Results 101 to 110 of about 33,125 (301)

The Contemplation of Explainable Artificial Intelligence Techniques: Model Interpretation using Explainable AI

open access: yes, 2022
Machine intelligence and data science are two disciplines that are attempting to develop Artificial Intelligence. Explainable AI is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems.
Tavishee Chauhan   +3 more
core   +1 more source

Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS

open access: yesScientific Reports
Chronic kidney disease is a persistent ailment marked by the gradual decline of kidney function. Its classification primarily relies on the estimated glomerular filtration rate and the existence of kidney damage.
Ahmed M. Elshewey   +2 more
doaj   +1 more source

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

open access: yesAdvanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour   +5 more
wiley   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

open access: yesAdvanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang   +2 more
wiley   +1 more source

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

open access: yes, 2020
Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered \u27black-box\u27 and \u27hard-to-debug\u27.
Nils, Weskamp   +3 more
core   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

open access: yesAdvanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara   +8 more
wiley   +1 more source

Explainable Artificial Intelligence Approaches in Primary Education: A Review

open access: yes
Artificial intelligence (AI) methods have been integrated in education during the last few decades. Interest in this integration has increased in recent years due to the popularity of AI.
Jim Prentzas, Ariadni Binopoulou
core   +1 more source

Investigating Human-Centered Perspectives in Explainable Artificial Intelligence

open access: yes, 2023
The widespread use of Artificial Intelligence (AI) in various domains has led to a growing demand for algorithmic understanding, transparency, and trustworthiness.
Muhammad Suffian   +3 more
core  

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

open access: yesAdvanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán   +5 more
wiley   +1 more source

Does Explainable Artificial Intelligence Improve Human Decision-Making?

open access: yes, 2020
Explainable AI provides insights to users into the why for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect.
Marusich, Laura R.   +9 more
core   +1 more source

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