Outputs (11)

Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers (2024)
Journal Article

This research aims to find an optimal balance between privacy and performance in forecasting mental health sentiment. This paper investigates federated learning (FL) augmented with a novel data obfuscation (DO) technique, where synthetic data is used... Read More about Privacy-enhanced sentiment analysis in mental health: Federated learning with data obfuscation and bidirectional encoder representations from transformers.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database (2024)
Presentation / Conference Contribution

Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and... Read More about A novel hybrid framework for realistic UAV detection using a mixed RF signal database.

A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT (2024)
Presentation / Conference Contribution

Despite its distributed nature and being privacy-preserving by nature, Federated Learning (FL) is vulnerable to poisoning attacks in which malicious actors can inject fake model parameters or false data to compromise the learning process. This articl... Read More about A lightweight integrity-driven federated learning approach to mitigate poisoning attacks in IoT.

A novel hybrid framework for realistic UAV detection using a mixed RF signal database (2024)
Presentation / Conference Contribution

Advances in Unmanned Aerial Vehicles (UAVs) empower a plethora of applications but also raise significant security and privacy challenges. Effective UAVs detection systems are crucial for mitigating these risks. This paper deals with this problem and... Read More about A novel hybrid framework for realistic UAV detection using a mixed RF signal database.

A survey on emerging blockchain technology platforms for securing the Internet of Things (2024)
Journal Article

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressin... Read More about A survey on emerging blockchain technology platforms for securing the Internet of Things.

TG-SPRED: Temporal graph for sensorial data PREDiction (2024)
Journal Article

This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network's operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readi... Read More about TG-SPRED: Temporal graph for sensorial data PREDiction.

Social web in IoT: Can evolutionary computation and clustering improve ontology matching for social web of things? (2023)
Journal Article

Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we sug... Read More about Social web in IoT: Can evolutionary computation and clustering improve ontology matching for social web of things?.

DPFTT: Distributed particle filter for target tracking in the Internet of Things (2023)
Presentation / Conference Contribution

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (... Read More about DPFTT: Distributed particle filter for target tracking in the Internet of Things.

Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES (2023)
Presentation / Conference Contribution

This work targets a comprehensive model enabling energy-constrained IoT (Internet of Things) sensor devices to be inactive for extended periods while estimating their readings of real-time events. Although events seem semantically uncoupled, they are... Read More about Generating event sensor readings using spatial correlations and a graph sensor adversarial model for energy saving in IoT: GSAVES.

Knowledge guided deep learning for general-purpose computer vision applications (2023)
Presentation / Conference Contribution

This research targets general-purpose smart computer vision that eliminates reliance on domain-specific knowledge to reach adaptable generic models for flexible applications. It proposes a novel approach in which several deep learning models are trai... Read More about Knowledge guided deep learning for general-purpose computer vision applications.