# National University of Ireland, Maynooth

**Type:** Institutions  
**Canonical URL:** https://scholariq.org/institutions/national-university-of-ireland-maynooth/

## Facts

| Field | Value |
| --- | --- |
| Avg h-index | 56.6 |
| Citations | 809,015 |
| City | Maynooth |
| Country | IE |
| Description | National University of Ireland, Maynooth is a research organisation in Maynooth, IE. OpenAlex records 21,916 works and 809,015 citations for it. 2,827 researchers list it as their most recent affiliation. |
| Homepage | https://www.maynoothuniversity.ie/ |
| OpenAlex ID | https://openalex.org/I157286207 |
| Researchers | 2,827 |
| ROR ID | https://ror.org/048nfjm95 |
| Type | education |
| Wikidata ID | https://www.wikidata.org/wiki/Q2601879 |
| Works | 21,916 |

## University papers

- [A machine learning approach to recognize bias and discrimination in job advertisements](https://scholariq.org/papers/a-machine-learning-approach-to-recognize-bias-and-discrimination-in-job/)
- [A machine learning approach to detecting fraudulent job types](https://scholariq.org/papers/a-machine-learning-approach-to-detecting-fraudulent-job-types/)
- [A Context-Aware Approach for Extracting Hard and Soft Skills](https://scholariq.org/papers/a-context-aware-approach-for-extracting-hard-and-soft-skills/)
- [An approach to information retrieval and question answering in the legal domain](https://scholariq.org/papers/an-approach-to-information-retrieval-and-question-answering-in-the-legal-domain/)
- [NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity](https://scholariq.org/papers/normas-at-semeval-2016-task-1-semsim-a-multi-feature-approach-to-semantic-text/)
- [Comparative analysis of PCA-based and Neural Network based face recognition systems](https://scholariq.org/papers/comparative-analysis-of-pca-based-and-neural-network-based-face-recognition/)
- [The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks](https://scholariq.org/papers/the-pv-ale-dataset-enhancing-apple-leaf-disease-classification-through-transfer-2/)
- [Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper](https://scholariq.org/papers/beyond-binary-towards-embracing-complexities-in-cyberbullying-detection-and/)
- [Textual Inference with Deep Learning Technique](https://scholariq.org/papers/textual-inference-with-deep-learning-technique/)
- [Solving Bar Exam Questions with Deep Neural Networks.](https://scholariq.org/papers/solving-bar-exam-questions-with-deep-neural-networks/)
- [The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks](https://scholariq.org/papers/the-pv-ale-dataset-enhancing-apple-leaf-disease-classification-through-transfer/)

## University researchers

- [Kolawole John Adebayo](https://scholariq.org/researchers/kolawole-john-adebayo/)

## University top topics

Showing 8 of 19.

- [Climate variability and models](https://scholariq.org/topics/climate-variability-and-models/)
- [Cosmology and Gravitation Theories](https://scholariq.org/topics/cosmology-and-gravitation-theories/)
- [Geographic Information Systems Studies](https://scholariq.org/topics/geographic-information-systems-studies/)
- [Quantum Chromodynamics and Particle Interactions](https://scholariq.org/topics/quantum-chromodynamics-and-particle-interactions/)
- [Behavioral and Psychological Studies](https://scholariq.org/topics/behavioral-and-psychological-studies/)
- [Employment and Welfare Studies](https://scholariq.org/topics/employment-and-welfare-studies/)
- [Radio Astronomy Observations and Technology](https://scholariq.org/topics/radio-astronomy-observations-and-technology/)
- [Stability and Control of Uncertain Systems](https://scholariq.org/topics/stability-and-control-of-uncertain-systems/)

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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.