Results 91 to 100 of about 15,501 (203)

Multilinear Weighted Regression (MWE) with Neural Networks for trend prediction

Applied Soft Computing, 2019
The ability to define accurate linear models to find patterns or relationships between variables is one of the most challenging fields in Computer Science. In particular, extrapolative applications are widely used to predict values in Biological, Behavioral and Social Sciences.
Arteta Albert, Alberto, Mingo López, Luis Fernando de, Gómez Blas, Nuria   +2 more
openaire   +2 more sources

Estimation of the GCV of Coal Using Real‐Time Plant Data

Energy Science &Engineering, Volume 14, Issue 2, Page 905-915, February 2026.
ABSTRACT Online or real‐time strategies of estimating the gross calorific value (GCV) of coal are still not fully explored in academic literature, even though both conventional and sophisticated offline methods for estimating the GCV are well described.
Lethukuthula Nokwazi Vilakazi, Daniel Madyira   +1 more
wiley   +1 more source

Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy [PDF]

, 2019
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms.
Avestimehr, Salman, Kalan, Seyed Mohammadreza Mousavi, Li, Songze, Raviv, Netanel, Soltanolkotabi, Mahdi, Yu, Qian   +5 more
core   +1 more source

Driving Factors Behind Airbnb Pricing - A Multilinear Regression Analysis

, 2023
With a high increase of users in the world's ever expanding sharing economy, Airbnb has become a customary solution in short term rentals of accommodations. In this market, it is the host's job to choose a pricing which sufficiently corresponds to what tenants are willing to pay.
Flöjs, Johan, Herrgård, Jonathan
openaire   +1 more source

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

Energies
This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods.
Quota Alief Sias, Rahma Gantassi, Yonghoon Choi, Jeong Hwan Bae   +3 more
doaj   +1 more source

Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems [PDF]

, 2014
In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e.,
Cichocki, Andrzej
core  

Urban Traumatic Moving Injuries Before and During SARS-CoV-2: A Multilinear Regression Analysis. [PDF]

Cureus, 2023
Jung G, Giordano V, Harrington T, Jung L.   +3 more
europepmc   +1 more source

Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models. [PDF]

Pharmaceuticals (Basel), 2023
Umar H, Rizaner N, Usman AG, Aliyu MR, Adun H, Ghali UM, Uzun Ozsahin D, Abba SI.   +7 more
europepmc   +1 more source

Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods [PDF]

, 2015
Training neural networks is a challenging non-convex optimization problem, and backpropagation or gradient descent can get stuck in spurious local optima.
Anandkumar, Anima, Janzamin, Majid, Sedghi, Hanie   +2 more
core   +3 more sources

Training Input-Output Recurrent Neural Networks through Spectral Methods [PDF]

, 2016
We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters.
Anandkumar, Anima, Sedghi, Hanie
core   +2 more sources