All Outputs (54)

ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare (2025)
Journal Article

Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover,... Read More about ARUC-GAN: A CycleGAN-based attention residual U-Net for low-dose CT denoising in smart healthcare.

Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency (2025)
Journal Article

This survey paper provides an in-depth exploration of Federated Learning (FL) in Internet of Things (IoT) environments , focusing on privacy-preserving techniques and their influence on model performance and network efficiency. It highlights key chal... Read More about Federated learning in IoT environments: Examining the three-way see-saw for privacy, model-performance, and network-efficiency.

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 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?.

A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting (2023)
Journal Article

Significant efforts have been made for vehicle-to-vehicle communications that now enable the Internet of Vehicles (IoV). However, current IoV solutions are unable to capture traffic data both accurately and securely. Another drawback of current IoV m... Read More about A Secure Intelligent System for Internet of Vehicles: Case study on traffic forecasting.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads (2022)
Journal Article

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. T... Read More about Vehicle detection using improved region convolution neural network for accident prevention in smart roads.

Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

Intelligent deep fusion network for anomaly identification in maritime transportation systems (2022)
Journal Article

This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime dat... Read More about Intelligent deep fusion network for anomaly identification in maritime transportation systems.

Emergent deep learning for anomaly detection in internet of everything (2021)
Journal Article

This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environment... Read More about Emergent deep learning for anomaly detection in internet of everything.

On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications (2021)
Journal Article

Prediction of sensor readings in event-based Internet-of-Things (IoT) applications is considered. A new approach is proposed, which allows turning off sensors in periods when their readings can be predicted, thus preserving energy that would be consu... Read More about On predicting sensor readings with sequence modeling and reinforcement learning for energy-efficient IoT applications.

Towards energy efficient clustering in wireless sensor networks: A comprehensive review (2021)
Journal Article

Clustering is one of the fundamental approaches used to optimize energy consumption in wireless sensor networks. Clustering protocols proposed in the literature can be classified according to different criteria related to their features such as the c... Read More about Towards energy efficient clustering in wireless sensor networks: A comprehensive review.

Towards optimized one-step clustering approach in wireless sensor networks (2021)
Journal Article

This paper introduces a nonlinear integer programming model for the clustering problem in wireless sensor networks, with a threefold contribution. First, all factors that may influence the energy consumption of clustering protocols, such as cluster-h... Read More about Towards optimized one-step clustering approach in wireless sensor networks.

Trajectory outlier detection: New problems and solutions for smart cities (2021)
Journal Article

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for th... Read More about Trajectory outlier detection: New problems and solutions for smart cities.

Deep learning vs. traditional solutions for group trajectory outliers (2020)
Journal Article

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, whi... Read More about Deep learning vs. traditional solutions for group trajectory outliers.

When the decomposition meets the constraint satisfaction problem (2020)
Journal Article

This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems (PD-CSP). The main idea is th... Read More about When the decomposition meets the constraint satisfaction problem.

A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories (2020)
Journal Article

This paper addresses the taxi fraud problem and introduces a new solution to identify trajectory outliers. The approach as presented allows to identify both individual and group outliers and is based on a two phase-based algorithm. The first phase de... Read More about A two-phase anomaly detection model for secure intelligent transportation ride-hailing trajectories.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection (2020)
Journal Article

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories... Read More about Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection.

A recurrent neural network for urban long-term traffic flow forecasting (2020)
Journal Article

This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, na... Read More about A recurrent neural network for urban long-term traffic flow forecasting.