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Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning

Jiuxin Wang, Yutian Luo, Zhengming Yang, Xinli Zhao et al. • Applied Sciences (2021)

In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in ...

DOAJ Open Access (DOAJ) DOI: 10.3390/app11083589

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

Gabriel de Sousa Meira, João Victor Ferreira Guedes, Edilson de Souza Bias • Drones (2024)

The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has...

DOAJ Open Access (DOAJ) DOI: 10.3390/drones8070341

Machine learning for biochemical engineering: A review

Max Mowbray, Thomas Savage, Chufan Wu, Ziqi Song et al. • Biochemical Engineering Journal (2021)

The field of machine learning is comprised of techniques, which have proven powerful approaches to knowledge discovery and construction of ‘digital twins’ in the highly dimensional, nonlinear and stochastic domains common to biochemical eng...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.bej.2021.108054

Machine learning in aerodynamic shape optimization

Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins • Progress in Aerospace Sciences (2022)

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and pr...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.paerosci.2022.100849

Machine learning insight in quality control and maintenance management of drilling mud pumps

Xiaoming Wu, Jingxuan Chang, Chengcheng Long, Hamzeh Ghorbani • Journal of Big Data (2026)

Abstract This study proposes a machine learning-based framework for predictive maintenance and quality control of drilling mud pumps, targeting critical operational challenges such as equipment failure, downtime, and high maintenance costs....

DOAJ Open Access (DOAJ) DOI: 10.1186/s40537-026-01475-9

Increased Atmospheric Aridity and Reduced Precipitation Drive the 2023 Extreme Wildfire Season in Canada

Gengke Lai, Yongguang Zhang • Geophysical Research Letters (2025)

Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiome...

DOAJ Open Access (DOAJ) DOI: 10.1029/2024GL114492

Introduction to Machine Learning

Richard J. Roiger • Chapman & Hall/CRC eBooks (2020)

R is one of the top-rated problem-solving tools for machine learning, data science, data analytics, data mining, and statistical analysis. R ’s popularity has resulted in the development of thousands of tutorials. Unfortunately, it’s easy t...

OPENALEX DOI: https://doi.org/10.1201/9781003006695-1

Machine learning for advanced energy materials

Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An • Energy and AI (2021)

The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low su...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.egyai.2021.100049

Using fuzzy and machine learning iterative optimized models to generate the flood susceptibility maps: case study of Prahova River basin, Romania

Romulus Costache, Hazem Ghassan Abdo, Arun Pratap Mishra, Subodh Chandra Pal et al. • Geomatics, Natural Hazards & Risk (2023)

AbstractIn this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating D...

DOAJ Open Access (DOAJ) DOI: 10.1080/19475705.2023.2281241

Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in Xinjiang, China

Asiguli Adili, Maihesumu Aikemu, Karanvir Singh Rana, Yihan Wei et al. • Scientific Reports (2025)

Abstract This study aims to identify risk factors associated with diabetic peripheral neuropathy (DPN) in patients with type 2 diabetesmellitus (T2DM) and to develop a predictive model to support clinical decision-making. A total of 1,001 p...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-025-19922-7

MARKETING ACTIVITIES OF IT COMPANIES: INFORMATION AND ORGANISATIONAL CAPABILITIES FOR DIGITAL PRODUCT DEVELOPMENT

Kostiantyn Fuks • Green, Blue and Digital Economy Journal (2024)

The purpose of this article is to provide a comprehensive examination of the informational and organisational capabilities of marketing activities in the market for digital products and services. It highlights the importance of data analysi...

DOAJ Open Access (DOAJ) DOI: 10.30525/2661-5169/2024-1-4

Application of Machine Learning in Microbiology

Kaiyang Qu, Fei Guo, Xiangrong Liu, Yuan Lin et al. • Frontiers in Microbiology (2019)

Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but...

OPENALEX cc-by DOI: https://doi.org/10.3389/fmicb.2019.00827 PMC: 6482238

Machine learning for chemical discovery

Alexandre Tkatchenko • Nature Communications (2020)

Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel mach...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-020-17844-8 PMC: 7431574