Results 191 to 200 of about 91,882 (291)

EFL quarterly : an E-Finance Lab publication ; 4/2008 [PDF]

, 2011
E-Finance Lab
core  

Perturbation‐induced responses improved seizure forecasting in epileptic rats

Epilepsia, EarlyView.
Abstract Objective The unpredictability of seizures is one of the most challenging aspects of uncontrolled epilepsy for patients. Prior work forecasting seizure risk has measured changes in passive intracranial electroencephalographic (EEG) signals, but currently, there are no such clinical devices available.
Wei‐Chih Chang, Jack Lin, Warwick Cheung, Alan Lai, Mark J. Cook, David B. Grayden, William C. Stacey   +6 more
wiley   +1 more source

Novel methods for selecting stock portfolio in conditions of uncertainty and forecasting with RR-DEA, ANFIS, FGP: A case study of Tehran stock exchange. [PDF]

PLoS One
Taheri M, Azizi A, Mohammadi E, Saghaei A.   +3 more
europepmc   +1 more source

Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman, Enrico Tubaldi, Roberto Gentile, John Douglas   +3 more
wiley   +1 more source

Portfolio Model Considering Normal Uncertain Preference Relations of Investors. [PDF]

Entropy (Basel)
Zhou Y, Yan C, Wang X.
europepmc   +1 more source

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source

How our advisory board members are using generative AI in teaching. [PDF]

Patterns (N Y)
Patel P, Mani G.
europepmc   +1 more source

AI‐Driven Optimization of a Hybrid PV–Wind–BESS Microgrid for a Rural Educational Institution in Developing Countries

Energy Science &Engineering, EarlyView.
An AI‐driven CNN–LSTM forecasting framework is integrated with HOMER Pro to optimally design a grid‐connected PV–wind–BESS microgrid for a rural school in Bangladesh, achieving 91.7% renewable penetration, low energy cost (0.0397 USD/kWh), and an 81.5% reduction in CO2 emissions. ABSTRACT Hybrid renewable microgrid planning in HOMER Pro often relies on
Robiul Khan, Md. Wajed Ali, Md. Feroz Ali, Md. Shafiul Alam, Mohammad Ali, Imil Hamda Imran   +5 more
wiley   +1 more source

Information-Theoretic Cost-Benefit Analysis of Hybrid Decision Workflows in Finance. [PDF]

Entropy (Basel)
Beaucamp P, Maylor H, Chen M.
europepmc   +1 more source

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley   +1 more source