Machine Learning Techniques Used in Big Data

Nita, Stefania Loredana

doi:10.21279/1454-864x-16-i1-078

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…As a consequence, models for evaluating massive and complicated data, as well as for delivering faster and more accurate results, may be created quickly and automatically. The application of these models results in highly exact predictions, allowing for more competent judgments and intelligent actions to be made in real-time without the need for human intervention [ 26 ]. The development of ML models to predict concrete strength is now underway to decrease the wastage of materials and experimental cycles.…”

Section: Introductionmentioning

confidence: 99%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

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Section: Introductionmentioning

confidence: 99%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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Improved information dissemination services for the agricultural sector in Thailand: development and evaluation of a machine learning based rice crop yield prediction system

Ngandee,

Taparugssanagorn

2023

Information Development

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The upward surge in world population, sustainability, and concerns about food and nutritional security place the food and agricultural sector amidst challenges. These challenges encompass increasing food supply, meeting higher quality standards, and enhancing productivity and crop yield prediction. This study discusses the development and evaluation of a Machine Learning (ML) based rice yield prediction system. Utilizing extensive historical datasets from the Office of Agricultural Economics (OAE), Ministry of Agriculture and Cooperatives (MOAC), Thai Meteorological Department, and Department of Internal Trade of Thailand, all pivotal variables at the national rice level were considered. Examining ML models like the Generalized Linear Model (GLM), Feed-Forward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest (RF), the study proposes a Web-based system to disseminate Thailand's rice information and yield predictions, aiding decision-making processes. Beyond evaluating the performance of each prediction model, user satisfaction was scrutinized. Results reveal that the FFNN, a deep neural network, adeptly handles complex nonlinear relationships in high-dimensional datasets. Despite the FFNN's longer training runtime due to Big-O complexity, it exhibits the shortest execution time for predictions. System usability assessment, based on ten standardized questions, indicates participants found the proposed system marginally acceptable, reporting positive user experiences and feeling confident in system use without perceiving it as inconsistent or cumbersome. This study is significant for enhancing agricultural information dissemination services across diverse sectors, benefiting farmers, wholesalers, retailers, and policymakers.

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Machine Learning Techniques Used in Big Data

Cited by 2 publications

References 9 publications

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Improved information dissemination services for the agricultural sector in Thailand: development and evaluation of a machine learning based rice crop yield prediction system

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