Ernesto Tarantino

1.7k total citations
76 papers, 965 citations indexed

About

Ernesto Tarantino is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Ernesto Tarantino has authored 76 papers receiving a total of 965 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 14 papers in Computational Theory and Mathematics and 13 papers in Computer Networks and Communications. Recurrent topics in Ernesto Tarantino's work include Metaheuristic Optimization Algorithms Research (23 papers), Evolutionary Algorithms and Applications (22 papers) and Diabetes Management and Research (12 papers). Ernesto Tarantino is often cited by papers focused on Metaheuristic Optimization Algorithms Research (23 papers), Evolutionary Algorithms and Applications (22 papers) and Diabetes Management and Research (12 papers). Ernesto Tarantino collaborates with scholars based in Italy, Czechia and United States. Ernesto Tarantino's co-authors include Ivanoe De Falco, Antonio Della Cioppa, Umberto Scafuri, Domenico Maisto, Roberto Vaccaro, Marek Tudruj, Giovanna Sannino, Giuseppe De Pietro, Francesco Fontanella and Angelo Marcelli and has published in prestigious journals such as Sensors, Information Sciences and Applied Soft Computing.

In The Last Decade

Ernesto Tarantino

72 papers receiving 898 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ernesto Tarantino Italy 17 533 172 136 124 121 76 965
Ivanoe De Falco Italy 22 675 1.3× 218 1.3× 195 1.4× 141 1.1× 141 1.2× 106 1.3k
Antonio Della Cioppa Italy 19 704 1.3× 300 1.7× 161 1.2× 70 0.6× 42 0.3× 78 1.2k
Xiao Luo China 23 784 1.5× 94 0.5× 471 3.5× 203 1.6× 58 0.5× 129 1.6k
Junwu Zhu China 13 187 0.4× 71 0.4× 217 1.6× 139 1.1× 288 2.4× 88 870
Andreas Janecek Austria 11 367 0.7× 126 0.7× 154 1.1× 82 0.7× 101 0.8× 21 795
Chunxue Wu China 15 177 0.3× 25 0.1× 170 1.3× 112 0.9× 263 2.2× 62 847
Yuichi Nakamura Japan 14 577 1.1× 33 0.2× 113 0.8× 262 2.1× 196 1.6× 65 1.3k
José M. Puerta Spain 19 819 1.5× 171 1.0× 247 1.8× 221 1.8× 85 0.7× 75 1.4k
Hsing‐Chung Chen Taiwan 16 377 0.7× 25 0.1× 105 0.8× 231 1.9× 376 3.1× 130 1.1k

Countries citing papers authored by Ernesto Tarantino

Since Specialization
Citations

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

Fields of papers citing papers by Ernesto Tarantino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ernesto Tarantino

This figure shows the co-authorship network connecting the top 25 collaborators of Ernesto Tarantino. A scholar is included among the top collaborators of Ernesto Tarantino 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 Ernesto Tarantino. Ernesto Tarantino 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.
Falco, Ivanoe De, et al.. (2023). A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction. Sensors. 23(6). 2957–2957. 14 indexed citations
2.
Cioppa, Antonio Della, et al.. (2022). Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment. Sensors. 22(23). 9445–9445. 3 indexed citations
3.
Falco, Ivanoe De, et al.. (2022). An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models. Digital Library (University of West Bohemia). 1–6. 2 indexed citations
4.
Falco, Ivanoe De, et al.. (2020). A Grammatical Evolution Approach for Estimating Blood Glucose Levels. 1–6. 5 indexed citations
5.
Cioppa, Antonio Della, et al.. (2019). De–randomized Meta-Differential Evolution for Calculating and Predicting Glucose Levels. Digital Library (University of West Bohemia). 269–274. 2 indexed citations
6.
Falco, Ivanoe De, Giuseppe De Pietro, Antonio Della Cioppa, et al.. (2019). Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes. Future Generation Computer Systems. 98. 377–391. 21 indexed citations
7.
Falco, Ivanoe De, Giuseppe De Pietro, Giovanna Sannino, et al.. (2018). Deep Neural Network Hyper-Parameter Setting for Classification of Obstructive Sleep Apnea Episodes. 1187–1192. 16 indexed citations
8.
Falco, Ivanoe De, Umberto Scafuri, & Ernesto Tarantino. (2014). Two new fast heuristics for mapping parallel applications on cloud computing. Future Generation Computer Systems. 37. 1–13. 11 indexed citations
9.
Brabazon, Anthony, Gianni A. Di, Rolf Drechsler, et al.. (2011). Applications of Evolutionary ComputationEvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, Proceedings, Part II. PORTO Publications Open Repository TOrino (Politecnico di Torino). 6625. 1–510. 3 indexed citations
10.
Barbiroli, Marina, et al.. (2011). Advantages and disadvantages of the introduction of IMT systems at 800 MHz band. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 16. 1184–1188. 1 indexed citations
11.
Urquhart, Neil, Ernesto Tarantino, Penousal Machado, et al.. (2010). Applications of Evolutionary Computation. Lecture notes in computer science. 3 indexed citations
12.
Falco, Ivanoe De, Umberto Scafuri, & Ernesto Tarantino. (2010). An adaptive multisite mapping for computationally intensive grid applications. Future Generation Computer Systems. 26(6). 857–867. 5 indexed citations
13.
Falco, Ivanoe De, Ernesto Tarantino, Antonio Della Cioppa, & Francesco Fontanella. (2005). A new variable---length genome genetic algorithm for data clustering in semeiotics. 923–927. 2 indexed citations
14.
Falco, Ivanoe De, et al.. (2000). A Kolmogorov complexity-based Genetic Programming tool for string compression. Genetic and Evolutionary Computation Conference. 427–434. 8 indexed citations
15.
Falco, Ivanoe De, et al.. (2000). Towards a Closer Simulation of Natural Mutation: the Translocation Operator. 198–206. 3 indexed citations
16.
Tarantino, Ernesto, et al.. (1999). Towards a Simulation of Natural Mutation. Genetic and Evolutionary Computation Conference. 156–163. 4 indexed citations
17.
Cioppa, Antonio Della, et al.. (1997). Genetic Programming Estimates of Kolmogorov Complexity. international conference on Genetic algorithms. 743–750. 9 indexed citations
18.
Falco, Ivanoe De, et al.. (1997). An analysis of parallel heuristics for task allocation in multicomputers. Computing. 59(3). 259–275. 7 indexed citations
19.
Tarantino, Ernesto, et al.. (1994). Solving the Mapping Problem by Parallel Tabu Search.. Applied Informatics. 264–267. 3 indexed citations
20.
Falco, Ivanoe De, et al.. (1992). SIMULATION OF GENETIC ALGORITHMS ON MIMD MULTICOMPUTERS. Parallel Processing Letters. 2(4). 381–389. 10 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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