2021
Authors
Carneiro, D; Guimarães, M; Carvalho, M; Novais, P;
Publication
Trends and Applications in Information Systems and Technologies - Volume 1, WorldCIST 2021, Terceira Island, Azores, Portugal, 30 March - 2 April, 2021.
Abstract
In the last years, developments in data collection, storing, processing and analysis technologies resulted in an unprecedented use of data by organizations. The volume and variety of data, combined with the velocity at which decisions must now be taken and the dynamism of business environments, pose new challenges to Machine Learning. Namely, algorithms must now deal with streaming data, concept drift, distributed datasets, among others. One common task nowadays is to update or re-train models when data changes, as opposed to traditional one-shot batch systems, in which the model is trained only once. This paper addresses the issue of when to update or re-train a model, by proposing an approach to predict the performance metrics of the model if it were trained at a given moment, with a specific set of data. We validate the proposed approach in an interactive Machine Learning system in the domain of fraud detection. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2021
Authors
Anjos Azevedo, P; Rua Carneiro, D;
Publication
Dereito: revista xurídica da Universidade de Santiago de Compostela
Abstract
2021
Authors
Santos, MC; Borges, AI; Carneiro, DR; Ferreira, FJ;
Publication
ICoMS 2021: 4th International Conference on Mathematics and Statistics, Paris, France, June 24 - 26, 2021
Abstract
Breaks in water consumption records can represent apparent losses which are generally associated with the volumes of water that are consumed but not billed. The detection of these losses at the appropriate time can have a significant economic impact on the water company's revenues. However, the real datasets available to test and evaluate the current methods on the detection of breaks are not always large enough or do not present abnormal water consumption patterns. This study proposes an approach to generate synthetic data of water consumption with structural breaks which follows the statistical proprieties of real datasets from a hotel and a hospital. The parameters of the best-fit probability distributions (gamma, Weibull, log-Normal, log-logistic, and exponential) to real water consumption data are used to generate the new datasets. Two decreasing breaks on the mean were inserted in each new dataset associated with one selected probability distribution for each study case with a time horizon of 914 days. Three different change point detection methods provided by the R packages strucchange and changepoint were evaluated making use of these new datasets. Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indices, a higher performance has been observed for the breakpoint method provided by the package strucchange.
2020
Authors
Teymourifar, A; Rodrigues, AM; Ferreira, JS;
Publication
WSEAS TRANSACTIONS ON COMPUTERS
Abstract
2020
Authors
Teymourifar, A; Rodrigues, AM; Ferreira, JS;
Publication
Proceedings - 24th International Conference on Circuits, Systems, Communications and Computers, CSCC 2020
Abstract
This paper compares the non-dominated sorting genetic algorithm (NSGA-II) and NSGA-III to solve multiobjective sectorization problems (MO-SPs). We focus on the effects of the parameters of the algorithms on their performance and we use statistical experimental design to find more effective parameters. For this purpose, the analysis of variance (ANOVA), Taguchi design and response surface method (RSM) are used. The criterion of the comparison is the number of obtained nondominated solutions by the algorithms. The aim of the problem is to divide a region that contains distribution centres (DCs) and customers into smaller and balanced regions in terms of demands and distances, for which we generate benchmarks. The results show that the performance of algorithms improves with appropriate parameter definition. With the parameters defined based on the experiments, NSGA-III outperforms NSGA-II. © 2020 IEEE.
2020
Authors
Costa, E; Soares, AL; Sousa, JP;
Publication
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
Abstract
This paper aims to contribute to the lack of design knowledge on digital platforms (DPs), by studying the new and specific context of DPs managed by industrial business associations (IBAs) to improve the inter- nationalisation of small and medium enterprises (SMEs). A specific objective is to elicit detailed digital plat- form ?s requirements and features for this particular organisational context. A design science research (DSR) approach is adopted to develop design propositions (the artifact), following the context -intervention -me- chanism -outcome logic (CIMO-logic). The design propositions are derived for DPs that can support different types of generative mechanisms of social interaction: information sharing, collaboration, and collective action. The design propositions are obtained by balancing empirical knowledge based on interviews performed with IBAs and SMEs in Portugal and in the UK, with theoretical knowledge from the literature of information systems, DPs and collaborative networks (CNs). The utility of the design propositions is further evaluated by experts and IBAs. The findings are proved to be relevant for practice, mainly for IBAs, SMEs, and digital platform designers to develop more effective collaborative DPs and sociotechnical systems, supporting CNs and the internationalisa- tion needs of SMEs. The knowledge generated in this study brings new design knowledge on DPs, contributing with design propositions translated into tangible and concrete requirements and capabilities, situated in a specific context and empirical setting.
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