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In this study, the Quantum-PSO (QPSO) method is proposed to enhance the global search performance and convergence ability of PSO by incorporating concepts from quantum mechanics. The QPSO method was tested alongside Binary PSO and PSO on the same grid model to compare these methods.

7p vijiraiya 19-05-2025 2 1   Download

In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iterative Sequential DNN (ISDNN), inspired by recent developments in data detection algorithms. ISDNN is a DNN based on the projected gradient descent algorithm for CE problems, with the iterative iterations transforming into a DNN using the deep unfolding method.

7p vijiraiya 19-05-2025 1 1   Download

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

To overcome the technical restriction caused by the actuator shortfall, this paper introduces a new control strategy for underactuated AUVs(UAUVs) with five degrees of freedom, utilising the recursion technique and an artificial neural network.

15p vijiraiya 19-05-2025 6 1   Download

The problem of reducing traffic congestion on highways is one of the conundrums that the transport industry as well as the government would like to solve. In this article, we apply the Double Deep Q-Network (DDQN) algorithm to a multi-agent model of traffic congestion and compare it with two other algorithms.

8p vimitsuki 06-05-2025 2 1   Download

In this study, we utilized the YOLO (You Only Look Once) model to evaluate the performance of real-time vehicle detection and classification. Additionally, we adjusted the learning rate parameter to achieve optimal performance.

9p vimaito 11-04-2025 0 0   Download

This paper investigates the system performance of hybrid time-power switching based relaying (TPSR) energy harvesting enabled in the multisource half-duplex relaying network over the Rayleigh fading channel. The outage probability (OP) of the proposed system model with implementing maximal ratio combining (MRC) and selection combination (SC) technique at the receiver is presented and analyzed.

8p viling 11-10-2024 5 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 2 1   Download

This paper introduces the application of artificial intelligence to build a security control software system in local military units. This software system uses state-of-the-art convolutional neural networks (CNN SOTA) for facial recognition by testing two of the best facial recognition models currently available: the FaceNet model and the VGGFace model.

8p vifilm 11-10-2024 6 1   Download

The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

7p viengfa 28-10-2024 3 2   Download

This paper is structured as follows. The following section presents related work. Section 3 summarizes the characteristics of the two datasets utilized in the model and the system’s overall architecture for image-based disease diagnosis. Section 4 provides our experimental results that compare the performance metrics with other studies.

6p viengfa 28-10-2024 3 2   Download

This study proposes to test a combination model between CNN network and XGBoost algorithm for weather image classification problem. The proposed model uses deep learning network, namely CNN for feature extraction, then feeds the features into the XGBoost classifier to recognize the images.

6p viengfa 28-10-2024 1 1   Download

Accurate daily load forecasting is critical for effective energy management planning. In this study, the article proposes a new method for daily load forecasting that takes advantage of load data and weather data over time in Tien Giang.

10p viengfa 28-10-2024 1 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 3 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 5 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 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

The results of this study would be useful in quickly and accurately predicting CPI to the management agencies, investors, construction contractors to pre-plan the construction investment costs. This will also help in suitably adjusting changing construction cost with time.

11p viengfa 28-10-2024 2 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 4 2   Download