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Center for Artificial Intelligence and Cybersecurity – AIRI

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Machine Learning Laboratory

The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data

18.03.2022

Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned […]

Intra-domain and cross-domain transfer learning for time series data—How transferable are the features?

12.01.2022

In practice, it is very challenging and sometimes impossible to collect datasets of labelled data large enough to successfully train a machine learning model, and one possible solution to this problem is using transfer learning. In this study, we investigate how transferable are features between different domains of time series data and under what conditions. […]

Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data

11.12.2021

In a recent study we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. […]

Cast suppression in radiographs by generative adversarial networks

19.10.2021

Injured extremities commonly need to be immobilized by casts to allow proper healing. We propose a method to suppress cast superimpositions in pediatric wrist radiographs based on the cycle generative adversarial network (CycleGAN) model. We retrospectively reviewed unpaired pediatric wrist radiographs (n = 9672) and sampled them into 2 equal groups, with and without cast. […]

Deep Semi-Supervised Algorithm for Learning Cluster-Oriented Representations of Medical Images Using Partially Observable DICOM Tags and Images

19.10.2021

The task of automatically extracting large homogeneous datasets of medical images based on detailed criteria and/or semantic similarity can be challenging because the acquisition and storage of medical images in clinical practice is not fully standardised and can be prone to errors, which are often made unintentionally by medical professionals during manual input. In this […]

XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis

16.03.2021

Background and objectives: Computer-aided diagnosis relies on machine learning algorithms that require filtered and preprocessed data as the input. Aligning the image in the desired direction is an additional manual step in post- processing, commonly overlooked due to workload issues. Several state-of-the-art approaches for fracture detection and disease-struck region segmentation benefit from correctly oriented images, […]

Machine Learning for Knowledge Transfer in Medical Radiology

26.12.2020

Machine Learning for Knowledge Transfer in Medical Radiology

Medical radiology is often used in clinical analysis to establish a medical diagnosis in a non-invasive manner. By considering the morphological properties of the observed area, clinicians can determine the presence of an injury or a disease without the need for invasive surgery. The purpose of computer-aided diagnosis (CAD) is to help physicians with interpreting […]

ICAIH 2020 conference presentation

08.12.2020

Ivan Štajduhar gave a talk titled “The reach and limitations of machine intelligence” at the Third International Conference on Artificial Intelligence Humanities (ICAIH 2020), co-organised by the Chung-Ang University, Seoul, South Korea and the University of Rijeka.

Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification

14.07.2020

Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy […]

European Network for assuring food integrity using non-destructive spectral sensors

17.06.2020

There is an increasing need for the food industry to provide information on their products in order to satisfy quality standards and to protect their products from food fraud. Recent developments in technology, and advances in big data analytics, provide the opportunity for step-changes that can transform the role of food integrity assurance from one […]

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Latest Projects

ABsistemDCiCloud

Machine Learning for Knowledge Transfer in Medical Radiology

Estimating River Discharges in Highly Stratified Estuaries

Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis (InfoCoV)

European Network for assuring food integrity using non-destructive spectral sensors

Latest Research Papers

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Entropy-Based Concentration and Instantaneous Frequency of TFDs from Cohen’s, Affine, and Reassigned Classes

Coupled encoding methods for antimicrobial peptide prediction: How sensitive is a highly accurate model?

The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data

Improved Parametrized Multiple Window Spectrogram with Application in Ship Navigation Systems

Latest News

Assoc. prof. Jonatan Lerga received the Croatian Academy of Sciences and Arts award

Dr. Sc. Nikola Lopac successfully defended his doctoral dissertation

Presentation at the conference “Digital Innovation and Technology for People”

Assoc. prof. dr. sc. Jonatan Lerga presented AIRI Center at the IEEE Rijeka : Computer Society Congress 2021

Prof. dr. sc. Ana Mestrovic participated at the Panel on perspectives and real-life applications of AI organized by IEEE Technology and Engineering Management Society

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Center for Artificial Intelligence and Cybersecurity
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