Development of a Hybrid Intelligence Algorithm to Estimate the Derivative Weight of Dawakin Tofa Clay for Heat Storage DOI Open Access

Abubakar D. Maiwada,

Abdullahi Adamu, Umar Danjuma Maiwada

и другие.

AUIQ technical engineering science., Год журнала: 2024, Номер 1(2)

Опубликована: Дек. 16, 2024

The accurate prediction of thermogravimetric properties is critical for evaluating the suitability natural materials like Dawakin Tofa clay heat storage applications, but traditional linear models often fail to capture complex, non-linear relationships inherent in such datasets. This study develops a hybrid intelligence framework integrating Bilateral Neural Network (BNN), Kernel Support Vector Machine (KSVM), Step-Wise Linear Regression (SWLR), and Robust (RLR) predict derivative weight based on 5,030 experimentally obtained instances. Comprehensive data preprocessing, including normalization, feature selection, dataset splitting (80% training 20% testing), ensured high-quality inputs models. results demonstrated that significantly outperformed approaches, with BNN achieving coefficient determination R² 0.999, Mean Absolute Error (MAE) 0.004377, Percentage (MAPE) 9.6% testing dataset. Similarly, KSVM achieved an MAE 0.012134, MAPE 26.7%, indicating its robust predictive capabilities. In contrast, performed poorly, SWLR RLR yielding values 0.03 -0.41, respectively, unacceptably high 612% 53.5%. findings underscore limitations predicting complex behaviors highlight transformative potential advanced machine learning techniques KSVM. Furthermore, these align global sustainability efforts, SDG 7 12, by optimizing use locally available, eco-friendly energy storage. provides replicable leveraging artificial enhance material characterization, offering significant step toward developing sustainable solutions.

Язык: Английский

Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models DOI Creative Commons

Hongkun Fu,

Jian Lü,

Jian Li

и другие.

Agronomy, Год журнала: 2025, Номер 15(1), С. 205 - 205

Опубликована: Янв. 16, 2025

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding management, and optimizing resource allocation. This study proposes a method predicting yields in China’s major winter wheat-producing regions using MOD13A1 data deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of Convolutional Neural Network (CNN) with IGWO, accuracy significantly enhanced. Additionally, explores potential Green Normalized Difference Vegetation Index (GNDVI) prediction. The research utilizes collected from March to May between 2001 2010, encompassing vegetation indices, environmental variables, statistics. results indicate that IGWO-CNN outperforms traditional machine approaches standalone CNN models terms accuracy, achieving highest performance R2 0.7587, RMSE 593.6 kg/ha, MAE 486.5577 MAPE 11.39%. finds April optimal period early wheat. validates effectiveness combining remote sensing prediction, providing technical support precision agriculture contributing global food security sustainable development.

Язык: Английский

Процитировано

1
Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models DOI
Mohsen Saroughi, Okan Mert Katipoğlu, Gaye Aktürk

и другие.

Acta Geophysica, Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Язык: Английский

Процитировано

0
Machine learning-enhanced prediction of sensible heat storage potential in Kano-Nigeria based on thermogravimetric analysis DOI Creative Commons

Abubakar D. Maiwada,

Abdullahi Adamu, Jamilu Usman

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Abstract The challenge of efficiently predicting the sensible heat storage potential natural materials like Dawakin Tofa clay for sustainable energy applications necessitates innovative solutions. This study investigates use machine learning models: Interactive Linear Regression (ILR), Stepwise (SWLR), Robust (RLR), and (Kernel Support Vector Machine (KSVM). Also, four non-linear models were employed as: G-Matern 5/2 (GM5/2), Trilayered neural network (TNN), Boosted Tree (BoT) bagged Neural Networks (BTNN). Further, some ensemble methods used are: Simple Average Ensemble (SAE), Weighted (WAE), Network (NNE). In laboratory, test was carried out at Centre Genetics Engineering Biotechnology Federal University Technology in Minna, Niger State, Nigeria. sample placed a platinum pan, then heated it rate 10°C per minute while using nitrogen air as purge gases. entire experiment took 33 minutes to complete, with results printed documentation. To ensure accuracy, we repeated analysis three times averaged results. By utilizing locally abundant clay, research promotes cost-effective solutions, reducing reliance on synthetic lowering environmental footprint. Among models, NNE exhibited best performance, achieving near-perfect accuracy minimal error metrics (MSE = 0.000212, RMSE 0.01456 training; MSE 0.0001696, 0.01302 testing). SAE demonstrated moderate reliable generalization, WAE showed high variability training weaker despite improvement testing phase. highlights superiority nonlinear particularly (NNE), accurately modeling thermal behavior sample. It also provides foundation optimizing storage, recommending material modifications, expanded datasets, pilot-scale studies, economic assessments. further underscores integrating advanced techniques create scalable, systems, addressing critical challenges transition renewable energy.

Язык: Английский

Процитировано

0
Data-driven compressive strength investigation and design suggestions for rubberized concrete DOI
Chang Zhou, Yuzhou Zheng

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112477 - 112477

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
A novel high-frequency cutting force compensation model for micro-milling polyvinylidene fluoride thin films tri-axis force sensors based on machine learning algorithms DOI
Hong Huang, Honghao Zhao,

Chongtian Zhang

и другие.

Precision Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Predictors of Self-Rated Health by Considering Socio-Economic and Regional Differences in Turkey DOI
Songül Çınaroğlu

Journal of Social Service Research, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Апрель 30, 2025

Язык: Английский

Процитировано

0
Development of a Hybrid Intelligence Algorithm to Estimate the Derivative Weight of Dawakin Tofa Clay for Heat Storage DOI Open Access

Abubakar D. Maiwada,

Abdullahi Adamu, Umar Danjuma Maiwada

и другие.

AUIQ technical engineering science., Год журнала: 2024, Номер 1(2)

Опубликована: Дек. 16, 2024

The accurate prediction of thermogravimetric properties is critical for evaluating the suitability natural materials like Dawakin Tofa clay heat storage applications, but traditional linear models often fail to capture complex, non-linear relationships inherent in such datasets. This study develops a hybrid intelligence framework integrating Bilateral Neural Network (BNN), Kernel Support Vector Machine (KSVM), Step-Wise Linear Regression (SWLR), and Robust (RLR) predict derivative weight based on 5,030 experimentally obtained instances. Comprehensive data preprocessing, including normalization, feature selection, dataset splitting (80% training 20% testing), ensured high-quality inputs models. results demonstrated that significantly outperformed approaches, with BNN achieving coefficient determination R² 0.999, Mean Absolute Error (MAE) 0.004377, Percentage (MAPE) 9.6% testing dataset. Similarly, KSVM achieved an MAE 0.012134, MAPE 26.7%, indicating its robust predictive capabilities. In contrast, performed poorly, SWLR RLR yielding values 0.03 -0.41, respectively, unacceptably high 612% 53.5%. findings underscore limitations predicting complex behaviors highlight transformative potential advanced machine learning techniques KSVM. Furthermore, these align global sustainability efforts, SDG 7 12, by optimizing use locally available, eco-friendly energy storage. provides replicable leveraging artificial enhance material characterization, offering significant step toward developing sustainable solutions.

Язык: Английский

Процитировано

0