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Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

• Authors : Alreshidi I; Centre for Computational Engineering Sciences, Cranfield University, Cranfield MK43 0AL, UK.; Machine Learning and Data Analytics Laboratory, Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, Cranfield MK43 0AL, UK.

Subjects: Neural Networks, Computer* ; Machine Learning*; Electroencephalography/Electroencephalography/Electroencephalography/methods

• Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Nov 08; Vol. 23 (22). Date of Electronic Publication: 2023 Nov 08.Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220

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Nonparametric failure time: Time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures.

• Authors : Sparapani RA; Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.; Logan BR

Subjects: Software* ; Machine Learning*; Humans

• Source: Biometrics [Biometrics] 2023 Dec; Vol. 79 (4), pp. 3023-3037. Date of Electronic Publication: 2023 Apr 16.Publisher: Biometric Society Country of Publication: England NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Advancements in Cold Spray Additive Manufacturing: Process, Materials, Optimization, Applications, and Challenges.

• Authors : Kafle, Abishek¹ (AUTHOR) ; Silwal, Raman¹ (AUTHOR) ; Koirala, Bikram¹ (AUTHOR)

• Source: Materials (1996-1944). Nov2024, Vol. 17 Issue 22, p5431. 27p.

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Explaining the Anomaly Detection in Additive Manufacturing via Boosting Models and Frequency Analysis.

• Authors : Vozza, Mario^1,2 (AUTHOR) ; Polden, Joseph³ (AUTHOR) ; Mattera, Giulio⁴ (AUTHOR)

• Source: Mathematics (2227-7390). Nov2024, Vol. 12 Issue 21, p3414. 17p.

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Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study.

• Authors : Ahmadiankalati, Mojtaba¹ (AUTHOR); Boury, Himani¹ (AUTHOR); Subbarao, Padmaja^2,3 (AUTHOR)

• Source: BMC Medical Research Methodology. 11/1/2024, Vol. 24 Issue 1, p1-11. 11p.

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Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review.

• Authors : Xiao, Shaoping¹ (AUTHOR) ; Li, Junchao¹ (AUTHOR); Wang, Zhaoan¹ (AUTHOR)

• Source: Future Internet. Nov2024, Vol. 16 Issue 11, p419. 30p.

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Active learning for prediction of tensile properties for material extrusion additive manufacturing.

• Authors : Nasrin T; Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA.; Pourali M

Subjects: Algorithms* ; Machine Learning*; Data Collection

• Source: Scientific reports [Sci Rep] 2023 Jul 15; Vol. 13 (1), pp. 11460. Date of Electronic Publication: 2023 Jul 15.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Progress in Additive Manufacturing of Magnesium Alloys: A Review.

• Authors : Chen, Jiayu¹ (AUTHOR) ; Chen, Bin¹ (AUTHOR)

• Source: Materials (1996-1944). Aug2024, Vol. 17 Issue 15, p3851. 28p.

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Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study

• Authors : Mojtaba Ahmadiankalati; Himani Boury; Padmaja Subbarao

Subjects: Asthma; Bayesian additive regression tree; CHILD Cohort Study

• Source: BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-11 (2024)

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A model for predicting academic performance on standardised tests for lagging regions based on machine learning and Shapley additive explanations

• Authors : Mario Suaza-Medina; Rita Peñabaena-Niebles; Maria Jubiz-Diaz

Subjects: CRISP-DM; Educational data mining; Lagging region

• Source: Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)

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