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   Articles with "explainable artificial" as a keyword



Memristive Explainable Artificial Intelligence Hardware

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recommendations! Published in 2024 at "Advanced Materials"

DOI: 10.1002/adma.202400977

Abstract: Artificial intelligence (AI) is often considered a black box because it provides optimal answers without clear insight into its decision‐making process. To address this black box problem, explainable artificial intelligence (XAI) has emerged, which provides… read more here.

Keywords: memristor; hardware; xai; artificial intelligence ... See more keywords

Sunflower yield modeling with explainable artificial intelligence: Historical weather impacts across half a century of American production

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recommendations! Published in 2025 at "Agronomy Journal"

DOI: 10.1002/agj2.70204

Abstract: This study applies explainable artificial intelligence (XAI) to analyze the impact of inter‐year variation in weather conditions on yields of oilseed sunflower ( Helianthus annuus L.) across the United States. By integrating historical county‐level yield… read more here.

Keywords: artificial intelligence; yield; weather; explainable artificial ... See more keywords

HayCAMJ: A new method to uncover the importance of main filter for small objects in explainable artificial intelligence

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recommendations! Published in 2024 at "Neural Computing and Applications"

DOI: 10.1007/s00521-024-09640-y

Abstract: Visual XAI methods enable experts to reveal importance maps highlighting intended classes over input images. This research paper presents a novel approach to visual explainable artificial intelligence (XAI) for object detection in deep learning models.… read more here.

Keywords: importance; method; artificial intelligence; small objects ... See more keywords

A novel explainable artificial intelligence framework using knockoffs techniques with applications to sports analytics

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recommendations! Published in 2025 at "Annals of Operations Research"

DOI: 10.1007/s10479-025-06575-y

Abstract: The rapid integration of black-box Machine Learning (ML) models into critical decision-making scenarios has triggered an urgent call for transparency from stakeholders in Artificial Intelligence (AI). This call stems from growing concerns about the deployment… read more here.

Keywords: artificial intelligence; framework; decision making; sports analytics ... See more keywords
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Explainable artificial intelligence to evaluate industrial internal security using EEG signals in IoT framework

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recommendations! Published in 2021 at "Ad Hoc Networks"

DOI: 10.1016/j.adhoc.2021.102641

Abstract: Abstract Industrial insider threat detection has consistently been a popular field of research. To help detect potential insider threats, the emotional states of humans are identified through a wide range of physiological signals including the… read more here.

Keywords: artificial intelligence; insider; eeg signals; security ... See more keywords

Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence

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recommendations! Published in 2021 at "Applied Energy"

DOI: 10.1016/j.apenergy.2021.116807

Abstract: Abstract In this paper, we present a newly developed eXplainable artificial intelligence (XAI) model to analyze the impacts of climate change on the cooling energy consumption ( E c ) in buildings, predict long-term E… read more here.

Keywords: artificial intelligence; climate change; energy; explainable artificial ... See more keywords

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

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recommendations! Published in 2021 at "Computer methods and programs in biomedicine"

DOI: 10.1016/j.cmpb.2021.106415

Abstract: BACKGROUND AND OBJECTIVE Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that… read more here.

Keywords: adverse outcome; explainable artificial; adverse outcomes; pharmacovigilance ... See more keywords

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence

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recommendations! Published in 2022 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.1c01263

Abstract: In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important for… read more here.

Keywords: explainable artificial; structure activity; artificial intelligence; activity ... See more keywords

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

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recommendations! Published in 2022 at "Journal of chemical information and modeling"

DOI: 10.1021/acs.jcim.2c01126

Abstract: Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the… read more here.

Keywords: developmental toxicity; toxicology; explainable artificial; intelligence ... See more keywords

Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2

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recommendations! Published in 2022 at "ACS Omega"

DOI: 10.1021/acsomega.1c06976

Abstract: To improve virtual screening for drug discovery, we present a collaborative approach between explainable artificial intelligence (AI) and simplified chemical interaction scores to efficiently search for active ligands bound to the target receptor. In particular,… read more here.

Keywords: collaborative approach; explainable artificial; intelligence simplified; approach explainable ... See more keywords

Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics

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recommendations! Published in 2022 at "Journal of Advances in Modeling Earth Systems"

DOI: 10.1029/2022ms003162

Abstract: The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision‐making… read more here.

Keywords: explainable artificial; neural network; bnn; neural networks ... See more keywords