Daniele Loiacono

1.9k total citations
84 papers, 1.2k citations indexed

About

Daniele Loiacono is a scholar working on Artificial Intelligence, Radiation and Molecular Biology. According to data from OpenAlex, Daniele Loiacono has authored 84 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Artificial Intelligence, 12 papers in Radiation and 11 papers in Molecular Biology. Recurrent topics in Daniele Loiacono's work include Artificial Intelligence in Games (31 papers), Evolutionary Algorithms and Applications (30 papers) and Metaheuristic Optimization Algorithms Research (18 papers). Daniele Loiacono is often cited by papers focused on Artificial Intelligence in Games (31 papers), Evolutionary Algorithms and Applications (30 papers) and Metaheuristic Optimization Algorithms Research (18 papers). Daniele Loiacono collaborates with scholars based in Italy, United States and Spain. Daniele Loiacono's co-authors include Pier Luca Lanzi, Luigi Cardamone, Stewart W. Wilson, David E. Goldberg, Julian Togelius, Alper Kanyilmaz, Diego Pérez-Liébana, Yago Sáez, Enrique Onieva and Mike Preuß and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Physics in Medicine and Biology.

In The Last Decade

Daniele Loiacono

82 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniele Loiacono Italy 21 854 201 161 158 157 84 1.2k
David E. Moriarty United States 12 781 0.9× 57 0.3× 24 0.1× 60 0.4× 32 0.2× 20 1.0k
Diaa Salama AbdElminaam Egypt 15 440 0.5× 132 0.7× 16 0.1× 64 0.4× 57 0.4× 77 931
Hamid Tairi Morocco 20 393 0.5× 721 3.6× 65 0.4× 30 0.2× 16 0.1× 170 1.4k
Hala H. Zayed Egypt 15 369 0.4× 662 3.3× 25 0.2× 20 0.1× 26 0.2× 78 1.1k
Miguel Rio United Kingdom 18 268 0.3× 134 0.7× 39 0.2× 35 0.2× 25 0.2× 106 1.3k
Na Zou United States 17 376 0.4× 149 0.7× 99 0.6× 12 0.1× 53 0.3× 70 1.0k
Anirudh Goyal Canada 7 518 0.6× 213 1.1× 14 0.1× 21 0.1× 50 0.3× 38 901
Cong Liu China 17 431 0.5× 335 1.7× 28 0.2× 14 0.1× 14 0.1× 73 1.0k
Erkan Bostancı Türkiye 17 187 0.2× 279 1.4× 17 0.1× 31 0.2× 20 0.1× 91 777
José M. Martínez Spain 19 247 0.3× 1.0k 5.2× 41 0.3× 161 1.0× 19 0.1× 133 1.3k

Countries citing papers authored by Daniele Loiacono

Since Specialization
Citations

This map shows the geographic impact of Daniele Loiacono'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 Daniele Loiacono with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniele Loiacono more than expected).

Fields of papers citing papers by Daniele Loiacono

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniele Loiacono. 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 Daniele Loiacono. The network helps show where Daniele Loiacono may publish in the future.

Co-authorship network of co-authors of Daniele Loiacono

This figure shows the co-authorship network connecting the top 25 collaborators of Daniele Loiacono. A scholar is included among the top collaborators of Daniele Loiacono 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 Daniele Loiacono. Daniele Loiacono 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.
Loiacono, Daniele, F. Lobefalo, Giacomo Reggiori, et al.. (2024). Deep learning‐based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy. Medical Physics. 51(6). 4402–4412. 1 indexed citations
2.
Bramanti, Stéfania, Carmelo Carlo‐Stella, Isabella Castiglioni, et al.. (2023). Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment. Current Oncology. 30(4). 4067–4077. 1 indexed citations
3.
Clerici, Elena, Chiara De Philippis, Daniele Loiacono, et al.. (2023). Internal Guidelines for Reducing Lymph Node Contour Variability in Total Marrow and Lymph Node Irradiation. Cancers. 15(5). 1536–1536. 5 indexed citations
4.
Hernández, Víctor, Isabella Castiglioni, Elena Clerici, et al.. (2023). Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy. Journal of Applied Clinical Medical Physics. 24(6). e13931–e13931. 6 indexed citations
5.
Franzese, Ciro, Maria Ausilia Teriaca, Stefano Tomatis, et al.. (2023). Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review. Journal of Personalized Medicine. 13(6). 946–946. 9 indexed citations
6.
Gozzi, Noemi, Martina Sollini, Margarita Kirienko, et al.. (2022). Image Embeddings Extracted from CNNs Outperform Other Transfer Learning Approaches in Classification of Chest Radiographs. Diagnostics. 12(9). 2084–2084. 7 indexed citations
7.
Mancosu, Pietro, Isabella Castiglioni, Mauro Iori, et al.. (2022). Applications of artificial intelligence in stereotactic body radiation therapy. Physics in Medicine and Biology. 67(16). 16TR01–16TR01. 13 indexed citations
8.
Hernández, Víctor, Isabella Castiglioni, Elena Clerici, et al.. (2022). Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy. Strahlentherapie und Onkologie. 199(4). 412–419. 9 indexed citations
9.
Lanzi, Pier Luca, et al.. (2021). Image Embedding and Model Ensembling for Automated Chest X-Ray Interpretation. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 3 indexed citations
10.
Loiacono, Daniele, et al.. (2015). Procedural weapons generation for unreal tournament III. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 13. 1–8. 10 indexed citations
11.
Loiacono, Daniele, Andreas Lommatzsch, & Roberto Turrin. (2014). An analysis of the 2014 RecSys Challenge. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–6. 4 indexed citations
12.
Lanzi, Pier Luca, et al.. (2014). Evolving maps for match balancing in first person shooters. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–8. 22 indexed citations
13.
Loiacono, Daniele & Mike Preuß. (2013). Computational intelligence and games. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 957–978. 7 indexed citations
14.
Loiacono, Daniele, et al.. (2012). Evolving the optimal racing line in a high-end racing game. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 108–115. 15 indexed citations
15.
Loiacono, Daniele, et al.. (2011). A cheating detection framework for Unreal Tournament III: A machine learning approach. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 266–272. 16 indexed citations
16.
Onieva, Enrique, Luigi Cardamone, Daniele Loiacono, & Pier Luca Lanzi. (2010). Overtaking opponents with blocking strategies using fuzzy logic. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 123–130. 14 indexed citations
17.
Cardamone, Luigi, Daniele Loiacono, & Pier Luca Lanzi. (2009). Evolving competitive car controllers for racing games with neuroevolution. 1179–1186. 38 indexed citations
18.
Loiacono, Daniele, et al.. (2009). Learning a context-aware weapon selection policy for Unreal Tournament III. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 310–316. 2 indexed citations
19.
Lanzi, Pier Luca & Daniele Loiacono. (2006). Standard and averaging reinforcement learning in XCS. 1489–1496. 4 indexed citations
20.
Lanzi, Pier Luca, Daniele Loiacono, Stewart W. Wilson, & David E. Goldberg. (2005). XCS with Computed Prediction in Continuous Multistep Environments. 3. 2032–2039. 18 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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