# Unlabeled learning algorithms and operations: overview and future trends in defense sector

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/unlabeled-learning-algorithms-and-operations-overview-and-future-trends-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Eduardo e Oliveira,Marco Rodrigues,João Paulo Góes Pereira,António M. Lopes,Ivana Ilic Mestric,S. Bjelogrlic |
| Citations | 18 |
| DOI | 10.1007/s10462-023-10692-0 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s10462-023-10692-0.pdf |
| OpenAlex ID | https://openalex.org/W4391965926 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Eduardo e Oliveira](https://scholariq.org/researchers/eduardo-e-oliveira/)

## Paper journal

- [Artificial Intelligence Review](https://scholariq.org/journals/artificial-intelligence-review/)

## Paper primary topic

- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)

## Paper topics

- [Adversarial Robustness in Machine Learning](https://scholariq.org/topics/adversarial-robustness-in-machine-learning/)
- [Explainable Artificial Intelligence (XAI)](https://scholariq.org/topics/explainable-artificial-intelligence-xai/)
- [Machine Learning and Data Classification](https://scholariq.org/topics/machine-learning-and-data-classification/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.