English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This article presents two systems that can simulate and predict Particlesratios created in high energy proton-proton (pp) collisions as a function oftransverse momentum and the center-of-mass energy. An adaptive neurofuzzyinference system (ANFIS) and an artificial neural network (ANN) system are thesystems in discussion. The ANFIS and ANN simulation results for trainingparticles ratios as evaluated with training data points revealed an excellentmatch to the experimental data. The ANFIS and ANN's prediction abilities werealso tested using data points that were not included in training and theyperformed well. The results clearly show that these methods are capable ofextracting collision information and that they are helpful. Also, ANFIS and ANNresults were compared with additional theoretical results (PYTHIA (CR Mode),HERWIG7, PYTHIA, PYTHIA8 (Monash), and EPOS-LHC). It is found that the ANFISshows better performance and is trained more quickly than the ANN system andother theoretical models.

Comparison between Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System For The Baryon-to-Meson Ratios in Proton-Proton Collisions