Hamza AmraniUniversità degli Studi di Milano-Bicocca | UNIMIB · Department of Informatics, Systems and Communication (DISCo)
Hamza Amrani
Master of Computer Science
Artificial Intelligence, Machine Learning and Deep Learning, Signal Processing
About
9
Publications
1,812
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32
Citations
Introduction
Additional affiliations
November 2019 - May 2021
Position
- Research Associate
Description
- Supervisor: Prof. Daniela Micucci Development of a framework for long-term data collection to support automatic HAR, personalization of HAR models with Incremental Learning and Deep Learning approaches, and Unsupervised Learning in HAR with Clustering approaches based on Deep Learning. Thesis supervision: 3 BSc students and 1 MSc student.
Education
November 2018 - October 2021
Field of study
- Deep Learning, Machine Learning, Inertial Signal Processing, Representation Learning, Data Homogenization
September 2015 - October 2018
Field of study
- Images, Deep Learning, Feature Extraction, Information Retrieval
Publications
Publications (9)
Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, lead...
Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, lead...
Personalization is essential in enhancing the performance of machine learning models in brain-computer interfaces (BCIs) for emotion recognition, specifically in valence and arousal classification. In this work, we address the challenge of person-alizing BCI models utilizing a wireless consumer non-invasive electroencephalogram (EEG) device with dr...
One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labeled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can col...
Several techniques have been proposed to address the problem of recognizing activities of daily living from signals. Deep learning techniques applied to inertial signals have proven to be effective, achieving significant classification accuracy. Recently, research in human activity recognition (HAR) models has been almost totally model-centric. It...
Current sensor-based Human Activity Recognition (HAR) techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may do overtime to his or her way of carrying out activities. Moreover, the performances of subject-independent models are proportional to the size and the variability of the data...
Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the c...
