Georgina Cosma

2.5k total citations · 1 hit paper
78 papers, 1.7k citations indexed

About

Georgina Cosma is a scholar working on Artificial Intelligence, Information Systems and Safety Research. According to data from OpenAlex, Georgina Cosma has authored 78 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 16 papers in Information Systems and 14 papers in Safety Research. Recurrent topics in Georgina Cosma's work include Academic integrity and plagiarism (13 papers), Sentiment Analysis and Opinion Mining (8 papers) and Software Engineering Research (7 papers). Georgina Cosma is often cited by papers focused on Academic integrity and plagiarism (13 papers), Sentiment Analysis and Opinion Mining (8 papers) and Software Engineering Research (7 papers). Georgina Cosma collaborates with scholars based in United Kingdom, Iraq and China. Georgina Cosma's co-authors include Mike Joy, Aboozar Taherkhani, T.M. McGinnity, Liam Maguire, Yuhua Li, Ammar Belatreche, Giovanni Acampora, David J. Brown, A. Graham Pockley and Masood A. Khan and has published in prestigious journals such as PLoS ONE, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Georgina Cosma

72 papers receiving 1.6k citations

Hit Papers

A review of learning in b... 2019 2026 2021 2023 2019 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A review of learning in biologically plausible spiking neural networks
    2019 268 citations Aboozar Taherkhani, Georgina Cosma et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Georgina Cosma United Kingdom 21 580 343 310 299 234 78 1.7k
Ruggero Donida Labati Italy 23 393 0.7× 417 1.2× 85 0.3× 129 0.4× 916 3.9× 75 1.9k
Jie M. Zhang China 21 931 1.6× 711 2.1× 121 0.4× 259 0.9× 130 0.6× 75 2.2k
Zahid Akhtar United States 23 424 0.7× 413 1.2× 85 0.3× 80 0.3× 952 4.1× 123 2.0k
Richard Jiang United Kingdom 20 550 0.9× 106 0.3× 137 0.4× 42 0.1× 474 2.0× 98 1.7k
Blaise Agüera y Arcas United States 10 2.3k 3.9× 378 1.1× 482 1.6× 53 0.2× 262 1.1× 17 3.1k
Jacob W. Crandall United States 19 683 1.2× 117 0.3× 52 0.2× 205 0.7× 255 1.1× 53 1.9k
Gongping Yang China 28 409 0.7× 289 0.8× 98 0.3× 190 0.6× 1.2k 5.1× 136 3.0k
Peter Corcoran Ireland 27 545 0.9× 672 2.0× 435 1.4× 53 0.2× 1.2k 5.0× 264 3.3k
Sanjeev Sofat India 18 361 0.6× 371 1.1× 76 0.2× 52 0.2× 219 0.9× 81 1.4k
Christin Seifert Germany 19 807 1.4× 161 0.5× 45 0.1× 67 0.2× 393 1.7× 100 1.5k

Countries citing papers authored by Georgina Cosma

Since Specialization
Citations

This map shows the geographic impact of Georgina Cosma's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Georgina Cosma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georgina Cosma more than expected).

Fields of papers citing papers by Georgina Cosma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Georgina Cosma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Georgina Cosma. The network helps show where Georgina Cosma may publish in the future.

Co-authorship network of co-authors of Georgina Cosma

This figure shows the co-authorship network connecting the top 25 collaborators of Georgina Cosma. A scholar is included among the top collaborators of Georgina Cosma based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Georgina Cosma. Georgina Cosma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cosma, Georgina, et al.. (2025). Neural Machine Unranking. IEEE Transactions on Neural Networks and Learning Systems. PP. 1–12.
2.
Cosma, Georgina, et al.. (2025). Nature and prevalence of long-term conditions in people with intellectual disability: retrospective longitudinal population-based study. BMJ Open. 15(1). e090857–e090857. 1 indexed citations
3.
Cosma, Georgina, et al.. (2024). Advancing continual lifelong learning in neural information retrieval: Definition, dataset, framework, and empirical evaluation. Information Sciences. 687. 121368–121368. 1 indexed citations
4.
Cosma, Georgina, et al.. (2024). VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-modal Information Retrieval. ACM Transactions on Knowledge Discovery from Data. 18(9). 1–21.
5.
6.
Cosma, Georgina, et al.. (2023). Clip-Rr: Improved Clip Network for Relation-Focused Cross-Modal Information Retrieval. SSRN Electronic Journal. 1 indexed citations
7.
Cosma, Georgina, et al.. (2022). A Framework for Enabling Unpaired Multi-Modal Learning for Deep Cross-Modal Hashing Retrieval. Journal of Imaging. 8(12). 328–328. 1 indexed citations
8.
Giangrande, Paolo, Vincenzo Madonna, Georgina Cosma, et al.. (2021). Lifetime Estimation of Enameled Wires Under Accelerated Thermal Aging Using Curve Fitting Methods. IEEE Access. 9. 18993–19003. 15 indexed citations
9.
Cosma, Georgina, et al.. (2021). ArSL-CNN a convolutional neural network for Arabic sign language gesture recognition. Indonesian Journal of Electrical Engineering and Computer Science. 22(2). 1096–1096. 30 indexed citations
10.
Cosma, Georgina, et al.. (2021). Generalisation Power Analysis for finding a stable set of features using evolutionary algorithms for feature selection. Knowledge-Based Systems. 231. 107450–107450. 4 indexed citations
11.
12.
Giangrande, Paolo, Vincenzo Madonna, Georgina Cosma, et al.. (2020). The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines. IEEE Access. 8. 40283–40297. 19 indexed citations
13.
14.
Taherkhani, Aboozar, Georgina Cosma, & T.M. McGinnity. (2018). Deep-FS: A feature selection algorithm for Deep Boltzmann Machines. Neurocomputing. 322. 22–37. 54 indexed citations
15.
16.
Cosma, Georgina, et al.. (2017). Perceptual Comparison of Source-Code Plagiarism within Students from UK, China, and South Cyprus Higher Education Institutions. ACM Transactions on Computing Education. 17(2). 1–16. 10 indexed citations
17.
Acampora, Giovanni & Georgina Cosma. (2014). A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews. Nottingham Trent University's Institutional Repository (Nottingham Trent Repository). 73–80. 5 indexed citations
18.
Cosma, Georgina & Mike Joy. (2012). Evaluating the performance of LSA for source-code plagiarism detection. Warwick Research Archive Portal (University of Warwick). 18 indexed citations
19.
Joy, Mike, Georgina Cosma, Jane Sinclair, & Jane Yin-Kim Yau. (2009). A taxonomy of plagiarism in computer science. Warwick Research Archive Portal (University of Warwick). 3372–3379. 10 indexed citations
20.
Pero, R W, G Berglund, Nelwyn T. Christie, et al.. (1993). The Malmö biomarker programme. Journal of Internal Medicine. 233(1). 69–74. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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