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Convolutional neural networks combined
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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
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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
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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
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This paper analyses the combination of IMU sensors and electromyography sensors (EMG) to improve the identification accuracy of human movements. We propose the hybrid convolutional neural network (CNN) and long short-term memory neuron network (LSTM) for the human gait analysis problem to achieve an accuracy of 0.9418, better than other models including pure CNN models.
18p
vimulcahy
18-09-2023
8
4
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In this paper, a model combining recurrent neural networks and convolutional neural networks is proposed to build a model that can simultaneously estimate missing values and classify time series data. The experimental results demonstrate that the proposed model performs better than the existing methods for time series classification with incomplete data.
16p
viengels
25-08-2023
5
5
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This approach utilizes the You Only Look Once (YOLO) v3 algorithm combined with a Convolutional Neural Network (CNN) to early detect and assess fire rick for indoors alert. The results showed that the proposed solution is promising for decision making and early handling fire beforehand to ensure the safety inside buildings.
7p
viengels
25-08-2023
5
4
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Recent years have witnessed the strong growth of Automatic Speech Recognition (ASR) studies due to its wide range of applications. However, there are few efforts put into the Vietnamese language. This paper introduces an end-to-end approach using Conformer, a combination of Transfomer and Convolution Neural Network, and pseudo labeling for Vietnamese ASR systems.
7p
viberkshire
09-08-2023
6
6
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Convolutional Neural Network (CNN) can detect images from cameras installed on self-driving cars. First, we drove a car on a simulator and recorded frames from three cameras: left, right, and center. These frames were recorded at the rate of 30 frames per second. Additional data recorded were the distribution of steering angles, average velocity, etc. were passed through a CNN to train a self-driving system.
7p
vifalcon
18-05-2023
7
5
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This article proposes a multi-scale deep learning network to classify different underwater acoustic signal sources. The proposed network is cleverly designed with multiple branches, creating a multi-scale block which allows learning various spatial features of Constant-Q Transform spectrograms.
16p
viargus
20-02-2023
3
2
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This model experimented on collector leaves data set Flavia leaf data set and the Swedish leaf data set. The classication results indicate that the proposed CNN model is effective for leaf recognition with the best accuracy greater than 98.22%.
12p
redemption
20-12-2021
11
0
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The main aim of this work is to detect and track human activity, and classify actions for two publicly available video databases. In this work, a novel approach of feature extraction from video sequence by combining Scale Invariant Feature Transform and optical flow computation are used where shape, gradient and orientation features are also incorporated for robust feature formulation.
27p
spiritedaway36
28-11-2021
10
1
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This paper experiments with the deep learning model convolution neural network (CNN), long short-term memory (LSTM), and the combined model of CNN and LSTM. The training data set comprise reviews of cars in Vietnamese that are pre-processed according to the method of aspect analysis based on an ontology of semantic and sentimental approaches.
7p
viaespa2711
31-07-2021
17
1
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This paper proposes a hybrid approach combining two emerging convolutional neural networks: Faster R-CNN and YOLOv2 to detect drones in images. Experimental results show that the approach can add up to almost 5% and more than 11% to precision and recall for Faster R-CNN and add up to 3% and more than 6% to these two metrics for YOLOv2.
7p
nguaconbaynhay12
13-06-2021
26
1
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This paper developed a novel solution of the change detection based on a combination of the Neighbor-based Ratio operator and the Convolutional Wavelet Neural Network algorithm for improving the accuracy of change detection in multi-temporal SAR images.
8p
nguaconbaynhay12
13-06-2021
34
1
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This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification.
7p
chauchaungayxua12
11-05-2021
18
4
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Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks
Benefiting from big data, powerful computation and new algorithmic techniques, we have been witnessing the renaissance of deep learning, particularly the combination of natural language processing (NLP) and deep neural networks.
12p
vicoachella2711
27-10-2020
16
1
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Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy.
13p
tamynhan4
06-09-2020
14
3
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Keyword spotting (KWS) is one of the important systems on speech applications, such as data mining, call routing, call center, customer-controlled smartphone, smart home systems with voice control, etc. With the goals of researching some factors affecting the Vietnamese Keyword spotting system, we study the combination architecture of CNN (Convolutional Neural Networks)-RNN (Recurrent Neural Networks) on both clean and noise environments with 2 distance speaker cases: 1m and 2m.
11p
kethamoi6
01-07-2020
28
2
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