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Publicações

Publicações por CEGI

2019

Driverless Cars-Another Piece of the Puzzle

Autores
Dias, TG;

Publicação
IEEE TECHNOLOGY AND SOCIETY MAGAZINE

Abstract

2019

Real-Time Monitoring of a Mobile Ticketing Solution

Autores
Ferreira, MC; Universidade do Porto – Faculdade de Engenharia, Porto, Portugal,; Dias, TG; Cunha, JFe;

Publicação
Journal of Traffic and Logistics Engineering

Abstract

2019

Codesign of a Mobile Ticketing Service Solution Based on BLE

Autores
Ferreira, MC; Universidade do Porto – Faculdade de Engenharia, Porto, Portugal,; Dias, TG; Cunha, JFe;

Publicação
Journal of Traffic and Logistics Engineering

Abstract

2019

Handbook of Service Science, Volume II

Autores
Maglio, PP; Kieliszewski, CA; Spohrer, JC; Lyons, K; Patrício, L; Sawatani, Y;

Publicação
Service Science: Research and Innovations in the Service Economy

Abstract

2019

A Data Mining Approach for Predicting Academic Success – A Case Study

Autores
Martins, MPG; Miguéis, VL; Fonseca, DSB; Alves, A;

Publicação
Advances in Intelligent Systems and Computing

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

2019

Evaluating the short-term effect of cross-market discounts in purchases using neural networks: A case in retail sector

Autores
Migueis, VL; Camanho, AS; Cunha, JFE;

Publicação
EXPERT SYSTEMS

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.

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