Giovanni Paragliola

828 total citations
35 papers, 520 citations indexed

About

Giovanni Paragliola is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Giovanni Paragliola has authored 35 papers receiving a total of 520 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Cognitive Neuroscience. Recurrent topics in Giovanni Paragliola's work include Machine Learning in Healthcare (10 papers), Context-Aware Activity Recognition Systems (7 papers) and Privacy-Preserving Technologies in Data (6 papers). Giovanni Paragliola is often cited by papers focused on Machine Learning in Healthcare (10 papers), Context-Aware Activity Recognition Systems (7 papers) and Privacy-Preserving Technologies in Data (6 papers). Giovanni Paragliola collaborates with scholars based in Italy, Germany and India. Giovanni Paragliola's co-authors include Antonio Coronato, Muddasar Naeem, Giuseppe De Pietro, Patrizia Ribino, Sajid Bashir, Zaib Ullah, Mykola Pechenizkiy, Claudia Di Napoli, Rustam Stolkin and Lorenzo Palma and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Giovanni Paragliola

33 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Giovanni Paragliola Italy 12 238 67 66 65 60 35 520
Muddasar Naeem Italy 12 221 0.9× 40 0.6× 91 1.4× 44 0.7× 59 1.0× 27 598
Tanoy Debnath Bangladesh 7 222 0.9× 131 2.0× 86 1.3× 22 0.3× 79 1.3× 12 561
Fatima Alshehri Saudi Arabia 4 113 0.5× 75 1.1× 120 1.8× 37 0.6× 58 1.0× 6 420
Prableen Kaur India 5 227 1.0× 56 0.8× 55 0.8× 25 0.4× 48 0.8× 9 474
Eman M. G. Younis Egypt 14 326 1.4× 105 1.6× 50 0.8× 102 1.6× 61 1.0× 35 819
Noura Al Moubayed United Kingdom 11 239 1.0× 57 0.9× 26 0.4× 89 1.4× 26 0.4× 59 502
Syed Thouheed Ahmed India 13 194 0.8× 115 1.7× 95 1.4× 28 0.4× 64 1.1× 59 539
Zheng Xiao China 14 323 1.4× 160 2.4× 76 1.2× 61 0.9× 182 3.0× 54 786
Bambang Tutuko Indonesia 15 119 0.5× 110 1.6× 49 0.7× 199 3.1× 80 1.3× 84 769
Gorka Epelde Spain 10 236 1.0× 58 0.9× 24 0.4× 20 0.3× 29 0.5× 45 509

Countries citing papers authored by Giovanni Paragliola

Since Specialization
Citations

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

Fields of papers citing papers by Giovanni Paragliola

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Giovanni Paragliola

This figure shows the co-authorship network connecting the top 25 collaborators of Giovanni Paragliola. A scholar is included among the top collaborators of Giovanni Paragliola 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 Giovanni Paragliola. Giovanni Paragliola 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.
Ribino, Patrizia, Claudia Di Napoli, Giovanni Paragliola, Davide Chicco, & Francesca Gasparini. (2025). Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study. BioData Mining. 18(1). 26–26. 1 indexed citations
2.
Ribino, Patrizia, Maria Mannone, Claudia Di Napoli, et al.. (2025). Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering. BioData Mining. 18(1). 64–64.
3.
Ribino, Patrizia, et al.. (2024). Clustering of longitudinal Clinical Dementia Rating data to identify predictors of Alzheimer's disease progression. Procedia Computer Science. 251. 326–333.
4.
Rastegarpanah, Alireza, et al.. (2024). Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms. Batteries. 10(5). 147–147. 6 indexed citations
5.
Paragliola, Giovanni, Patrizia Ribino, & Zaib Ullah. (2023). A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network. Journal of Sensor and Actuator Networks. 12(6). 78–78. 2 indexed citations
6.
Napoli, Claudia Di, et al.. (2023). Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers. Algorithms. 16(8). 362–362. 5 indexed citations
7.
Pietro, Giuseppe De, et al.. (2022). Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes. Applied Intelligence. 53(11). 14072–14084. 3 indexed citations
8.
Naeem, Muddasar, Antonio Coronato, Zaib Ullah, Sajid Bashir, & Giovanni Paragliola. (2022). Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning. Sensors. 22(21). 8278–8278. 8 indexed citations
9.
Paragliola, Giovanni. (2022). Application of Federated Learning Approaches for Time-Series Classification in eHealth Domain. Procedia Computer Science. 207. 3545–3552. 4 indexed citations
10.
Paragliola, Giovanni & Antonio Coronato. (2021). Definition of a novel federated learning approach to reduce communication costs. Expert Systems with Applications. 189. 116109–116109. 35 indexed citations
11.
12.
Paragliola, Giovanni, et al.. (2021). Definition of an FHIR-based multiprotocol IoT home gateway to support the dynamic plug of new devices within instrumented environments. Journal of Reliable Intelligent Environments. 8(4). 319–331. 7 indexed citations
13.
Coronato, Antonio, Muddasar Naeem, Giuseppe De Pietro, & Giovanni Paragliola. (2020). Reinforcement learning for intelligent healthcare applications: A survey. Artificial Intelligence in Medicine. 109. 101964–101964. 182 indexed citations
14.
Paragliola, Giovanni & Muddasar Naeem. (2019). Risk management for nuclear medical department using reinforcement learning algorithms. Journal of Reliable Intelligent Environments. 5(2). 105–113. 12 indexed citations
15.
Naeem, Muddasar, Antonio Coronato, & Giovanni Paragliola. (2019). Adaptive Treatment Assisting System for Patients Using Machine Learning. 460–465. 12 indexed citations
16.
Paragliola, Giovanni & Antonio Coronato. (2018). A Reinforcement-Learning-Based Approach for the Planning of Safety Strategies in AAL Applications.. 498–505. 1 indexed citations
17.
Paragliola, Giovanni & Antonio Coronato. (2018). Gait Anomaly Detection of Subjects With Parkinson’s Disease Using a Deep Time Series-Based Approach. IEEE Access. 6. 73280–73292. 41 indexed citations
18.
Coronato, Antonio & Giovanni Paragliola. (2017). A structured approach for the designing of safe AAL applications. Expert Systems with Applications. 85. 1–13. 14 indexed citations
19.
Coronato, Antonio, Giuseppe De Pietro, & Giovanni Paragliola. (2014). A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Systems with Applications. 41(17). 7868–7877. 42 indexed citations
20.

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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