Deep learning Neural network
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This study proposes a new approach for diagnosing pediatric sepsis that utilizes a convolutional neural network and a combination of 7 immune-related genes (IRGs), including CD24, TTK, PRG2, CLEC7A, CCL3, TNFAIP3, and CCRL2. A three-layer gene selection process involves a sequential procedure that combines differential gene expression analysis, selection of immune-related genes, and gene score calculation using the F-score algorithm.
8p vithomson 02-07-2024 0 0 Download
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This research undertakes a comparative analysis of lane detection methodologies, explicitly focusing on traditional image processing techniques and Convolutional Neural Networks (CNNs). The evaluation utilized a sample of 500 images from the CULane dataset, which encompasses a diverse range of traffic scenarios.
14p vithomson 02-07-2024 0 0 Download
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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.
7p vithomson 02-07-2024 0 0 Download
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This research focuses on developing a method to optimize the DCNN (Deep Convolutional Neural Network) classification model for plant diseases. We enriched the data by incorporating data from two public datasets, PlantVillage Dataset (PVD) and CroppedPlant Dataset (CPD), and we trained the model using two-step transfer learning.
6p vithomson 02-07-2024 0 0 Download
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Part 2 of ebook "Introduction to deep learning: From logical calculus to artificial intelligence" provides readers with contents including: Chapter 4 - Feed forward neural networks; Chapter 5 - Modifications and extensions to a feed-forward neural network; Chapter 6 - Convolutional neural networks; Chapter 7 - Recurrent neural networks; Chapter 8 - Autoencoders; Chapter 9 - Neural language models; Chapter 10 - An overview of different neural network architectures; Chapter 11 - Conclusion;...
107p daonhiennhien 03-07-2024 1 1 Download
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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.
8p viwalton 02-07-2024 3 1 Download
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In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed.
18p vikoch 27-06-2024 2 1 Download
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Preoperative diagnosis of flum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved.
11p vikoch 27-06-2024 1 1 Download
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Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians
12p vikoch 27-06-2024 1 1 Download
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This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type.
10p visergeyne 18-06-2024 1 0 Download
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In this article, we propose a generative model based on the adversarial network structure to enhance images for the RAD-DAR multi-target dataset. The results of comparisons and evaluations indicate that the images generated by the proposed method exhibit a high degree of similarity to the original images.
7p visergeyne 18-06-2024 2 0 Download
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This paper presents an RTL (Register Transfer Logic) level microarchitecture of hardware-and bandwidth-efficient high-performance 2D convolution unit for CNN in deep learning. The 2D convolution unit is made up of three main components including a dedicated Loader, a Circle Buffer, and a MAC (Multiplier-Accumulator) unit.
13p viambani 18-06-2024 2 1 Download
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This article introduces an advanced method in the field of facial recognition, using a unique technique that combines Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) to integrate different perspectives.
9p viambani 18-06-2024 5 1 Download
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Text categorization aims to automatically assign given text passages or documents to predetermined categories or subjects. Despite the wide array of techniques employed in classifying English text, there remains a dearth of research on Vietnamese text classification. This paper introduces a novel approach utilizing a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) with a deep network structure for Vietnamese text classification.
10p viambani 18-06-2024 3 1 Download
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The authors propose a novel BCMO-DNN algorithm for vibration optimization of functionally graded porous microplates. The theory is based on a unified framework of higher-order shear deformation theory and modified strain gradient theory. A hybrid combination of deep learning neural network and balancing composite motion optimization is developed to solve the optimization problems and predict stochastic vibration behaviors of functionally graded porous microplates with uncertainties of material properties.
12p dathienlang1012 03-05-2024 3 0 Download
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Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications.
12p vibransone 28-03-2024 5 1 Download
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Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models.
16p vibransone 28-03-2024 4 2 Download
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Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer.
17p vibransone 28-03-2024 3 2 Download
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The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up.
17p viellison 28-03-2024 2 2 Download
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In this paper, a damage detection methodology for steel frame structures under fire load using time-history acceleration and machine learning (ML) is proposed. A randomly created dataset by finite element analysis (FEA) is utilized to develop deep neural networks (DNNs).
5p vicwell 06-03-2024 7 3 Download