Machine learning models

In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.

8p viling 11-10-2024 1 1   Download

In this study, we propose a novel approach using the oscillation characteristics of the RMS current as the input to machine learning models, combined with the confident learning technique. Using the oscillation characteristics obtained by taking a discrete Fourier transform (DFT) of the RMS current as model input, we aim to reduce the computational requirements of the machine learning models.

12p viling 11-10-2024 4 1   Download

Accurate forecasting of the electrical load is a critical element for grid operators to make well-informed decisions concerning electricity generation, transmission, and distribution. In this study, an Extreme Learning Machine (ELM) model was proposed and compared with four other machine learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

10p viengfa 28-10-2024 2 1   Download

Artificial neural networks, which are an essential tool in Machine Learning, are used to solve many types of problems in different fields. This article will introduce an application of the artificial neural network model in the diagnosis of heart disease based on the heart.csv data file.

6p viengfa 28-10-2024 4 2   Download

The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed.

9p viengfa 28-10-2024 3 2   Download

This article conducts an exhaustive investigation into the utilization of machine learning (ML) methods for forecasting the maximum load capacity (MLC) of circular reinforced concrete columns (CRCC) using Fiber-Reinforced Polymer (FRP). Extreme Gradient Boosting (XGB) algorithm is combined with novel metaheuristic algorithms, namely Sailfish Optimizer and Aquila Optimizer, to fine-tune its hyperparameters.

18p viengfa 28-10-2024 2 2   Download

Predicting the macroscopic permeability of porous media is critical in various scientific and engineering applications. This study proposes a novel model that combines Random Forest (RF) and rime-ice (RIME) optimization algorithm, denoted RIME-RF-RIME, to predict permeability based on six key features covering fluid phase dimensions, geometric characteristics, surrounding phase permeability, and media porosity.

14p viengfa 28-10-2024 2 2   Download

In this study, we aim to delineate landslide susceptibility zones within Dien Bien province, Vietnam, leveraging the capabilities of various machine learning models including Light Gradient Boosting Machine (LGBM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB).

19p viengfa 28-10-2024 4 2   Download

This study delves into the application of machine learning (ML), specifically a Gradient Boosting (GB) model, for predicting the punching shear strength (PSS) of two-way reinforced concrete flat slabs.

16p viengfa 28-10-2024 3 2   Download

This study introduces and evaluates the Long-term Traffic Prediction Network (LTPN), a specialized machine learning framework designed for realtime traffic prediction in urban environments.

12p viengfa 28-10-2024 3 2   Download

Efficient ship detection is essential for inland waterway management. Recent advances in artificial intelligence have prompted research in this field. This study introduces a real-time ship detection model utilizing computer vision and the YOLO object detection framework.

14p viengfa 28-10-2024 2 2   Download

This paper presents the development of an Artificial Intelligence (AI) and Machine Learning (ML) model designed to detect cracks on concrete surfaces. The objective is to enhance the automation, precision, and performance of crack detection using the computer vision algorithm.

13p viengfa 28-10-2024 2 2   Download

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures.

17p viengfa 28-10-2024 2 2   Download

The study is a complement to the studies of the tensile strength of cement paste backfill gradually becoming complete. Evaluate the reliability of the proposed model and analyze the influence of the components on the tensile strength. At the same time, the study is also interested in the influence of the components.

9p viengfa 28-10-2024 4 2   Download

Rubberized concrete is a material that is both ecologically friendly and sustainable, and it has been finding more and more usage in building applications recently. In this study, a machine learning model, namely LightGBM, is developed to predict rubberized concrete's compressive strength (CS) using 11 input parameters.

18p viengfa 28-10-2024 3 1   Download

This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles.

8p viengfa 28-10-2024 3 2   Download

In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output.

12p viengfa 28-10-2024 3 2   Download

This paper develops an Artificial Neural Network (ANN) model based on 96 experimental data to forecast the dynamic modulus of asphalt concrete mixtures. This study applied the repeated KFold cross-validation technique with 10 folds on the training data set to make the simulation results more reliable and find a model with more general predictive power.

9p viengfa 28-10-2024 5 2   Download

This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete Cs. A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model.

12p viengfa 28-10-2024 5 2   Download

In this study, an artificial neural networkbased Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition.

12p viengfa 28-10-2024 2 2   Download