Details
Name
Vera MiguéisCluster
Industrial and Systems EngineeringRole
Senior ResearcherSince
01st July 2013
Nationality
PortugalCentre
Industrial Engineering and ManagementContacts
+351 22 209 4190
vera.migueis@inesctec.pt
2022
Authors
Oliveira, EE; Migueis, VL; Borges, JL;
Publication
IEEE ACCESS
Abstract
Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.
2022
Authors
Oliveira, EE; Migueis, VL; Borges, JL;
Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.
2022
Authors
Migueis, VL; Pereira, A; Pereira, J; Figueira, G;
Publication
JOURNAL OF CLEANER PRODUCTION
Abstract
2022
Authors
Ortiz, D; Migueis, V; Leal, V; Knox Hayes, J; Chun, J;
Publication
SUSTAINABILITY
Abstract
2022
Authors
Paulo, M; Migueis, VL; Pereira, I;
Publication
EXPERT SYSTEMS WITH APPLICATIONS
Abstract
Despite being one of the most cost-effective methods, email marketing remains challenging due to the low rate of opened emails and the high percentage of unsubscribed campaigns. Since the sender and the subject line are the only information that the recipient sees at first when receiving an email, the decision to open an email critically depends on these two factors, which should stand out and catch the recipient's attention. Therefore, the motivation behind this study is to support email campaign editors in choosing a subject line based on its potential quality. We propose and compare several models to measure the quality of a subject line, considering its potential to promote the email opening. The subject lines' structure and content are explored together with different machine learning techniques (Random Forest, Decision Trees, Neural Networks, Naive Bayes, Support Vector Machines, and Gradient Boosting). To validate the proposed model, a data set of 140,000 emails' subject lines was used. The results revealed that the models proposed are very promising to support the definition of the email marketing subject lines and show that the combination of data regarding the structure, the content of the subject lines, and senders characteristics leads to more accurate classifications of the potential of the subject line.
Supervised Thesis
2021
Author
Francisco Teixeira Ferreira
Institution
UP-FEUP
2021
Author
Cláudia Monteiro
Institution
UP-FEP
2021
Author
Eduardo Luís de Meireles e Oliveira
Institution
UP-FEUP
2021
Author
Paulo Miguel Novais Gameiro
Institution
UM
2021
Author
Joaquim Manuel Gonçalves Oliveira
Institution
UM
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