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Extract deep features
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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
vithomson
02-07-2024
0
0
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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
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A deep learning approach combining autoencoder with supervised classifiers for IoT anomaly detection
Anomaly detection for Internet of things (IoT) networks is a challenging issue due to the huge number of devices that connect to each other and generate huge amounts of data. In this study, we propose a model combining Autoencoder (AE) with classification algorithms to build an endto-end architecture for processing, feature extraction and data classification.
13p
viambani
18-06-2024
7
1
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In this work, an efficient and accurate face recognition system based on edge processing using GPUs was completely developed. A complete pipeline that contains a sequence of processing steps, including pre-processing, face feature extraction, and matching, is proposed.
8p
vibego
02-02-2024
3
0
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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
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In this paper, we proposed a new method to predict lncRNA-disease associations using multiple features and deep learning. Our method uses a weighted
10p
visystrom
22-11-2023
6
5
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This paper proposes a framework for deploying a voice search engine that uses Whisper, a deep learning-based automatic speech recognition model, and combines TF-IDF, N-gram, and Truncated SVD as feature extraction approaches to search for text ground truth in a dictionary of military symbols using Cosine similarity.
10p
viwarmachine
01-07-2023
9
4
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This paper proposes an efficient deep learning-based vehicle speed estimation on highway lanes in the Vietnam transport system. The input videos are recorded by fixed surveillance cameras. An optimized single shot multibox detector network, called SSD, is utilized for vehicle license plate detection (LPD).
11p
viargus
20-02-2023
4
1
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In this paper, we propose to use Conformal Geometric Algebra (CGA) to feature extraction and reduce dimensions of the data. First, the action data is preprocessed to normalize the data. Next, use CGA to reduce dimensions of data and create feature vectors. Finally, use the LSTM for training and prediction. The experiment was conducted on the CMU dataset with 8 different actions and the results showed that the proposed method has higher results than the previous methods.
7p
visherylsandberg
18-05-2022
17
3
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Colonoscopy image classification is an image classification task that predicts whether colonoscopy images contain polyps or not. It is an important task input for an automatic polyp detection system. Recently, deep neural networks have been widely used for colonoscopy image classification due to the automatic feature extraction with high accuracy.
11p
vikissinger
03-03-2022
11
1
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We present the method of the HCMUS team pariticipating in Multimodal Person Discovery in Broadcast TV Task at the MediaEval Challenge 2016. There are two main tasks in our method. First we identify a list of potential characters of interest from all video clips. Each potential character is defined as a pair of face track, a sequence of face patches, and a name. We use OCR results and face detection to find potential characters.
4p
viclerkmaxwel
16-02-2022
21
5
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Classification of rocks is one of the basic parts of geological research and is a difficult task due to the heterogeneous properties of rocks. This process is time consuming and requires sufficiently knowledgeable and experienced specialists in the field of petrography. This paper has a novelty in classifying plutonic rock types for the first time using thin section images; and proposes an approach that uses the deep learning method for automatic classification of 12 types of plutonic rocks.
10p
tanmocphong
29-01-2022
10
1
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The novelty of our approach relies on a proper flow and fusion of information (DGHNL structure and its optimization). We show that the proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% (54 errors per 1000 classifications) for the Statlog German credit approval data.
18p
guernsey
28-12-2021
8
1
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In one of the experiments, the extracted features have been feed into a fully connected network which detects violence in frame level. Moreover, in another experiment, we have fed the extracted features of 30 frames to a long short-term memory (LSTM) network at a time.
22p
redemption
20-12-2021
8
1
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This paper proposes a method for improving the feature set of Android malware classification based on co-concurrence matrix (co-matrix). The co-matrix is established based on a list of raw features extracted from .apk files. The proposed feature can take the advantage of CNN while remaining important features of the Android malwares. Experimental results of CNN model conducted on a very popular Android malware dataset, Drebin, prove the feasibility of our proposed co-matrix feature.
8p
spiritedaway36
28-11-2021
12
1
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In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories.
20p
spiritedaway36
28-11-2021
18
1
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To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder.
18p
spiritedaway36
28-11-2021
11
0
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The pretrained BERT multilingual model is used to generate embedding vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results compared to existing works using the same dataset.
21p
spiritedaway36
25-11-2021
7
1
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This project proposes two new methods of deep neural networks and handcrafted features for damage detection. The first method uses a convolution neural network (CNN) to extract deep features in time series and Long Short Term Memory (LSTM) network to find a statistically significant correlation of each lagged feature in time series data.
6p
billyelliot
11-11-2021
27
1
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With the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.
8p
vitzuyu2711
29-09-2021
9
1
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