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Centre for Artificial Intelligence and Cybersecurity – AIRI

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Machine Learning Laboratory

Machine Learning Laboratory deals with numerous challenges in several fields, by developing and integrating suitable data-driven machine learning solutions to perceived problems.

Head of Laboratory

Ivan Štajduhar, Assoc. Prof., PhD (RITEH)

Laboratory Projects

Machine Learning for Knowledge Transfer in Medical Radiology

Machine Learning for Knowledge Transfer in Medical Radiology

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

National Competence Centres in the Framework of EuroHPC (EUROCC)

Hyperspectral Image Analysis Using Machine Learning and Adaptive Data-Driven Filtering

Development of machine-learning-based techniques for illness and injury detection in medical images

Computer-Aided Digital Analysis And Classification of Signals

Computer-Aided Digital Analysis And Classification of Signals

A Network for Gravitational Waves, Geophysics and Machine Learning

Image Processing, Information Engineering & Interdisciplinary Knowledge Exchange

Laboratory Research Papers

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

Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network

Automatic Annotation of Narrative Radiology Reports

Adaptive Filtering and Analysis of EEG Signals in the Time-Frequency Domain Based on the Local Entropy

Automatic Music Transcription for Traditional Woodwind Instruments Sopele

Automatic Music Transcription for Traditional Woodwind Instruments Sopele

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

Adaptive State Estimator With Intersection of Confidence Intervals Based Preprocessing

Mirroring Quasi-Symmetric Organ Observations for Reducing Problem Complexity

Semi-automated detection of anterior cruciate ligament injury from MRI

Uncensoring censored data for machine learning: A likelihood-based approach

Learning Bayesian networks from survival data using weighting censored instances

Impact of censoring on learning Bayesian networks in survival modelling

Affiliated Researchers

  • Jonatan Lerga, Assist. Prof., PhD (RITEH)
  • Dejan Ljubobratović
  • Franko Hržić, mag. ing. comp. (RITEH)
  • Ivan Štajduhar, Assoc. Prof., PhD (RITEH)
  • Marko Gulić, Assist. Prof., PhD (PFRI)
  • Teo Manojlović, mag. ing. comp.

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Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

University of Rijeka

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