On the Black-Box Challenge for Fraud Detection Using Machine Learning (II): Nonlinear Analysis through Interpretable Autoencoders

Article Computer Science, Theory & Methods

Discovering prerequisite relations from educational documents through word embeddings

Fabio Gasparetti

Summary: This article discusses a new approach for discovering prerequisites in educational documents based on word embeddings. The method uses statistical language models to identify and classify prerequisites, and its accuracy is validated through experimental benchmarks covering multiple datasets of educational material.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2022)

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Article Chemistry, Multidisciplinary

On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection

Jacobo Chaquet-Ulldemolins et al.

Summary: Artificial intelligence is rapidly shaping the global financial market and its services, showing great potential in credit fraud detection. However, financial institutions face challenges in complying with non-discriminatory and data protection regulations while harnessing the power of AI. This study proposes a reliable, unbiased, and interpretable methodology to automatically evaluate credit fraud risk.

APPLIED SCIENCES-BASEL (2022)

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Article Mathematics

A Mixed Statistical and Machine Learning Approach for the Analysis of Multimodal Trail Making Test Data

Niccolo Pancino et al.

Summary: The study aims to explore the detection of neuropathological syndromes and differentiate between individuals with chronic pain, extrapyramidal patients and healthy controls through eye-tracking technology and data analysis from visual sequential search tests. Statistical tests, novel indicators, and deep learning techniques are used to classify the three patient groups effectively. Preliminary experiments show promising results.

MATHEMATICS (2021)

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Article Computer Science, Artificial Intelligence