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Academic Articles & Research Papers
Showing 20 of 188 academic articles
Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov et al. • arXiv (Cornell University)
(2019)
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient desc...
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DOI: https://doi.org/10.48550/arxiv.1902.02476
Jong-Min Kim • Axioms
(2025)
This paper presents the use of Copula-based deep learning with Horvitz–Thompson (HT) weights and inverse probability of treatment weighting (IPTW) for estimating propensity scores in causal inference problems. This study compares the perfor...
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Open Access (DOAJ)
DOI: 10.3390/axioms14060458
Kaixuan Sun, Qi Yao, Yanhui Li • PLoS ONE
(2025)
The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challengi...
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Open Access (DOAJ)
DOI: 10.1371/journal.pone.0337103
Chang Min Hyun, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee et al. • Physics in Medicine and Biology
(2018)
This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is u...
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DOI: https://doi.org/10.1088/1361-6560/aac71a
Samane Esmaelzade Kalkhoran, Seyyed Saeed Ghannadpour • Scientific Reports
(2026)
Abstract Porphyry Cu deposits in the Urmia–Dokhtar magmatic belt constitute important exploration targets, yet the heterogeneous and multi-scale nature of geological, geochemical, and remote sensing data introduces substantial uncertainty i...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-61085-6
Theodore Spaide, PhD, Anand E. Rajesh, MD, Nayoon Gim, Marian Blazes, MD et al. • Ophthalmology Science
(2025)
Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participant...
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Open Access (DOAJ)
DOI: 10.1016/j.xops.2024.100587
Ajantha Vijayakumar, Joseph Abraham Sundar Koilraj, Muthaiah Rajappa • International Journal of Computational Intelligence Systems
(2025)
Abstract Oral dosage forms are the most widely employed method of drug delivery in therapeutic treatments. However, the presence of visual defects in blister packages can adversely affect the drug's bioavailability and therapeutic efficacy,...
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Open Access (DOAJ)
DOI: 10.1007/s44196-025-00986-2
Fangzhou Liu, Yang Zhao, Jiqiao Chen, Ting Luo • EAI Endorsed Transactions on Energy Web
(2026)
Power distribution network fault handling imposes stringent requirements on both response timeliness and decision reliability. The high penetration of distributed renewable generation, the widespread deployment of power-electronic devices, ...
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Open Access (DOAJ)
DOI: 10.4108/ew.13683
Ahmed E. Radwan, David A. Wood, Ahmed A. Radwan • Journal of Rock Mechanics and Geotechnical Engineering
(2022)
Pore pressure is an essential parameter for establishing reservoir conditions, geological interpretation and drilling programs. Pore pressure prediction depends on information from various geophysical logs, seismic, and direct down-hole pre...
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Open Access (DOAJ)
DOI: 10.1016/j.jrmge.2022.01.012
Tanattrin Bunnag • Forecasting
(2026)
Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GA...
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Open Access (DOAJ)
DOI: 10.3390/forecast8050087
Sina Ardabili, Amir Hosein Mosavi, Pedram Ghamisi, Ferdinánd Filip et al. • Algorithms
(2020)
Abstract Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simpl...
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DOI: https://doi.org/10.3390/a13100249
Ayaz Ahmad, Fawad Ahmad, Muhammad Suleman Khan, Adel Aldalbahi • PeerJ Computer Science
(2026)
Proactive caching in multi-tier cellular networks (MTCNs) is an efficient technique for alleviating heavy data traffic on backhaul links, thereby reducing content latency and increasing the overall cache hit ratio (CHR) and user satisfactio...
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Open Access (DOAJ)
DOI: 10.7717/peerj-cs.4004
Simon Willcock, Javier Martínez‐López, Danny A. P. Hooftman, Kenneth J. Bagstad et al. • Ecosystem Services
(2018)
Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within i...
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DOI: https://doi.org/10.1016/j.ecoser.2018.04.004
Heming Jia, Marjan Kordani, Iman Ahmadianfar, Arvin Samadi Koucheksaraee et al. • Engineering Applications of Computational Fluid Mechanics
(2026)
Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices usin...
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Open Access (DOAJ)
DOI: 10.1080/19942060.2025.2602582
Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan, Denis Navarro et al. • IEEE Open Journal of the Industrial Electronics Society
(2026)
This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction...
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Open Access (DOAJ)
DOI: 10.1109/OJIES.2026.3663897
Paul Sajda • Annual Review of Biomedical Engineering
(2006)
Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art...
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DOI: https://doi.org/10.1146/annurev.bioeng.8.061505.095802
William W. Cohen, Andrew McCallum, Sam T. Roweis (2008)
This volume contains the papers accepted to the 25th International Conference on Machine Learning (ICML 2008). ICML is the annual conference of the International Machine Learning Society (IMLS), and provides a venue for the presentation and...
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Fahad Ahmed Khokhar, Tommaso Zoppi, Luigi Cennini, Andrea Ceccarelli et al. • Scientific Reports
(2026)
Abstract The last decade has seen a surging opportunity for developing autonomous systems (e.g., fully autonomous driving, computer-guided robotic surgery, mobile robots for inspections and surveillance, and manufacturing robots) to relieve...
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Open Access (DOAJ)
DOI: 10.1038/s41598-026-45091-2
Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu et al. • Journal of Hydrology: Regional Studies
(2025)
Study Area: The Tailan River Basin in the Aksu region and the Yulong Kashi River in the Hotan River Basin of Xinjiang are located at respective geographical coordinates of 80°21'44'' to 81°10'14'' E, 40°41'41'' to 42°15'13'' N, and 77.25° t...
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Open Access (DOAJ)
DOI: 10.1016/j.ejrh.2025.102311
Nikolaos Sifakis, Antonios Kapenis, Athanasios Kolios, George Arampatzis • Energies
(2026)
Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative as...
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Open Access (DOAJ)
DOI: 10.3390/en19122897