Optimized Fuzzy Backpropagation Neural Network using Genetic Algorithm for Predicting Indonesian Stock Exchange Composite Index

Investment activities in the capital market have the possibility to generate profits and at the same time also cause losses. The composite stock price index as an indicator used to determine investment continues to change over time. Uncertainty of stock exchange composite index requires investors to be able to make predictions so as to produce maximum profits. The aim of this study is to forecast the composite stock price index. The input variables used are Indonesia interest rates, rupiah exchange rates, Dow Jones index, and world gold prices. All data obtained in the period from January 2008 to March 2019. Data are used to build the Fuzzy Backpropagation Neural Network (FBPNN), model. The weight of FBPNN model was optimized using Genetic Algorithm then used to forecast the composite stock price index. The forecasting result of the composite stock price index for April to June 2019 respectively were 5822.6, 5826.8, and 5767.3 with the MAPE value of 8.42%. These results indicate that Indonesia interest rates, rupiah exchange rate, Dow Jones index, and the gold price are the proper indicators to predict the composite stock price index

Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning

In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is $pp \to \ell N \to 3 \ell + E_T^{\rm miss}$, while the dominant background is $p p \to W Z \to 3 \ell + E_T^{\rm miss}$. We use either the Multi-Layer Perceptron or the Boosted Decision Tree with Gradient Boosting to analyse the kinematic observables and optimize the discrimination of background and signal events. It is found that the reconstructed $Z$ boson mass and heavy neutrino mass from the charged leptons and missing transverse energy play crucial roles in separating the signal from backgrounds. The prospects of heavy-light neutrino mixing $|V_{\ell N}|^2$ (with $\ell = e,\,\mu$) are estimated by using machine learning at the hadron colliders with $\sqrt{s}=14$ TeV, 27 TeV, and 100 TeV, and it is found that $|V_{\ell N}|^2$ can be improved up to ${\cal O} (10^{-6})$ for heavy neutrino mass $m_N = 100$ GeV and ${\cal O} (10^{-4})$ for $m_N = 1$ TeV.Comment: 33 pages, 14 figures, 4 tables, more details and more references added, version published in JHE