Matjaz Gams
Cited by
Cited by
Monitoring stress with a wrist device using context
M Gjoreski, M Luštrek, M Gams, H Gjoreski
Journal of biomedical informatics 73, 159-170, 2017
Accelerometer placement for posture recognition and fall detection
H Gjoreski, M Lustrek, M Gams
2011 Seventh International Conference on Intelligent Environments, 47-54, 2011
Continuous stress detection using a wrist device: in laboratory and real life
M Gjoreski, H Gjoreski, M Luštrek, M Gams
proceedings of the 2016 ACM international joint conference on pervasive and …, 2016
An agent-based approach to care in independent living
B Kaluža, V Mirchevska, E Dovgan, M Luštrek, M Gams
Ambient Intelligence: First International Joint Conference, AmI 2010, Malaga …, 2010
Non-invasive blood pressure estimation from ECG using machine learning techniques
M Simjanoska, M Gjoreski, M Gams, A Madevska Bogdanova
Sensors 18 (4), 1160, 2018
Automatic recognition of gait-related health problems in the elderly using machine learning
B Pogorelc, Z Bosnić, M Gams
Multimedia tools and applications 58, 333-354, 2012
How accurately can your wrist device recognize daily activities and detect falls?
M Gjoreski, H Gjoreski, M Luštrek, M Gams
Sensors 16 (6), 800, 2016
Artificial intelligence and ambient intelligence
M Gams, IYH Gu, A Härmä, A Muñoz, V Tam
Journal of Ambient Intelligence and Smart Environments 11 (1), 71-86, 2019
Transforming arbitrary tables into logical form with TARTAR
A Pivk, P Cimiano, Y Sure, M Gams, V Rajkovič, R Studer
Data & Knowledge Engineering 60 (3), 567-595, 2007
Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds
M Gjoreski, A Gradišek, B Budna, M Gams, G Poglajen
Ieee Access 8, 20313-20324, 2020
Automatic detection of perceived stress in campus students using smartphones
M Gjoreski, H Gjoreski, M Lutrek, M Gams
2015 international conference on intelligent environments, 132-135, 2015
New measurements highlight the importance of redundant knowledge
M Gams
Proceedings of the fourth european working session on learning, 71-79, 1989
Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors
M Gjoreski, V Janko, G Slapničar, M Mlakar, N Reščič, J Bizjak, V Drobnič, ...
Information Fusion 62, 47-62, 2020
Learning in distributed systems and multi-agent environments
P Brazdil, M Gams, S Sian, L Torgo, W Van de Velde
Machine Learning—EWSL-91: European Working Session on Learning Porto …, 1991
Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer
H Gjoreski, J Bizjak, M Gjoreski, M Gams
Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial …, 2016
What makes classification trees comprehensible?
M Luštrek, M Gams, S Martinčić-Ipšić
Expert Systems with Applications 62, 333-346, 2016
Efficient activity recognition and fall detection using accelerometers
S Kozina, H Gjoreski, M Gams, M Luštrek
Evaluating AAL Systems Through Competitive Benchmarking: International …, 2013
Datasets for cognitive load inference using wearable sensors and psychological traits
M Gjoreski, T Kolenik, T Knez, M Luštrek, M Gams, H Gjoreski, V Pejović
Applied Sciences 10 (11), 3843, 2020
Detecting falls with location sensors and accelerometers
M Luštrek, H Gjoreski, S Kozina, B Cvetkovic, V Mirchevska, M Gams
Proceedings of the AAAI Conference on Artificial Intelligence 25 (2), 1662-1667, 2011
Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals
M Gjoreski, MŽ Gams, M Luštrek, P Genc, JU Garbas, T Hassan
IEEE access 8, 70590-70603, 2020
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