Machine learning-driven property predictions of polypropylene composites using IR spectroscopy DOI Creative Commons
Szilvia Klébert, Róbert Várdai, Anita Rácz

и другие.

Composites Science and Technology, Год журнала: 2025, Номер unknown, С. 111127 - 111127

Опубликована: Фев. 1, 2025

Язык: Английский

Artificial intelligence and machine learning models application in biodiesel optimization process and fuel properties prediction DOI
Muhammad Arif, Adel I. Alalawy, Yuanzhang Zheng

и другие.

Sustainable Energy Technologies and Assessments, Год журнала: 2024, Номер 73, С. 104097 - 104097

Опубликована: Ноя. 29, 2024

Язык: Английский

Процитировано

4
Advancing Algal Biofuel Production through Data-Driven Insights: A Comprehensive Review of Machine Learning Applications DOI

Omole Olakunle,

Chukwuma C. Ogbaga, Jude A. Okolie

и другие.

Computers & Chemical Engineering, Год журнала: 2025, Номер unknown, С. 109049 - 109049

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
New Viewpoint of Low-Carbon Economy Based on Green Materials and Processes via Systematic Review and Simulation Practices: Life Cycle Assessment, Energy Management, and Policymaking DOI

Raouf AliAkbari,

Elaheh Kowsari, Mohammad Gheibi

и другие.

Materials Circular Economy, Год журнала: 2025, Номер 7(1)

Опубликована: Фев. 22, 2025

Язык: Английский

Процитировано

0
Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions DOI Creative Commons

Aamir Raza,

Muhammad Adnan Shahid, Muhammad Zaman

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 774 - 774

Опубликована: Фев. 23, 2025

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely accurate yield prediction essential for ensuring security. There has been a growing use remote sensing, climate data, their combination to estimate yields, but optimal indices time window wheat in arid regions remain unclear. This study was conducted (1) assess performance widely recognized sensing predict at different growth stages, (2) evaluate predictive accuracy machine learning models, (3) determine appropriate period regions, (4) impact parameters on model accuracy. The vegetation indices, due proven effectiveness, used this include Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Atmospheric Resistance (ARVI). Moreover, four viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), Bagging (BTs), were evaluated region. whole divided into three windows: tillering grain filling (December 15–March), stem elongation (January heading (February–March 15). developed Google Earth Engine (GEE), combining data. results showed that RF with ARVI could accurately maturity stages an R2 > 0.75 error less than 10%. stage identified as regions. While delivered best results, GB EVI slightly lower precision still outperformed other models. It concluded multisource data models promising approach

Язык: Английский

Процитировано

0
Machine learning-driven property predictions of polypropylene composites using IR spectroscopy DOI Creative Commons
Szilvia Klébert, Róbert Várdai, Anita Rácz

и другие.

Composites Science and Technology, Год журнала: 2025, Номер unknown, С. 111127 - 111127

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0