, the suggested approach looks to be a great candidate to substitute the conventional and time-consuming CFD simulations with a fast post-processing algorithm that is able to learn and self-optimize as long as it is used.

scholarly journals Discovering Business Processes from Email Logs Using fastText and Process Mining

2021 ◽  
Author(s):  
Yaghoub Rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
Sina Zamani
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Business Processes ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

scholarly journals Deep Learning Based Post-Process Correction of the Aerosol Parameters in the High-Resolution Sentinel-3 Level-2 Synergy Product

2021 ◽  
Author(s):  
Antti Lipponen ◽  
Jaakko Reinvall ◽  
Arttu Väisänen ◽  
Henri Taskinen ◽  
Timo Lähivaara ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Approximation Error ◽  
Model Parameters ◽  
Post Processing ◽  
Post Process ◽  
Processing Step ◽  
Aerosol Parameters ◽  
Level 2

Abstract. Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modeling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and re-processing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training data set is a less complex and easier task than the direct fully learned machine learning based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. With our approach, there is no need to re-run the existing retrieval algorithms and only a computationally feasible post-processing step is needed. Our approach is based on neural networks trained based on collocated satellite data and accurate ground based AERONET aerosol data. Based on our post-processing approach, we propose a post-process corrected high resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 level-1 data (300 meters at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with better accuracy than the operational Sentinel-3 level-2 Synergy aerosol product or a conventional fully learned machine learning approach.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

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