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Publicações

Publicações por Davide Rua Carneiro

2022

DIGITAL MATURITY: AN OVERVIEW APPLIED TO THE MANUFACTURING INDUSTRY IN THE REGION OF TAMEGA E SOUSA, PORTUGAL

Autores
Duarte, N; Pereira, C; Carneiro, D;

Publicação
12TH INTERNATIONAL SCIENTIFIC CONFERENCE BUSINESS AND MANAGEMENT 2022

Abstract
Digitalization is undoubtedly a major challenge for companies in the coming years. Applying a Design Science methodology this paper aims to describe the process for the development of a solution for obtaining an overview of the Digital Maturity in the manufacturing industry of the region of Tamega e Sousa (an industrial region located in the north of Portugal). The evaluation process consisted of a sample of 53 companies that allowed to get a first picture of the region. Summing up, it is possible to say that a digital strategy is in the companies' plans with a focus on processes digitalization. In general, an overall digital strategy for the companies is in line with the marketing and human resources, in a middle position, with a few companies taking the lead, the majority following, and some others still now awakening to this reality.

2021

Interactive Learning in decision-support: an application to Fraud Detection

Autores
Sousa, M; Carneiro, D;

Publicação
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)

Abstract
Usually, Machine Learning systems are seen as something fully automatic. Recently, however, interactive systems in which human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper, we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time.

2020

Optimizing Instance Selection Strategies in Interactive Machine Learning: An Application to Fraud Detection

Autores
Carneiro, D; Guimarães, M; Sousa, M;

Publicação
Hybrid Intelligent Systems - 20th International Conference on Hybrid Intelligent Systems (HIS 2020), Virtual Event, India, December 14-16, 2020

Abstract
Machine Learning systems are generally thought of as fully automatic. However, in recent years, interactive systems in which Human experts actively contribute towards the learning process have shown improved performance when compared to fully automated ones. This may be so in scenarios of Big Data, scenarios in which the input is a data stream, or when there is concept drift. In this paper we present a system for supporting auditors in the task of financial fraud detection. The system is interactive in the sense that the auditors can provide feedback regarding the instances of the data they use, or even suggest new variables. This feedback is incorporated into newly trained Machine Learning models which improve over time. In this paper we show that the order by which instances are evaluated by the auditors, and their feedback incorporated, influences the evolution of the performance of the system over time. The goal of this paper is to study of different instance selection strategies for Human evaluation and feedback can improve the learning speed. This information can then be used by the system to determine, at each moment, which instances would improve the system the most, so that these can be suggested to the users for validation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Continuously Learning from User Feedback

Autores
Carneiro, D; Sousa, M; Palumbo, G; Guimaraes, M; Carvalho, M; Novais, P;

Publicação
INFORMATION SYSTEMS AND TECHNOLOGIES, WORLDCIST 2022, VOL 1

Abstract
Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed sets of data. In this paper we describe a learning system that tackles some of these novel challenges. It learns and adapts in realtime by continuously incorporating user feedback, in a fully autonomous way. Moreover, it allows for users to manage features (e.g. add, edit, remove), reflecting these changes on-the-fly in the Machine Learning pipeline. The paper describes some of the main functionalities of the system, which despite being of general-purpose, is being developed in the context of a project in the domain of financial fraud detection.

2022

Data-Driven Production Planning Approach Based on Suppliers and Subcontractors Analysis: The Case of the Footwear Cluster

Autores
Ferreira, R; Sousa, C; Carneiro, D; Cardeiro, C;

Publicação
CENTERIS 2022 - International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies 2022, Hybrid Event / Lisbon, Portugal, November 9-11, 2022.

Abstract

2020

A Soft Context-Aware Traffic Management System for Smart Cities

Autores
Carneiro, D; Amaral, A; Carvalho, M;

Publicação
INTELLIGENT ENVIRONMENTS 2020

Abstract

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