Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2020 ◽  
pp. 1524-1546
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Presentation Method ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2018 ◽  
Vol 29 (3) ◽  
pp. 67-88 ◽  
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Different Types ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

A hybrid deep learning model for efficient intrusion detection in big data environment

2020 ◽  
Vol 513 ◽  
pp. 386-396 ◽  
Author(s):  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Ahmed Alsanad ◽  
Majed Alrubaian ◽  
Giancarlo Fortino
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Data Environment ◽  
Deep Learning Model

scholarly journals In Machines We Trust: Are Robo-Advisers More Trustworthy Than Human Financial Advisers?

2019 ◽  
pp. 129-141 ◽  
Author(s):  
Hui Xian Chia
Keyword(s):  
Deep Learning ◽  
Black Box ◽  
Best Interests ◽  
Decision Making Process ◽  
Learning Technology ◽  
Technical Expertise ◽  
Learning Agents ◽  
The People ◽  
Self Interest ◽  
Deep Learning Model

This article examines the use of artificial intelligence (AI) and deep learning, specifically, to create financial robo-advisers. These machines have the potential to be perfectly honest fiduciaries, acting in their client’s best interests without conflicting self-interest or greed, unlike their human counterparts. However, the application of AI technology to create financial robo-advisers is not without risk. This article will focus on the unique risks posed by deep learning technology. One of the main fears regarding deep learning is that it is a “black box”, its decision-making process is opaque and not open to scrutiny even by the people who developed it. This poses a significant challenge to financial regulators, whom would not be able to examine the underlying rationale and rules of the robo-adviser to determine its safety for public use. The rise of deep learning has been met with calls for ‘explainability’ of how deep learning agents make their decisions. This paper argues that greater explainability can be achieved by describing the ‘personality’ of deep learning robo-advisers, and further proposes a framework for describing the parameters of the deep learning model using concepts that can be readily understood by people without technical expertise. This regards whether the robo-adviser is ‘greedy’, ‘selfish’ or ‘prudent’. Greater understanding will enable regulators and consumers to better judge the safety and suitability of deep learning financial robo-advisers.

scholarly journals A High-Order CFS Algorithm for Clustering Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fanyu Bu ◽  
Zhikui Chen ◽  
Peng Li ◽  
Tong Tang ◽  
Ying Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Learning Model ◽  
Heterogeneous Data ◽  
High Order ◽  
Tensor Model ◽  
Industrial Internet ◽  
Deep Learning Model

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.

Deep Learning for Moving Object Detection and Tracking

2021 ◽  
pp. 136-163
Author(s):  
Kalirajan K. ◽  
Seethalakshmi V. ◽  
Venugopal D. ◽  
Balaji K.
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Model ◽  
Moving Object Detection ◽  
Moving Object ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
High Level ◽  
Human Faces ◽  
Deep Learning Model

Moving object detection and tracking is the process of identifying and locating the class objects such as people, vehicle, toy, and human faces in the video sequences more precisely without background disturbances. It is the first and foremost step in any kind of video analytics applications, and it is greatly influencing the high-level abstractions such as classification and tracking. Traditional methods are easily affected by the background disturbances and achieve poor results. With the advent of deep learning, it is possible to improve the results with high level features. The deep learning model helps to get more useful insights about the events in the real world. This chapter introduces the deep convolutional neural network and reviews the deep learning models used for moving object detection. This chapter also discusses the parameters involved and metrics used to assess the performance of moving object detection in deep learning model. Finally, the chapter is concluded with possible recommendations for the benefit of research community.

scholarly journals Content-Based Image Retrieval : A Comprehensive Study

2019 ◽  
pp. 1073-1081
Author(s):  
Er Aman ◽  
Amit Rawat ◽  
Ashwin Giri ◽  
Hardik Gothwal
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Empirical Studies ◽  
Content Based Image Retrieval ◽  
Recent Success ◽  
Long Run ◽  
Learning Techniques ◽  
Depth Analysis ◽  
High Level ◽  
Learning Options

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.

Sign in / Sign up

Export Citation Format

Share Document