Neural network

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).

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This study presents a novel neural network (NN) framework for developing force fields specific to graphene monolayers, utilizing data obtained from first-principles calculations. The authors analyze three primary force components, force magnitude and the cosines of two angles across different configurations of surrounding carbon atoms.

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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.

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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.

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In this study, we propose the application of CycleGAN to generate T2 pulse sequence MRI images of the human brain from T2 Flair pulse sequence images of the same type and vice versa, thereby increasing the number of MRI images of various types.

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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.

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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.

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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.

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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.

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The basic characteristics of sensor were investigated, and these experimental data were used for a machine learning. The results of the model validation proved to be a reliable way between the experiment and prediction values.

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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.

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This paper presents the results of applying the Artificial Neural Network (ANN) model in determining pile bearing capacity. The traditional methods used to calculate the bearing capacity of piles still have many disadvantages that need to be overcome such as high cost, complicated calculation, time-consuming.

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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.

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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.

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This paper investigates the problem of finite-time stability for discrete-time neural networks with sector-bounded neuron activation functions and interval-like timevarying delay. The extended reciprocally convex approach is used to establish a delay-dependent sufficient condition to ensure finite-time stability for this class of systems.

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In this paper, author uses 8-bit fixed-point quantization to greatly reduce the memory space requirement of the feature maps and weights and the accuracy of LeNet-5 with MNIST dataset is only slightly reduced. In the hardware accelerator, author proposes a highly flexible CNN accelerator with reconfigurable layers.

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Bài viết trình bày về cách sử dụng nhiều GPU để huấn luyện mô hình trong học sâu (Deep Learning). Chúng tôi khảo sát các chiến lược học sâu trên mạng nơ-ron tích chập (Convolutional Neural Network – CNN).

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In this paper, we used Convolution neural network (CNN) that exploits the visual properties of the input data to obtain features from network traffic, thereby achieving good intrusion detection performance.

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In this paper, we propose a wavelet type-2 fuzzy brain imitated controller (WT2FBIC) for nonlinear robotic systems. The suggested method combines a wavelet type-2 fuzzy system (WT2FS) and a brain imitated controller (BIC) to improve learning efficiency.

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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.

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