Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

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Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach

10.20944/preprints202002.0180.v1 ◽

2020 ◽

Author(s):

Antonios Alexos ◽

Sotirios Chatzis

Keyword(s):

Deep Learning ◽

Real World ◽

Individual Case ◽

Learning Model ◽

Attention Mechanism ◽

Novel Approach ◽

Specific Individual ◽

Real World Datasets ◽

Deep Learning Model

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

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Hybrid Deep Learning Model for Stock Price Prediction

2018 IEEE Symposium Series on Computational Intelligence (SSCI) ◽

10.1109/ssci.2018.8628641 ◽

2018 ◽

Cited By ~ 4

Author(s):

Mohammad Asiful Hossain ◽

Rezaul Karim ◽

Ruppa Thulasiram ◽

Neil D. B. Bruce ◽

Yang Wang

Keyword(s):

Deep Learning ◽

Stock Price ◽

Learning Model ◽

Stock Price Prediction ◽

Price Prediction ◽

Deep Learning Model

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Tensor Deep Learning Model for Heterogeneous Data Fusion in Internet of Things

IEEE Transactions on Emerging Topics in Computational Intelligence ◽

10.1109/tetci.2018.2876568 ◽

2020 ◽

Vol 4 (1) ◽

pp. 32-41 ◽

Cited By ~ 2

Author(s):

Wei Wang ◽

Min Zhang

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Data Fusion ◽

Learning Model ◽

Heterogeneous Data ◽

Deep Learning Model

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Multi-Task Deep Learning Model with an Attention Mechanism for Ship Accident Sentence Prediction

Applied Sciences ◽

10.3390/app12010233 ◽

2021 ◽

Vol 12 (1) ◽

pp. 233

Author(s):

Ho-Min Park ◽

Jae-Hoon Kim

Keyword(s):

Deep Learning ◽

Learning Model ◽

Attention Mechanism ◽

Maritime Safety ◽

Proposed Model ◽

Deep Learning Model

The number of ship accidents occurring in the Korean ocean has been steadily increasing year by year. The Korea Maritime Safety Tribunal (KMST) has published verdicts to ensure that the relevant personnel can share judgment on these accidents. As of 2020, there have been 3156 ship accidents; thus, it is difficult for the relevant personnel to study these various accidents by only reading the verdicts. Therefore, in this study, we propose a multi-task deep learning model with an attention mechanism for predicting the sentencing of ship accidents. The tasks are accident types, applied articles, and the sentencing of ship accidents. The proposed model was tested under verdicts published by the KMST between 2010 and 2019. Through experiments, we show that the proposed model can improve the performance of sentence prediction and can assist the relevant personnel to study these accidents.

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Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

Infectious Diseases of Poverty ◽

10.1186/s40249-021-00912-6 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Zheng Kou ◽

Yi-Fan Huang ◽

Ao Shen ◽

Saeed Kosari ◽

Xiang-Rong Liu ◽

...

Keyword(s):

Deep Learning ◽

General Model ◽

Characteristic Curve ◽

Learning Model ◽

Attention Mechanism ◽

Animal Origin ◽

Coding Region ◽

Species Barrier ◽

Potential Pathogen ◽

Deep Learning Model

Abstract Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

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