Ilias SiniosoglouUniversity of Western Macedonia | UOWM · Department of Electrical and Computer Engineering
Ilias Siniosoglou
Master of Engineering
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27
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Introduction
Publications
Publications (27)
The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are particularly pronounced in the federated domain, where optimizing models for individual nodes poses significant diffi...
Current methods for low- and few-shot object detection have primarily focused on enhancing model performance for detecting objects. One common approach to achieve this is by combining model finetuning with data augmentation strategies. However, little attention has been given to the energy efficiency of these approaches in data-scarce regimes. This...
In the evolving field of medical imaging and machine learning (ML), this paper introduces a novel framework for evaluating synthetic pulmonary imaging aiming to assess synthetic data quality and applicability. Our study concentrates on synthetic X-ray chest images, crucial for diagnosing respiratory diseases. We employ SPINE (Synthetic Pulmonary Im...
Data exchange through mobile devices is rapidly increasing due to the high information demands of today's applications. The need for monitoring the exchanged traffic becomes important in order to control and optimize the device and network performance and security. Taking this under consideration, in this paper, we developed a cloud-based system fo...
The increased availability of medical data has significantly impacted healthcare by enabling the application of machine / deep learning approaches in various instances. However, medical datasets are usually small and scattered across multiple providers, suffer from high class-imbalance, and are subject to stringent data privacy constraints. In this...
In the ever-evolving era of Artificial Intelligence (AI), model performance has constituted a key metric driving innovation, leading to an exponential growth in model size and complexity. However, sustainability and energy efficiency have been critical requirements during deployment in contemporary industrial settings, necessitating the use of data...
Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the pref...
The time-consuming nature of training and deploying complicated Machine and Deep Learning (DL) models for a variety of applications continues to pose significant challenges in the field of Machine Learning (ML). These challenges are heightened in the federated domain, where optimizing models for individual nodes is particularly difficult. Many meth...
Federated learning (FL) is a novel methodology aiming at training machine learning (ML) and deep learning (DL) models in a decentralized manner in order to solve three main problems seen in the artificial intelligence (AI) sector, namely, (a) model optimization, (b) data security and privacy, and (c) resource optimization. FL has been established a...
The electrical grid has significantly evolved over the years, thus creating a smart paradigm, which is well known as the smart electrical grid. However, this evolution creates critical cybersecurity risks due to the vulnerable nature of the industrial systems and the involvement of new technologies. Therefore, in this paper, the ELECTRON architectu...
Modern Healthcare cyberphysical systems have begun to rely more and more on distributed AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of medical fields, while at the same time fortifying the privacy of the sensitive information that are present in th...
The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can
also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environmen...
In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical CyberPhysical Systems (MCPS) Networks that entails the cr...
Supervisory Control and Data Acquisition (SCADA) systems play a significant role in Critical Infrastructures (CIs) since they monitor and control the automation processes of the industrial equipment. However, SCADA relies on vulnerable communication protocols without any cybersecurity mechanism, thereby making it possible to endanger the overall op...
The Industrial Control Systems (ICS) are the underlying monitoring and control components of critical infrastruc-tures, which consist of a number of distributed field devices, such as Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs) and Human Machine Interfaces (HMIs). As modern ICS are connected to the Internet, in the context o...