Machine learning-based predictions of directionality and charge of cosmic muons—a simulation study using the mICAL detector

2019 ◽  
Vol 14 (11) ◽  
pp. P11020-P11020
Author(s):  
D. Samuel ◽  
A. Samalan ◽  
M. Omana Kuttan ◽  
L.P. Murgod
Keyword(s):  
Machine Learning ◽  
Simulation Study ◽  
Cosmic Muons

scholarly journals An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1015 ◽  
Author(s):  
Carles Bretó ◽  
Priscila Espinosa ◽  
Penélope Hernández ◽  
Jose M. Pavía
Keyword(s):  
Machine Learning ◽  
Gross Domestic Product ◽  
Maximum Entropy ◽  
Simulation Study ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Machine Learning Approach ◽  
Maximum Entropy Inference

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

Estimation of Pregnant Women Possibly Left Behind in Tsunami Predicted Areas of Nagoya City: A Simulation for Rescue Planning

2020 ◽  
pp. 1-8
Author(s):  
Ritsuko Hattori ◽  
Shoko Miyagawa ◽  
Kanetoshi Hattori
Keyword(s):  
Machine Learning ◽  
Pregnant Women ◽  
Simulation Study ◽  
Nankai Trough ◽  
Learning Model ◽  
Center Line ◽  
Left Behind ◽  
Machine Learning Model ◽  
The City

ABSTRACT Objective: In case of an outbreak of Nankai Trough Mega-earthquake, it is predicted that a tsunami would invade Nagoya City within 100 minutes, hitting about one third of the City of Nagoya. If the administrative plan of the city and midwives’ expertise are coordinated, pregnant women’s chances of survival will increase. The authors carried out this simulation study in an attempt to improve consistency of the two efforts. Method: We estimated the number of pregnant women using a machine learning model. The evacuation distance of pregnant women was estimated on the basis of the data of road center line. Results: Through this simulation study, it became clear that preparation for approximately 2600 pregnant women escaping from tsunami predicted area and for about 1200 pregnant women possibly left in the area is needed. Conclusions: Our study suggests that triage point planning is needed in areas where pregnant women are evacuated. The triage makes it possible to transport women to appropriate hospitals.

scholarly journals Investigating inefficiencies of bookmaker odds in football using machine learning

2020 ◽  
Author(s):  
Benedikt Mangold ◽  
Johannes Stübinger
Keyword(s):  
Machine Learning ◽  
Simulation Study ◽  
Efficient Market Hypothesis ◽  
Learning Models ◽  
Efficient Market ◽  
Simple Machine ◽  
Football Betting ◽  
Available Information ◽  
Over Time ◽  
Machine Learning Models

The efficient-market hypothesis states that it is impossible to beat the market, as the price reflects all available information. Applied to bookmaker odds for football games, there should not be a systematic way of winning money on the long run.However, we show that by using simple machine learning models we can systematically outperform the markets belief manifested through the bookmakers odds. The effect of this inefficiency is diminishing over time, which indicates that the knowledge that has been derived from and the pure amount of the data is also reflected in the odds in recent times.We give some insights how this effect differs across major football leagues in Europe, which algorithms are performing best and statistics on the ROI using machine learning in football betting. Additionally, we share how the simulation study has been designed in more detail.

scholarly journals Compton PET: a simulation study for a PET module with novel geometry and machine learning for position decoding

2018 ◽  
Vol 5 (1) ◽  
pp. 015018 ◽  
Author(s):  
Peng Peng ◽  
Martin S Judenhofer ◽  
Adam Q Jones ◽  
Simon R Cherry
Keyword(s):  
Machine Learning ◽  
Simulation Study

scholarly journals hcp → ω phase transition mechanisms in shocked zirconium: A machine learning based atomic simulation study

2019 ◽  
Vol 162 ◽  
pp. 126-135 ◽  
Author(s):  
Hongxiang Zong ◽  
Yufei Luo ◽  
Xiangdong Ding ◽  
Turab Lookman ◽  
Graeme J. Ackland
Keyword(s):  
Machine Learning ◽  
Phase Transition ◽  
Simulation Study ◽  
Atomic Simulation ◽  
Ω Phase
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