Results 121 to 130 of about 6,285,273 (279)

Adversarial defense for battery state-of-health prediction models

ICT Express
This study addresses the challenge of state of health (SOH) estimation for lithium-ion batteries using a generative graphical approach under adversarial conditions.
Masoumeh Mohammadi, Insoo Sohn
doaj   +1 more source

Comparing Huber's M-Estimator Function with the

, 2000
In any data set there some of the data will be bad or noisy. This study identifies two types of noise and investigates the effect of each in the training data of backpropagation neural networks.
David Clark, Mean Square Error
core  

Mean Square Error (MSE) and coefficient of determination (R²) of the RR-BLUP, Bayes B, Bayesian Lasso, Bin 0.01, Bin 0.005 e Bin 0.001 models, for the three heritabilities considering three population structures.

, 2019
Mean Square Error (MSE) and coefficient of determination (R²) of the RR-BLUP, Bayes B, Bayesian Lasso, Bin 0.01, Bin 0.005 e Bin 0.001 models, for the three heritabilities considering three population structures.
Ernandes Guedes Moura (7534847), Marcio Balestre (767559), Andrezza Kellen Alves Pamplona (7534850)   +2 more
core   +1 more source

Is Precision Agriculture Technology Adoption Persistently Overestimated?

Agribusiness, EarlyView.
ABSTRACT Precision agriculture is sometimes assumed to diffuse steadily over time, and industry planning frequently extrapolates early adoption trends forward. This study evaluates the accuracy of such expectations by comparing agricultural input dealers' forecasts of future service offerings with the actual levels of offerings that dealerships ...
Trey Malone, Chad Fiechter, Zanliang Meng, James Lowenberg‐DeBoer, Bruce Erickson, Jay Akridge   +5 more
wiley   +1 more source

Development of improved estimators of finite population mean in simple random sampling with dual auxiliaries and its application to real world problems

Heliyon
In general, the incorporation of supplementary information reduces the Mean Square Error (MSE) and, consequently, enhances the precision of estimating a population parameter.
Khazan Sher, Muhammad Ameeq, Muhammad Muneeb Hassan, Olayan Albalawi, Ayesha Afzal   +4 more
doaj   +1 more source

Food Security Strategy, Macroeconomic Dynamics and Food Import Sensitivity in Qatar

Agribusiness, EarlyView.
ABSTRACT Qatar has limited agricultural land and water resources and therefore remains highly dependent on food imports. This structural dependency makes Qatar vulnerable to external shocks such as the recent Gulf crisis, threatening food security and disrupting global supply chains. In response to previous shocks, Qatar started implementing a National
Hansini Ranjan, Simeon Kaitibie, Alanoud Ali S. Al‐Maadid, John N. Ng'ombe, Ishrat Hossain, Mostafizur Rahman   +5 more
wiley   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

AIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin, Jules Thibault, Clémence Fauteux‐Lefebvre   +2 more
wiley   +1 more source

An Error Assessment of the Kriging Based Approximation Model Using a Mean Square Error

, 2006
A Kriging model is a sort of approximation model and used as a deterministic model of a computationally expensive analysis or simulation. Although it has various advantages, it is difficult to assess the accuracy of the approximated model.
정도현, 이병채, 주병현, 조태민   +3 more
core   +1 more source

Kernel Mean P Power Error Loss for Robust Two-Dimensional Singular Value Decomposition

, 2019
Traditional matrix-based dimensional reduction methods, e.g., two-dimensional principal component analysis (2DPCA) and two-dimensional singular value decomposition (2DSVD), minimize mean square errors (MSE), which is sensitive to outliers.
Gao, Y, Sun, C, Zhang, M, Blumenstein, M, Michael Blumenstein, Changming Sun, Yongsheng Gao, Miaohua Zhang   +7 more
core   +1 more source

Development and experimental validation of a predictive resilient framework for cyber‐physical systems

AIChE Journal, EarlyView.
Abstract Cyber‐physical process systems integrate process dynamics, control, sensing, actuation, and communication, making them vulnerable to attacks that falsify data, alter control actions, or disrupt networks and drive operations toward unsafe states.
Luyang Liu, Ritu Ranjan, Zaman Sajid, Benjamin Wilhite, Costas Kravaris, Faisal Khan   +5 more
wiley   +1 more source