Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model

Attention is a deep learning mechanism which has been proved very helpful in the field of artificial intelligence and solving various AI problems, in order to bend the various intelligent tasks positively in the direction to its actual goal i.e AI. In this paper, I have used Attention Model to perform the task of sentiment analysis in any news article. After extracting the news article from a scraper and preprocessing the data, it will be fed into a sentiment analyser which will predict the sentiment of the news article at sentence and document level.

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Visual information retrieval using deep learning with visual attention model

International Journal of Latest Trends in Engineering and Technology ◽

10.21172/1.73.040 ◽

2016 ◽

Vol 7 (3) ◽

Keyword(s):

Information Retrieval ◽

Deep Learning ◽

Visual Attention ◽

Visual Information ◽

Visual Information Retrieval ◽

Visual Attention Model ◽

Attention Model

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Multi-Aspect Attention Model for Aspect-based Sentiment Classification Using Deep Learning

2020 International Conference on Advanced Information Technologies (ICAIT) ◽

10.1109/icait51105.2020.9261803 ◽

2020 ◽

Author(s):

Win Lei Kay Khine ◽

Nyein Thwet Thwet Aung

Keyword(s):

Deep Learning ◽

Sentiment Classification ◽

Attention Model

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Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2016/7942501 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Na Li ◽

Xinbo Zhao ◽

Yongjia Yang ◽

Xiaochun Zou

Keyword(s):

Neural Network ◽

Deep Learning ◽

Visual Attention ◽

Convolutional Neural Network ◽

Visual Information ◽

Local Features ◽

Classification Method ◽

Semantic Features ◽

Visual Attention Model ◽

Attention Model

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

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Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

Journal of Advanced Transportation ◽

10.1155/2021/9928073 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Chuanxiang Ren ◽

Chunxu Chai ◽

Changchang Yin ◽

Haowei Ji ◽

Xuezhen Cheng ◽

...

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Traffic Management ◽

Prediction Accuracy ◽

Function Optimization ◽

Short Term ◽

Learning Methods ◽

Attention Model ◽

Traffic Flow Prediction ◽

Flow Prediction

Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

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A Mixed Deep Learning Based Model to Early Detection of Depression

Journal of Web Engineering ◽

10.13052/jwe1540-9589.19344 ◽

2020 ◽

Author(s):

Boumahdi Fatima ◽

Madani Amina ◽

Rezoug Nachida ◽

Hentabli Hamza

Keyword(s):

Deep Learning ◽

Extraction Process ◽

Word Embeddings ◽

Learning Models ◽

Analysis Model ◽

Test Set ◽

Attention Model ◽

Social Media Platforms ◽

Deep Learning Model ◽

Detection Of Depression

Mental health is considered as one of today’s world’s most prominent plagues. Therefore, our work aims to use the potential of social media platforms to solve one of mental health’s biggest issues, which is depression identification. We propose a new deep learning model that we train on a depression-dedicated dataset in order to detect such mental illness from an individual’s posts. Our main contributions lie in the three following points: (1) We trained our own word embeddings using a depression-dedicated dataset. (2) We combined a Convolutional Neural Networks model with the Message-level Sentiment Analysis model in order to improve the feature extraction process and enhance the model’s performance. (3) We analyzed through different experiments the performance of three deep learning models in order to provide more perspectives and insights for depression researches. A total of four classifier models were deployed with the same dataset. Those implementing CNN-BiLSTM with Attention model attained greater overall Accuracy, Recall, Precision and F1 macro scores of 0.97, 0.95, 0.84 and 0.92 on the final assessment test set, respectively.

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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Complex & Intelligent Systems ◽

10.1007/s40747-020-00192-x ◽

2020 ◽

Author(s):

Gang Xue ◽

Shifeng Liu ◽

Yicao Ma

Keyword(s):

Supply Chain ◽

Deep Learning ◽

Image Classification ◽

Image Recognition ◽

Ground Truth ◽

Experimental Results ◽

Attention Model ◽

Convolutional Networks ◽

Classification Framework ◽

Fruit Sorting

Abstract Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to extract the features of image. In the first part of the framework, an unsupervised method with a set of images is applied to pre-train the greedy layer-wised CAE. We use CAE structure to initialize a set of weights and bias of ADN. In the second part of the framework, the supervised ADN with the ground truth is implemented. The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.

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