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Giovanni Paragliola
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2020 – today
- 2024
- [j19]Giovanni Paragliola
, Patrizia Ribino
Exploring heterogeneous data distribution issues in e-health federated systems. Biomed. Signal Process. Control. 92: 106039 (2024) - [j18]Sara Campanella, Giovanni Paragliola, Valentino Cherubini, Paola Pierleoni, Lorenzo Palma:
Towards Personalized AI-Based Diabetes Therapy: A Review. IEEE J. Biomed. Health Informatics 28(11): 6944-6957 (2024) - 2023
- [j17]Claudia Di Napoli, Giovanni Paragliola
Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers. Algorithms 16(8): 362 (2023) - [j16]Syed Ihtesham Hussain Shah
Projection based inverse reinforcement learning for the analysis of dynamic treatment regimes. Appl. Intell. 53(11): 14072-14084 (2023) - [j15]Syed Ihtesham Hussain Shah
An AI-empowered infrastructure for risk prevention during medical examination. Expert Syst. Appl. 225: 120048 (2023) - [j14]Giovanni Paragliola
A federated learning-based approach to recognize subjects at a high risk of hypertension in a non-stationary scenario. Inf. Sci. 622: 16-33 (2023) - [j13]Giovanni Paragliola
A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network. J. Sens. Actuator Networks 12(6): 78 (2023) - [c25]Patrizia Ribino, Claudia Di Napoli, Giovanni Paragliola, Luca Serino, Francesca Gasparini, Davide Chicco:
Exploratory analysis of longitudinal data of patients with dementia through unsupervised techniques. AIxAS@AI*IA 2023: 67-87 - [c24]Patrizia Ribino, Claudia Di Napoli, Giovanni Paragliola, Luca Serino:
Hyper-Parameter Optimization through Reinforcement Learning for Survival Prediction of Patients with Heart Failure. CENTERIS/ProjMAN/HCist 2023: 1754-1761 - [c23]Giovanni Paragliola, Claudia Di Napoli, Patrizia Ribino, Luca Serino:
Balancing Uneven Knowledge of Hospital Nodes for ICU Patients Diagnosis through Federated Learning. Ital-IA 2023: 206-211 - 2022
- [j12]Giovanni Paragliola
Definition of a novel federated learning approach to reduce communication costs. Expert Syst. Appl. 189: 116109 (2022) - [j11]Giovanni Paragliola
Evaluation of the trade-off between performance and communication costs in federated learning scenario. Future Gener. Comput. Syst. 136: 282-293 (2022) - [j10]Paolo Zampognaro
Definition of an FHIR-based multiprotocol IoT home gateway to support the dynamic plug of new devices within instrumented environments. J. Reliab. Intell. Environ. 8(4): 319-331 (2022) - [j9]Muddasar Naeem
Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning. Sensors 22(21): 8278 (2022) - [c22]Giovanni Paragliola:
Application of Federated Learning Approaches for Time-Series Classification in eHealth Domain. KES 2022: 3545-3552 - 2021
- [j8]Muddasar Naeem
A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment. Expert Syst. Appl. 168: 114285 (2021) - [j7]Giovanni Paragliola
An hybrid ECG-based deep network for the early identification of high-risk to major cardiovascular events for hypertension patients. J. Biomed. Informatics 113: 103648 (2021) - [c21]Antonio Coronato, Claudia Di Napoli, Giovanni Paragliola
Intelligent Planning of Onshore Touristic Itineraries for Cruise Passengers in a Smart City. Intelligent Environments 2021: 1-7 - [c20]Paolo Zampognaro
A FHIR based architecture of a multiprotocol IoT Home Gateway supporting dynamic plug of new devices within instrumented environments. ISCC 2021: 1-7 - 2020
- [j6]Antonio Coronato, Muddasar Naeem
Reinforcement learning for intelligent healthcare applications: A survey. Artif. Intell. Medicine 109: 101964 (2020) - [c19]Muddasar Naeem
A CNN based monitoring system to minimize medication errors during treatment process at home. APPIS 2020: 27:1-27:5 - [c18]Giovanni Paragliola
A Deep Learning-Based Approach for the Classification of Gait Dynamics in Subjects with a Neurodegenerative Disease. IntelliSys (3) 2020: 452-468 - [c17]Muddasar Naeem, Antonio Coronato
A Reinforcement Learning and IoT based System to Assist Patients with Disabilities. IoTBDS 2020: 491-498
2010 – 2019
- 2019
- [j5]Giovanni Paragliola
Risk management for nuclear medical department using reinforcement learning algorithms. J. Reliab. Intell. Environ. 5(2): 105-113 (2019) - [c16]Muddasar Naeem
Adaptive Treatment Assisting System for Patients Using Machine Learning. SNAMS 2019: 460-465 - 2018
- [j4]Giovanni Paragliola
Gait Anomaly Detection of Subjects With Parkinson's Disease Using a Deep Time Series-Based Approach. IEEE Access 6: 73280-73292 (2018) - [c15]Giovanni Paragliola, Antonio Coronato:
A Reinforcement-Learning-Based Approach for the Planning of Safety Strategies in AAL Applications. Intelligent Environments (Workshops) 2018: 498-505 - [c14]Giovanni Paragliola
A Reinforcement Learning-Based Approach for the Risk Management of e-Health Environments: A Case Study. SITIS 2018: 711-716 - [c13]Giovanni Paragliola
A Deep Gait Classification Approach for an Early Recognition of Huntington Diseases. SoMeT 2018: 981-991 - 2017
- [j3]Antonio Coronato, Giovanni Paragliola
A structured approach for the designing of safe AAL applications. Expert Syst. Appl. 85: 1-13 (2017) - [c12]Giovanni Paragliola, Antonio Coronato:
A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study. FedCSIS (Position Papers) 2017: 43-48 - [c11]Antonio Coronato, Giovanni Paragliola
Towards a Cognitive System for the Identification of Sleep Disorders. IIMSS 2017: 91-98 - 2016
- [c10]Alba Amato
Towards a coaching system for daily living activities: the use of kitchen objects and devices for cognitive impaired people. 3PGCIC 2016: 325-336 - [c9]Antonio Coronato, Giovanni Paragliola
An Approach for the Evaluation of Sleeping Behaviors Disorders in Patients with Cognitive Diseases: A Case Study. SITIS 2016: 545-550 - 2015
- [j2]Antonio Coronato, Giovanni Paragliola:
An anomolous situation detection system for cognitive impaired people. Comput. Syst. Sci. Eng. 30(1) (2015) - 2014
- [j1]Antonio Coronato
A situation-aware system for the detection of motion disorders of patients with Autism Spectrum Disorders. Expert Syst. Appl. 41(17): 7868-7877 (2014) - [c8]Antonio Coronato
Towards a Personal Health Records system for patients with Autism Spectrum Disorders. CICARE 2014: 90-97 - [c7]Mario Sicuranza
An Hybrid Architecture to Enhance Attacks Detection on IT infrastructure. IDC 2014: 437-442 - 2013
- [c6]Cesario Di Sarno, Valerio Formicola
Addressing Security Issues of Electronic Health Record Systems through Enhanced SIEM Technology. ARES 2013: 646-653 - [c5]Antonio Coronato
A Monitoring System enhanced by means of Situation-Awareness for Cognitive Impaired People. BODYNETS 2013: 124-127 - [c4]Mayutan Arumaithurai, Jan Seedorf, Giovanni Paragliola
Evaluation of ALTO-enhanced request routing for CDN interconnection. ICC 2013: 3519-3524 - [c3]Giovanni Paragliola
Intelligent Monitoring of Stereotyped Motion Disorders in Case of Children with Autism. Intelligent Environments 2013: 258-261 - [c2]Antonio Coronato
A Safe Kitchen for Cognitive Impaired People. UCAmI 2013: 17-25 - [c1]Antonio Coronato
An Intelligent Monitoring System for Cognitive Impaired People. UIC/ATC 2013: 232-237
Coauthor Index
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last updated on 2024-11-19 21:48 CET by the dblp team
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