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- Istituto Nazionale di Fisica Nucleare, Sezione di BariIT135 papers
- University of DelhiIN134 papers
- Florida Institute of TechnologyUS133 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PaviaIT133 papers
- Panjab UniversityIN133 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di BolognaIT132 papers
- Lappeenranta-Lahti University of TechnologyFI132 papers
- University of SeoulKR132 papers
- Ibero American UniversityMX131 papers
- Universidade do Estado do Rio de JaneiroBR131 papers
- University of RuhunaLK131 papers
- Vilnius UniversityLT131 papers
6 papers · 1 filter
An approach of statistical corrections to interactions in hadron resonance gas
Mahmoud Hanafy, Muhammad Maher
We propose a new model for hadrons with quantum mechanical attractive and repulsive interactions sensitive to some spatial correlation length parameter inspired by Beth-Uhlenbeck q…
Efficient Arabic emotion recognition using deep neural networks
Ahmed Ali, Yasser Hifny
Emotion recognition from speech signal based on deep learning is an active research area. Convolutional neural networks (CNNs) may be the dominant method in this area. In this pape…
MUSiC: a model-unspecific search for new physics in proton-proton collisions at 13 TeV
CMS Collaboration
Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated l…
Interstrip Capacitances of the Readout Board used in Large Triple-GEM Detectors for the CMS Muon Upgrade
M. Abbas, M. Abbrescia, H. Abdalla +178
We present analytical calculations, Finite Element Analysis modeling, and physical measurements of the interstrip capacitances for different potential strip geometries and dimensio…
Benchmarking Meta-heuristic Optimization
Mona Nasr, Omar Farouk, Ahmed Mohamedeen +3
Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms a…
Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm
Yunpeng Wang, A. W. Kandeal, Ahmed Swidan +5
Presented is a new generation prediction model of a tubular solar still (TSS) productivity utilizing two machine learning (ML) techniques, namely:Random forest (RF) and Artificial…