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Machine Learning and Knowledge Extraction, Volume 5, Issue 1

March 2023 - 20 articles

Cover Story: Explainable AI (XAI) aims to make black-box models more transparent for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners to start with the development of XAI software and to select the most suitable XAI methods. To address this challenge, XAIR is introduced, which is a systematic meta-review of the most promising XAI methods and tools aligned to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. This mapping aims to clarify the steps involved in developing XAI software and to encourage the integration of explainability in AI applications. View this paper
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Articles (20)

  • Article
  • Open Access
6 Citations
3,485 Views
16 Pages

Emergency incidents can appear anytime and any place, which makes it very challenging for emergency medical services practitioners to predict the location and the time of such emergencies. The dynamic nature of the appearance of emergency incidents c...

  • Article
  • Open Access
58 Citations
14,567 Views
26 Pages

Data augmentation is an important procedure in deep learning. GAN-based data augmentation can be utilized in many domains. For instance, in the credit card fraud domain, the imbalanced dataset problem is a major one as the number of credit card fraud...

  • Article
  • Open Access
7 Citations
3,312 Views
17 Pages

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating...

  • Article
  • Open Access
17 Citations
4,730 Views
18 Pages

Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting

  • Federico Cabitza,
  • Andrea Campagner,
  • Chiara Natali,
  • Enea Parimbelli,
  • Luca Ronzio and
  • Matteo Cameli

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI sy...

  • Article
  • Open Access
20 Citations
5,552 Views
17 Pages

A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data

  • Hassan Noroznia,
  • Majid Gandomkar,
  • Javad Nikoukar,
  • Ali Aranizadeh and
  • Mirpouya Mirmozaffari

Today, the chemical corrosion of metals is one of the main problems of large productions, especially in the oil and gas industries. Due to massive downtime connected to corrosion failures, pipeline corrosion is a central issue in many oil and gas ind...

  • Article
  • Open Access
3 Citations
3,438 Views
15 Pages

Investigating the relationship between the movement patterns of multiple limb segments during the rowing stroke on the resulting force production in elite rowers can provide foundational insight into optimal technique. It can also highlight potential...

  • Article
  • Open Access
3,456 Views
38 Pages

InvMap and Witness Simplicial Variational Auto-Encoders

  • Aniss Aiman Medbouhi,
  • Vladislav Polianskii,
  • Anastasia Varava and
  • Danica Kragic

Variational auto-encoders (VAEs) are deep generative models used for unsupervised learning, however their standard version is not topology-aware in practice since the data topology may not be taken into consideration. In this paper, we propose two di...

  • Systematic Review
  • Open Access
75 Citations
34,132 Views
24 Pages

Machine Learning and Prediction of Infectious Diseases: A Systematic Review

  • Omar Enzo Santangelo,
  • Vito Gentile,
  • Stefano Pizzo,
  • Domiziana Giordano and
  • Fabrizio Cedrone

The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational...

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Mach. Learn. Knowl. Extr. - ISSN 2504-4990Creative Common CC BY license