Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led […]
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Knowledge Graphs in the Era of Large Language Models (KGELL)
Knowledge Graphs (KGs) have gained attention due to their ability to represent structured and interlinked information. KGs represent knowledge in the form of relations between entities, referred to as facts, typically grounded in formal ontological models. Such machine-readable formats enable AI systems to make decisions using clear and verifiable data. Consequently, KGs have become essential […]
LIVE Quantum – Development of an Integrated AI Platform for Multichannel Personalized Management of User Requests
The LIVE Quantum project focuses on the research and development of an advanced, integrated AI-based platform for multichannel, personalized management of user requests, primarily targeting operators of critical infrastructure systems such as energy, water, gas, telecommunications, and utilities. The project combines industrial research and experimental development activities to create a scalable and modular solution that […]
Predicting Anomalous Trajectories Using Machine Learning
The proposed project focuses on applying machine learning to predict future trajectories, i.e., geographic position, using as input the changes in longitude and latitude, speed, and heading. Trajectories are classified based on inflection points present in the functions representing segments of the trajectories. Since the developed algorithms are intended for real-world data, preprocessing is planned. […]
Adaptive Algorithms for Integrating Compressive Sensing with Deep Learning
The project aims to improve image compressive sensing (CS) reconstruction by developing deep unfolding networks that combine the interpretability of iterative CS algorithms with the speed of deep learning. Instead of tying each network block to a single iteration, the proposed approach treats each module as an independent optimization problem with its own CS model […]
Deep Learning for Smart Energy Systems Management
As a key component of modern energy management systems, energy demand forecasting plays a crucial role in efficient resource allocation, grid stability, and the integration of renewables. Traditionally, classical techniques such as ARIMA and SARIMA have been used for energy demand prediction due to their simplicity and interpretability. However, these conventional methods often fail to […]
LIVE Quantum project kick-off meeting
The project kick-off meeting for LIVE Quantum – Development of an integrated AI platform for multichannel, personalized management of customer requests was held on 20 February 2026 in Zagreb. The project is valued at EUR 2,418,416.98 and is implemented under the IRI S3 call – Increasing the development of new products and services arising from […]
Deep Unfolding ADMM Network for CS Image Reconstruction with Long-Short Term Residuals
Deep learning has demonstrated exceptional learning capabilities, leading to the development various deep unfolding networks for image reconstruction. However, current deep unfolding networks often replace certain steps of traditional optimization algorithms with neural networks, thereby compromising the interpretability of the optimization algorithms. Additionally, each iteration in the unfolding process may result in certain image information […]
XDT-FMARL: An Explainable Federated Multi-Agent Reinforcement Learning Framework for Energy-Efficient IoT Task Offloading
Effectively managing the vast data generated by sensor networks has become crucial with the rapid spread of IoT devices under strict resource constraints. This research introduces a framework that integrates explainable artificial intelligence (XAI), digital twins (DT), federated learning (FL), and multi-agent reinforcement learning (MARL) to optimise energy use and task distribution in distributed IoT […]
Proactive Context Aware Task Offloading in Digital Twin Driven Federated IoT Systems with Large Language Models
This study considers the combination of Digital Twins (DT), Federated Learning (FL), and computation offloading to establish a context-aware framework for effective resource management in IoT networks. Although DT models can predict battery levels, CPU usage, and network delays to aid reinforcement learning (RL) agents, earlier RL-based controllers require significant training and are slow to […]
Pretraining and evaluation of BERT models for climate research
Motivated by the pressing issue of climate change and the growing volume of data, we pretrain three new language models using climate change research papers published in top-tier journals. Adaptation of existing domain-specific models based on Bidirectional Encoder Representations from Transformers (BERT) architecture is utilized for CliSciBERT (domain adaptation of SciBERT) and SciClimateBERT (domain adaptation […]
