Machine learning models

This study advances forest fire susceptibility mapping in Gia Lai province by leveraging optimized machine learning models.

13p vijiraiya 19-05-2025 1 1   Download

This paper presents an advanced machine learning model, named ES-ANN, which combines an Artificial NeuralNetwork (ANN) with Evolution Strategies (ES) to predict flyrock distance in open-pit mines with high accuracy.

14p vijiraiya 19-05-2025 1 1   Download

This study focuses on developing a machine learning model through the process of analyzing. comparing, and evaluating the performance of five models: AdaBoost, Decision Tree, RandomForest, ExtraTree, and BernoulliNB. All models are implemented using the "Predict Student Dropout Dataset." Based on the results obtained after processing the data, the study will conduct an analysis based on two main criteria: evaluation by average percentage, standard deviation, and final outcomes, as well as evaluation using a time-series model of age (Balanced Accuracy Progression).

16p visarada 28-04-2025 1 1   Download

In this article, we will discuss the use of a database, called Churn Modeling, which collects statistical data from banks. We will also explore the application of the BernoulliNB algorithm, combined with the incremental machine learning method, to process streaming data and analyze and predict customer churn rates in banks.

10p visarada 28-04-2025 1 1   Download

Ontology matching, a field within data management and information retrieval, utilizes machine learning to integrate and link different data sources by identifying equivalences and relationships between concepts across various ontologies. This report provides an overview of key techniques and methods in ontology matching, including vectorization and embeddings such as Word2Vec, similarity measure like Levenshtein Distance and Jaccard Index, and machine learning models that improve accuracy in classifying and matching concepts.

11p visarada 28-04-2025 1 1   Download

The research focuses on applying deep learning to automate the fruit recognition and classification process, meeting the development needs of modern agriculture. Applying this technology helps improve efficiency and classification quality and reduces labor costs, resulting in lower product prices. The research team used two deep learning models, SSD300 and YOLOv10s, to recognize and classify six types of fruits: apples, bananas, kiwis, lemons, oranges, and strawberries.

9p vimaito 11-04-2025 1 1   Download

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