Results 251 to 260 of about 6,391,262 (321)
Estimating the strength of bi-axially loaded track and channel cold formed composite column using different AI-based symbolic regression techniques. [PDF]
Ebid AM +3 more
europepmc +1 more source
ABSTRACT This study investigates how consumer taste and brand equity perceptions shape the acceptance of plant‐based milk products. Using a blind/informed tasting experiment, we evaluated consumers' willingness to buy (WTB) and taste perception of a plant‐based milk alternative produced by a traditional dairy brand, compared with competing plant‐based ...
Federico Parmiggiani +6 more
wiley +1 more source
ABSTRACT Food systems have a significant impact on environmental sustainability, underscoring the need for innovative technologies to support more sustainable agricultural methods. However, the adoption of these technologies hinges on consumer acceptance, making the analysis of consumer perceptions essential.
Greta Castellini, Guendalina Graffigna
wiley +1 more source
Symbolic Regression with Neural Symbolic Networks
This paper introduces a novel approach to symbolic regression, termed Neural Symbolic Networks (NSNs), designed to overcome the limitations of traditional neural networks in representing complex mathematical relationships. The core idea is to integrate the strengths of both neural networks and symbolic regression techniques.
openaire +1 more source
ABSTRACT Strategic positioning becomes increasingly important as markets mature, particularly in consumer‐facing industries that offer similar products, experiential cues, and values‐based messages. This study offers a conceptual model to examine the strategic positioning factors that motivate consumers to visit one local business over another before ...
Aaron J. Staples +2 more
wiley +1 more source
Learning regime‐dependent governing equations: A symbolic decision tree approach
Abstract Many chemical engineering systems are governed by mechanisms that switch across operating regimes, making the data‐driven discovery of regime‐dependent governing equations essential for predictive modeling, optimization, and control. We propose symbolic decision trees for the data‐driven discovery of regime‐dependent governing equations.
Ilias Mitrai +2 more
wiley +1 more source
Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira +13 more
wiley +1 more source
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
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A review on symbolic regression in power systems: Methods, applications, and future directions
Renewable and Sustainable Energy ReviewsAs power systems evolve with the increasing integration of renewable energy sources and smart grid technologies, there is a growing demand for flexible and scalable modeling approaches capable of capturing the complex dynamics of modern grids.
Pong Pwt
exaly +2 more sources

