Iuhasz Gabriel
Cited by
Cited by
Dice: Quality-driven development of data-intensive cloud applications
G Casale, D Ardagna, M Artac, F Barbier, E Di Nitto, A Henry, G Iuhasz, ...
2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering …, 2015
Neuroevolution based multi-agent system for micromanagement in real-time strategy games
I Gabriel, V Negru, D Zaharie
Proceedings of the fifth balkan conference in informatics, 32-39, 2012
An overview of monitoring tools for big data and cloud applications
G Iuhasz, I Dragan
Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2015 17th …, 2015
Neural network predictions of stock price fluctuations
G Iuhasz, M Tirea, V Negru
2012 14th International Symposium on Symbolic and Numeric Algorithms for …, 2012
Architecture of a scalable platform for monitoring multiple big data frameworks
G Iuhasz, D Pop, I Dragan
Scalable Computing: Practice and Experience 17 (4), 313-321, 2016
Support services for applications execution in multi-clouds environments
D Pop, G Iuhasz, C Craciun, S Panica
2016 IEEE international conference on autonomic computing (ICAC), 343-348, 2016
Tuning logstash garbage collection for high throughput in a monitoring platform
DN Doan, G Iuhasz
2016 18th International Symposium on Symbolic and Numeric Algorithms for …, 2016
M3at: Monitoring agents assignment model for data-intensive applications
V Kashansky, D Kimovski, R Prodan, P Agrawal, F Marozzo, G Iuhasz, ...
2020 28th Euromicro International Conference on Parallel, Distributed and …, 2020
Neuroevolution based multi-agent system with ontology based template creation for micromanagement in real-time strategy games
I Gabriel, V Negru, D Zaharie
Information Technology and Control 43 (1), 98-109, 2014
Anomaly detection for fault detection in wireless community networks using machine learning
L Cerdà-Alabern, G Iuhasz, G Gemmi
Computer Communications 202, 191-203, 2023
SERRANO: transparent application deployment in a secure, accelerated and cognitive cloud continuum
A Kretsis, P Kokkinos, P Soumplis, JJV Olmos, M Fehér, M Sipos, ...
2021 IEEE International Mediterranean Conference on Communications and …, 2021
Distributed platforms and cloud services: Enabling machine learning for big data
D Pop, G Iuhasz, D Petcu
Data Science and Big Data Computing: Frameworks and Methodologies, 139-159, 2016
A scalable platform for monitoring data intensive applications
I Drăgan, G Iuhasz, D Petcu
Journal of Grid Computing 17, 503-528, 2019
On processing extreme data
D Petcu, G Iuhasz, D Pop, D Talia, J Carretero, R Prodan, T Fahringer, ...
Scalable Computing. Practice and Experience 16 (4), 467-489, 2016
Applying self-* principles in heterogeneous cloud environments
I Drăgan, TF Fortiş, G Iuhasz, M Neagul, D Petcu
Cloud Computing: Principles, Systems and Applications, 255-274, 2017
Data mining considerations for knowledge acquisition in real time strategy games
G Iuhasz, VI Munteanu, V Negru
2013 IEEE 11th International Symposium on Intelligent Systems and …, 2013
Monitoring of exascale data processing
G Iuhasz, D Petcu
2019 IEEE International Conference on Advanced Scientific Computing (ICASC), 1-5, 2019
Overview of machine learning tools and libraries
D Pop, G Iuhasz
Inst. e-Austria Timisoara, 0
Dataset for anomaly detection in a production wireless mesh community network
L Cerdà-Alabern, G Iuhasz
Data in brief 49, 109342, 2023
TUFA: A TOSCA extension for the specification of accelerator-aware applications in the Cloud Continuum
A Spătaru, G Iuhasz, S Panica
2022 IEEE 46th Annual Computers, Software, and Applications Conference …, 2022
The system can't perform the operation now. Try again later.
Articles 1–20