activity
20142019
most citedA Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data

103 citations · 143 across the 12 of their papers we have counts for

collaborators

12 papers

cs.LG20199 cited

Topological Data Analysis of Time Series Data for B2B Customer Relationship Management

Rodrigo Rivera-Castro, Polina Pilyugina, Alexander Pletnev +3

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited to the…

stat.AP20191 cited

Usage of multiple RTL features for Earthquake prediction

P. Proskura, A. Zaytsev, I. Braslavsky +2

We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given…

eess.IV2019

Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction

Marina Pominova, Anna Kuzina, Ekaterina Kondrateva +4

In this work, we aim at predicting children's fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child he…

physics.data-an2019103 cited

A Predictive Model for Steady-State Multiphase Pipe Flow: Machine Learning on Lab Data

Evgenii Kanin, Andrei Osiptsov, Albert Vainshtein +1

Engineering simulators used for steady-state multiphase pipe flows are commonly utilized to predict pressure drop. Such simulators are typically based on either empirical correlati…

cs.LG2019

Demand forecasting techniques for build-to-order lean manufacturing supply chains

Rodrigo Rivera-Castro, Ivan Nazarov, Yuke Xiang +3

Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirement…

cs.LG20194 cited

Learning Ensembles of Anomaly Detectors on Synthetic Data

D. Smolyakov, N. Sviridenko, V. Ishimtsev +2

The main aim of this work is to develop and implement an automatic anomaly detection algorithm for meteorological time-series. To achieve this goal we develop an approach to constr…