Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms

Energy, Год журнала: 2023, Номер 282, С. 128446 - 128446

Опубликована: Июль 15, 2023

Язык: Английский

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An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms

Energy, Год журнала: 2024, Номер 296, С. 131259 - 131259

Опубликована: Апрель 9, 2024

Язык: Английский

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Impact of seasonal global land surface temperature (LST) change on gross primary production (GPP) in the early 21st century

Sustainable Cities and Society, Год журнала: 2024, Номер 110, С. 105572 - 105572

Опубликована: Июнь 3, 2024

Язык: Английский

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Estimation of transport CO2 emissions using machine learning algorithm

Transportation Research Part D Transport and Environment, Год журнала: 2024, Номер 133, С. 104276 - 104276

Опубликована: Июнь 5, 2024

Язык: Английский

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Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-Heuristic Algorithms

Energy, Год журнала: 2024, Номер 301, С. 131726 - 131726

Опубликована: Май 20, 2024

Язык: Английский

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Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images

Food Science & Nutrition, Год журнала: 2025, Номер 13(1)

Опубликована: Янв. 1, 2025

ABSTRACT Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity economic stability. Effective detection of maize crucial preventing disease spread ensuring high yields. This study presents VG‐GNBNet, an innovative transfer learning model that accurately detects healthy infected crops through two‐step feature extraction process. The proposed begins by leveraging the visual geometry group (VGG‐16) network to extract initial pixel‐based spatial features from images. These are then further refined using Gaussian Naive Bayes (GNB) decomposition‐based matrix factorization mechanism, which generates more informative classification purposes. incorporates machine models ensure comprehensive evaluation. By comparing VG‐GNBNet's performance against these models, we validate its robustness accuracy. Integrating deep techniques allows VG‐GNBNet capitalize on strengths both approaches, leading superior performance. Extensive experiments demonstrate VG‐GNBNet+GNB significantly outperforms other achieving impressive accuracy score 99.85%. highlights model's potential practical application in sector, where precise effective management yield optimization.

Язык: Английский

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Research on seismic performance prediction of CFST latticed column-composite box girder joint based on machine learning

Construction and Building Materials, Год журнала: 2025, Номер 460, С. 139811 - 139811

Опубликована: Янв. 1, 2025

Язык: Английский

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Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 127, С. 107356 - 107356

Опубликована: Ноя. 9, 2023

Язык: Английский

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Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption

Urban Climate, Год журнала: 2023, Номер 51, С. 101647 - 101647

Опубликована: Авг. 27, 2023

Язык: Английский

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Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection – A random forest machine learning approach

Fuel, Год журнала: 2023, Номер 357, С. 129914 - 129914

Опубликована: Сен. 26, 2023

Язык: Английский

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