Gian Marco Paldino

Gian Marco Paldino
  • Université Libre de Bruxelles

About

14
Publications
5,762
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194
Citations
Current institution
Université Libre de Bruxelles

Publications

Publications (14)
Article
Dynamic Thermal Rating (DTR) enhances grid flexibility by adapting line capabilities to weather conditions. For this purpose, DTR-based technologies require reliable and continuous measurement of the conductor temperature along the line route, which could hinder their wide-scale deployment due to the prohibitively high number of required sensors. E...
Article
The volume of e-commerce continues to increase year after year. Buying goods on the internet is easy and practical, and took a huge boost during the lockdowns of the Covid crisis. However, this is also an open window for fraudsters and the corresponding financial loss costs billions of dollars. In this paper, we study e-commerce credit card fraud d...
Article
Full-text available
Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest. This paper addresses the issue of imbalanced learning in credit card fraud detection by introducing a novel approach that models fraudulent behavior as a time-dependent process....
Article
Full-text available
A hybrid rule-based/ML approach using linear regression and artificial neural networks (ANNs) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316 L stainless steel. Non-parametric density estimation determined the central tendencies of the E pit /log( jpit ) and E...
Preprint
Full-text available
A hybrid rule-base/ML approach using linear regression and artificial neural networks (ANN) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316L stainless steel. Non-parametric density estimation determined the central tendencies of the E pit /log( jpit ) and E pas...
Data
https://data.mendeley.com/datasets/78rz8vw46x/2 This database comprises 5 Potentiodynamic Polarisation (PP) datasets. Each dataset consists of a pair of CSVs: 1 file containing the values of the applied potential scan rate (mV/s); and 1 having the corresponding current density j (µA/cm²) values.
Data
https://github.com/bcoelho-leonardo/Data-driven-analysis-of-the-local-current-distributions-of-316L-corrosion-in-NaCl-solution/blob/4efff485b115468840b25ea56ad81b31711c0f51/local%20current%20distributions%20of%20316L%20corrosion.ipynb
Article
The number of daily credit card transactions is inexorably growing: the e-commerce market expansion and the recent constraints for the Covid-19 pandemic have significantly increased the use of electronic payments. The ability to precisely detect fraudulent transactions is increasingly important, and machine learning models are now a key component o...
Preprint
Full-text available
This investigation proposes using Scanning Electrochemical Cell Microscopy (SECCM) as a high throughput tool to collect corrosion activity datasets from randomly probed locations on electropolished 316L SS. In the presence of chloride (varying concentrations), potentiodynamic polarisation tests (varied scan rates) triggered the development of pitti...
Article
Full-text available
The limitation of transmission lines thermal capacity plays a crucial role in the safety and reliability of power systems. Dynamic thermal line rating approaches aim to estimate the transmission line’s temperature and assess its compliance with the limitations above. Existing physics-based standards estimate the temperature based on environment and...
Article
Full-text available
Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies used in production are still based on batch learning, which is inadequate for two reasons: Models quick...
Article
Full-text available
The availability of massive amounts of temporal data opens new perspectives of knowledge extraction and automated decision making for companies and practitioners. However, learning forecasting models from data requires a knowledgeable data science or machine learning (ML) background and expertise, which is not always available to end-users. This ga...

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