Results 51 to 60 of about 44,498 (172)

Interactive analysis of high-dimensional association structures with graphical models [PDF]

, 1998
Graphical chain models are a capable tool for analyzing multivariate data. However, their practical use may still be cumbersome in some respect since fitting the model requires the application of an intensive selection strategy based on the calculation ...
Blauth, Angelika, Bry, François, Pigeot, Iris   +2 more
core   +1 more source

Machine‐Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles

Advanced Science, EarlyView.
Using machine learning on a mega‐scale global dataset (n = 1,336,840) reveals a robust personality trait architecture beyond the Big Five. A Big Two model, broadly capturing social engagement and internal mentation, defines a geometric space that links personality to neurocognitive profiles.
Kaixiang Zhuang, Ji Chen, Jinfeng Han, Wei Cheng, Jiang Qiu, Jianfeng Feng, Simon Eickhoff, Deniz Vatansever   +7 more
wiley   +1 more source

A Novel Algorithm for the Decomposition of Non-Stationary Multidimensional and Multivariate Signals

Computation
The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more.
Roberto Cavassi, Antonio Cicone, Enza Pellegrino, Haomin Zhou   +3 more
doaj   +1 more source

Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition

Ain Shams Engineering Journal, 2018
This study proposes a novel method for estimation of reference evapotranspiration (ETo) by accounting the time scale of variability using the Multivariate Empirical Mode Decomposition (MEMD).
S. Adarsh, Sulaiman Sanah, K.K. Murshida, P. Nooramol   +3 more
doaj   +1 more source

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain

Advanced Science, EarlyView.
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia   +3 more
wiley   +1 more source

Data-driven time-frequency analysis of multivariate data

, 2011
Empirical Mode Decomposition (EMD) is a data-driven method for the decomposition and time-frequency analysis of real world nonstationary signals.
Rehman, Naveed Ur, Rehman, Naveed Ur
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

Discrete Dynamics in Nature and Society, 2012
This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA)
Md. Rabiul Islam, Md. Rashed-Al-Mahfuz, Shamim Ahmad, Md. Khademul Islam Molla   +3 more
doaj   +1 more source

Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes

Entropy, 2012
Falls are unpredictable accidents and resulting injuries can be serious to the elderly. A preventative solution can be the use of vibration stimulus of white noise to improve the sense of balance.
Jiann-Shing Shieh, Chuan Wu, Bernard C. Jiang, Ku-Ping Chen, Maysam F. Abbod, Quan Liu, Qin Wei, Kai-Hong Wang, Dong-Hai Liu   +8 more
doaj   +1 more source

Traffic speed prediction of high‐frequency time series using additively decomposed components as features

IET Smart Cities, 2022
Traffic speed prediction is an integral part of an Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). Advanced knowledge of average traffic speed can help take proactive preventive steps to avoid impending problems.
Muhammad Ali, Kamaludin Mohamad Yusof, Benjamin Wilson, Carina Ziegelmueller   +3 more
doaj   +1 more source