![](images/graphics/blank.gif)
Deep convolutional networks
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Longterm Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet.
26p
dianmotminh02
03-05-2024
3
1
Download
-
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
-
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
-
This paper presents an end-to-end deep convolutional recurrent neural network solution for Khmer Optical Character Recognition (OCR) task. The proposed solution uses a sequenceto-sequence (Seq2Seq) architecture with an attention mechanism. The encoder extracts visual features from an input text-line image via layers of convolutional blocks and a layer of Gated Recurrent Units (GRU).
14p
vibego
02-02-2024
5
0
Download
-
This study introduces an approach to detect deepfake images using transfer learning methods, including XceptionNet, RestNet101, InceptionResV2, MobileNetv2, VGG19 and DenseNet121, along with comparing it with a traditional CNN model.
8p
vigrab
02-02-2024
6
3
Download
-
In this paper, we introduce a novel approach to enhance the capabilities of the humanoid robot IVastBot by in- tegrating various software components. This integration enables IVastBot to effectively recognize and respond to a wide array of human gestures and behaviors.
12p
vimichaelfaraday
28-12-2023
10
6
Download
-
Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.
10p
vileonardodavinci
23-12-2023
4
3
Download
-
In this paper, we propose a framework based on very deep convolutional neural network autoencoder for image retrieval, called AIR (Autoencoders for Image Retrieval). Our proposed framework allows to learn feature vectors directly from the raw image and in an unsupervised manner.
8p
visystrom
22-11-2023
6
3
Download
-
In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract.
10p
visystrom
22-11-2023
6
5
Download
-
Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development.
17p
vischultz
20-10-2023
3
1
Download
CHỦ ĐỀ BẠN MUỐN TÌM
![](images/graphics/blank.gif)