Special Issue "Recent Advances in Social Data and Artificial Intelligence 2019"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineering Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Prof. Dr. Hari Mohan Srivastava
grade E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
Interests: real and complex analysis; fractional calculus and its applications; integral equations and transforms; higher transcendental functions and their applications; q-series and q-polynomials; analytic number theory; analytic and geometric Inequalities; probability and statistics; inventory modelling and optimization
Special Issues and Collections in MDPI journals
Prof. Dr. Gautam Srivastava
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
Interests: blockchain technology; cryptography; big data; data mining; social networks; security and privacy; anonymity; graphs
Special Issues and Collections in MDPI journals
Prof. Vijay Mago
E-Mail Website
Guest Editor
Lakehead University, Canada
Interests: Social data analysis; Artificial Intelligence; Big data; Health Informatics; Medical decision making

Special Issue Information

Dear Colleagues,

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized.

In this Special Issue, we cordially invite and welcome review, expository, and original research articles dealing with the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace.

Cyberspace, the seamless integration of physical, social, and mental spaces, is an integral part of our society, ranging from learning and entertainment to business and cultural activities, and so on. However, there are a number of pressing challenges associated with cyberspace. For example, how do we strike a balance between the need for strong cybersecurity and preserving the privacy of ordinary citizens?

This Special Issue has emerged from the International Conference on Social Data and Artificial Intelligence (SDAI 2020) held in Toronto, Canada on 26–27 May 2020 and the IEEE Cyber Science and Technology Congress (CyberSciTech 2020) which will also be held in Canada (CyberSciTech 2020, Calgary, Canada, 22–26 June 2020).

To address the challenges described for both conferences, there is a need to establish new science and research portfolios that incorporate social data and artificial intelligence alone or in combination with cyber-physical, cyber-social, cyber-intelligent, and cyber-life technologies in a cohesive and efficient manner.

Prof. H. M. Srivastava
Prof. Gautam Srivastava
Prof. Vijay Mago
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Social data inadequacies and inconsistencies
  • Predictive models of social behaviors
  • Infrastructure and architecture for testing social theories
  • Data collection and analysis platforms
  • Relevance of IoT for social science theories
  • Building capacity to continuously collect data across a range of social media networks
  • Designing efficient parsers to deal with noisy social media data-sets for real-time tracking of health issues, diseases, and wellness
  • Designing tools to map and measure the effectiveness of health campaigns by healthcare organizations
  • Cross-validating the predictive models of social media data-sets with ground truth data
  • Developing frameworks and algorithms to perform real-time analysis of social media data-sets
  • Cyberspace theory and technology
  • Cyber social computing and networks
  • Cyber life and wellbeing
  • Cyber intelligence and cognitive science

Published Papers (27 papers)

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Article
Color Revolution: A Novel Operator for Imperialist Competitive Algorithm in Solving Cloud Computing Service Composition Problem
Symmetry 2021, 13(2), 177; https://doi.org/10.3390/sym13020177 - 22 Jan 2021
Viewed by 393
Abstract
In this paper, a novel high-performance and low-cost operator is proposed for the imperialist competitive algorithm (ICA). The operator, inspired by a sociopolitical movement called the color revolution that has recently arisen in some countries, is referred to as the color revolution operator [...] Read more.
In this paper, a novel high-performance and low-cost operator is proposed for the imperialist competitive algorithm (ICA). The operator, inspired by a sociopolitical movement called the color revolution that has recently arisen in some countries, is referred to as the color revolution operator (CRO). The improved ICA with CRO, denoted as ICACRO, is significantly more efficient than the ICA. On the other hand, cloud computing service composition is a high-dimensional optimization problem that has become more prominent in recent years due to the unprecedented increase in both the number of services in the service pool and the number of service providers. In this study, two different types of ICACRO, one that applies the CRO to all countries of the world (ICACRO-C) and one that applies the CRO solely to imperialist countries (ICACRO-I), were used for service time-cost optimization in cloud computing service composition. The ICACRO was evaluated using a large-scale dataset and five service time-cost optimization problems with different difficulty levels. Compared to the basic ICA and niching PSO, the experimental and statistical tests demonstrate that the ability of the ICACRO to approach an optimal solution is considerably higher and that the ICACRO can be considered an efficient and scalable approach. Furthermore, the ICACRO-C is stronger than the ICACRO-I in terms of the solution quality with respect to execution time. However, the differences are negligible when solving large-scale problems. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Overlapping Community Discovery Method Based on Two Expansions of Seeds
Symmetry 2021, 13(1), 18; https://doi.org/10.3390/sym13010018 - 24 Dec 2020
Cited by 1 | Viewed by 533
Abstract
The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, [...] Read more.
The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Symmetrical Model of Smart Healthcare Data Management: A Cybernetics Perspective
Symmetry 2020, 12(12), 2089; https://doi.org/10.3390/sym12122089 - 16 Dec 2020
Viewed by 619
Abstract
Issues such as maintaining the security and integrity of data in digital healthcare are growing day-by-day in terms of size and cost. The healthcare industry needs to work on effective mechanisms to manage these concerns and prevent any debilitating crisis that might affect [...] Read more.
Issues such as maintaining the security and integrity of data in digital healthcare are growing day-by-day in terms of size and cost. The healthcare industry needs to work on effective mechanisms to manage these concerns and prevent any debilitating crisis that might affect patients as well as the overall health management. To tackle such critical issues in a simple, feasible, and symmetrical manner, the authors considered the ideology of cybernetics. Working towards this intent, this paper proposes a symmetrical model that illustrates a compact version of the adopted ideology as a pathway for future researchers. Furthermore, the proposed ideology of cybernetics specifically focuses on how to plan the entire design concept more effectively. It is important for the designer to prepare for the future and manage the design structure from a product perspective. Therefore, the proposed ideology provides a symmetric mechanism that includes a variety of estimation and evaluation techniques as well as their management. The proposed model generates a symmetric, variety-issue, reduced infrastructure that can produce highly effective results due to an efficient usability, operatability, and symmetric operation execution which are the benefits of the proposed model. Furthermore, the study also performed a performance simulation assessment by adopting a multi-criteria decision-making approach that helped the authors compare the various existing and proposed models based on their levels of effectiveness. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm
Symmetry 2020, 12(12), 2025; https://doi.org/10.3390/sym12122025 - 07 Dec 2020
Cited by 1 | Viewed by 945
Abstract
In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into [...] Read more.
In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions
Symmetry 2020, 12(11), 1930; https://doi.org/10.3390/sym12111930 - 23 Nov 2020
Cited by 1 | Viewed by 558
Abstract
Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is [...] Read more.
Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners
Symmetry 2020, 12(11), 1798; https://doi.org/10.3390/sym12111798 - 30 Oct 2020
Cited by 8 | Viewed by 960
Abstract
Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) [...] Read more.
Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
User Behavior on Online Social Networks: Relationships among Social Activities and Satisfaction
Symmetry 2020, 12(10), 1656; https://doi.org/10.3390/sym12101656 - 10 Oct 2020
Cited by 1 | Viewed by 1301
Abstract
Social networking sites (SNSs) are now ubiquitous communities for constant online interpersonal interactions that trigger symmetric or asymmetric effects on our everyday life. Recent studies advocate in favor of the significant role that SNSs have in promoting well-being and, more importantly, in disseminating [...] Read more.
Social networking sites (SNSs) are now ubiquitous communities for constant online interpersonal interactions that trigger symmetric or asymmetric effects on our everyday life. Recent studies advocate in favor of the significant role that SNSs have in promoting well-being and, more importantly, in disseminating reliable information during a global crisis, such as the current COVID-19 pandemic. Based on the growing importance of SNSs to the global framework, the main purpose of this study is to empirically assess the link between the use of symmetric social networks such as Facebook, or asymmetric social networks, like Instagram, and the level of satisfaction, employing the methodology of structural equation modeling. The results of the research validate the hypothesis that SNS activities increase the level of satisfaction, and therefore, that there is a direct link between the number of posts and comments and the level of satisfaction. Furthermore, based on the reversible and significant link between the level of satisfaction and the importance attributed to SNSs, the main conclusion of the study is that the higher the importance of the SNS, the greater the level of dissatisfaction experienced by users. Also, public activities on social networks positively affect social network satisfaction, while private activities have a direct negative relationship with the importance of social networks. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Applied Identification of Industry Data Science Using an Advanced Multi-Componential Discretization Model
Symmetry 2020, 12(10), 1620; https://doi.org/10.3390/sym12101620 - 30 Sep 2020
Cited by 1 | Viewed by 558
Abstract
Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, [...] Read more.
Applied human large-scale data are collected from heterogeneous science or industry databases for the purposes of achieving data utilization in complex application environments, such as in financial applications. This has posed great opportunities and challenges to all kinds of scientific data researchers. Thus, finding an intelligent hybrid model that solves financial application problems of the stock market is an important issue for financial analysts. In practice, classification applications that focus on the earnings per share (EPS) with financial ratios from an industry database often demonstrate that the data meet the abovementioned standards and have particularly high application value. This study proposes several advanced multicomponential discretization models, named Models A–E, where each model identifies and presents a positive/negative diagnosis based on the experiences of the latest financial statements from six different industries. The varied components of the model test performance measurements comparatively by using data-preprocessing, data-discretization, feature-selection, two data split methods, machine learning, rule-based decision tree knowledge, time-lag effects, different times of running experiments, and two different class types. The experimental dataset had 24 condition features and a decision feature EPS that was used to classify the data into two and three classes for comparison. Empirically, the analytical results of this study showed that three main determinants were identified: total asset growth rate, operating income per share, and times interest earned. The core components of the following techniques are as follows: data-discretization and feature-selection, with some noted classifiers that had significantly better accuracy. Total solution results demonstrated the following key points: (1) The highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning with a percentage-split method for two classes in one run; (2) the highest accuracy mean, 91.44%, occurred in Models D and E from the use of naïve Bayes learning for cross-validation and percentage-split methods for each class for 10 runs; (3) the highest average accuracy mean, 87.53%, occurred in Models D and E with a cross-validation method for each class; (4) the highest accuracy, 92.46%, occurred in Model C from the use of decision tree learning-C4.5 with the percentage-split method and no time-lag for each class. This study concludes that its contribution is regarded as managerial implication and technical direction for practical finance in which a multicomponential discretization model has limited use and is rarely seen as applied by scientific industry data due to various restrictions. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Telemedicine Acceptance during the COVID-19 Pandemic: An Empirical Example of Robust Consistent Partial Least Squares Path Modeling
Symmetry 2020, 12(10), 1593; https://doi.org/10.3390/sym12101593 - 25 Sep 2020
Cited by 5 | Viewed by 1271
Abstract
The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which [...] Read more.
The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which model, the Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory power for the adoption of telemedicine addressing outlier-associated bias. We carried out an online survey of patients. The data obtained through the survey were analyzed using both consistent partial least squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed for elliptically symmetric unimodal distribution. Both estimation techniques led to similar results, without inconsistencies in interpretation. In short, the results indicate that the Theory of Planned Behavior Model provides a significant explanatory power. Furthermore, the findings show that attitude has the most substantial direct effect on behavioral intention to use telemedicine systems. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Tele-Education under the COVID-19 Crisis: Asymmetries in Romanian Education
Symmetry 2020, 12(9), 1502; https://doi.org/10.3390/sym12091502 - 12 Sep 2020
Cited by 1 | Viewed by 1563
Abstract
The COVID-19 pandemic has deepened social and educational asymmetries in some developing countries, such as Romania. Tele-education failed to replace face-to-face education due to the lack of symmetrical policy, connectivity, infrastructure, digitalized educational materials and digital competences. Was this issue predictable and, hence, [...] Read more.
The COVID-19 pandemic has deepened social and educational asymmetries in some developing countries, such as Romania. Tele-education failed to replace face-to-face education due to the lack of symmetrical policy, connectivity, infrastructure, digitalized educational materials and digital competences. Was this issue predictable and, hence, the stakeholders’ mission failed? Our qualitative research aims at analyzing, in depth, these digitalization asymmetries, with a sample formed of Information and Communication Technology (ICT) specialists working for/with Romanian 1–4 International Standard Classification of Education (ISCED) schools. The collected primary data were processed with Atlas.ti 8. The results emphasize major key areas to be addressed with future public symmetrical policy and change management strategies: equal access to infrastructure, as well as development of compulsory and complementary digital skills (for teachers and students). The necessity to support school management in accessing funding is also required to enhance digitalization. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach
Symmetry 2020, 12(9), 1381; https://doi.org/10.3390/sym12091381 - 19 Aug 2020
Cited by 1 | Viewed by 916
Abstract
In the Industry 4.0 scenario, innovation emerges as a clear driver for the economic development of societies. This effect is particularly true for the least developed countries. Nevertheless, there is a lack of studies that analyze this phenomenon in these nations. In this [...] Read more.
In the Industry 4.0 scenario, innovation emerges as a clear driver for the economic development of societies. This effect is particularly true for the least developed countries. Nevertheless, there is a lack of studies that analyze this phenomenon in these nations. In this context, this study aims to examine the impact of perceived barriers to innovation to predict companies′ innovative intentions in an emerging economy. This study is a preliminary effort to use data mining and symmetry-based learning concepts, especially classification, to assist the identification of strategies to incentivize intention to innovate in companies. Using the decision tree classification technique, we analyzed a sample of Chilean companies (N = 5876). The sample was divided into large enterprises (LEs) and small and medium enterprises (SMEs). In the group of large companies, the barriers that most impact the intention to innovate are innovation cost, lack of demand innovations, and lack of qualified personnel. Alternatively, in the group of small-medium companies, the barriers that most impact the intention to innovate are lack of own funds, lack of demand innovations, and lack of information about technology. These results show how the perceptions of barriers are significant to predict the intentions of innovation in Chilean companies. Furthermore, the perceptions of these barriers are contingent on the organizational sizes. These findings contribute to understanding the effect of contingencies on innovative intention in an emerging economy. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Blockchain Paradigm for Healthcare: Performance Evaluation
Symmetry 2020, 12(8), 1200; https://doi.org/10.3390/sym12081200 - 22 Jul 2020
Cited by 10 | Viewed by 1247
Abstract
Electronic health records (EHRs) have become a popular method to store and manage patients’ data in hospitals. Sharing these records makes the current healthcare data management system more accurate and cost-efficient. Currently, EHRs are stored using the client/server architecture by which each hospital [...] Read more.
Electronic health records (EHRs) have become a popular method to store and manage patients’ data in hospitals. Sharing these records makes the current healthcare data management system more accurate and cost-efficient. Currently, EHRs are stored using the client/server architecture by which each hospital retains the stewardship of the patients’ data. The records of a patient are scattered among different hospitals using heterogeneous database servers. These limitations constitute a burden towards a personalized healthcare, when it comes to offering a cohesive view and a shared, secure and private access to patients’ health history for multiple allied professionals and the patients. The data availability, privacy and security characteristics of the blockchain have a propitious future in the healthcare presenting solutions to the complexity, confidentiality, integrity, interoperability and privacy issues of the current client/server architecture-based EHR management system. This paper analyzes and compares the performance of the blockchain and the client/server paradigms. The results reveal that notable performance can be achieved using blockchain in a patient-centric approach. In addition, the immutable and valid patients’ data in the blockchain can aid allied health professionals in better prognosis and diagnosis support through machine learning and artificial intelligence. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
A Photo Post Recommendation System Based on Topic Model for Improving Facebook Fan Page Engagement
Symmetry 2020, 12(7), 1105; https://doi.org/10.3390/sym12071105 - 02 Jul 2020
Cited by 3 | Viewed by 942
Abstract
Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the [...] Read more.
Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the Facebook photo post needs to be analyzed effectively so that the social media companies and managers can concentrate on handling their fan pages. This research aimed to use text mining techniques to find the audience accurately. Therefore, we built a topic model recommendation system (TMRS) to analyze Facebook posts by sorting the target posts according to the recommended scores. The TMRS includes six stages, such as data preprocessing, Chinese word segmentation, word refinement, TF-IDF word vector conversion, creating model via Latent Semantic Indexing (LSI), or Latent Dirichlet Allocation (LDA), and calculating the recommendation score. In addition to automatically selecting posts to create advertisements, this model is more effective in using marketing budgets and getting more engagements. Based on the recommendation results, it is verified that the TMRS can increase the engagement rate compared to the traditional engagement rate recommended method (ERRM). Ultimately, advertisers can have the chance to create ads for the post with potentially high engagements under a limited budget. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Learning Context-Aware Outfit Recommendation
Symmetry 2020, 12(6), 873; https://doi.org/10.3390/sym12060873 - 26 May 2020
Cited by 1 | Viewed by 954
Abstract
With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, [...] Read more.
With the rapid development and increasing popularity of online shopping for fashion products, fashion recommendation plays an important role in daily online shopping scenes. Fashion is not only a commodity that is bought and sold but is also a visual language of sign, a nonverbal communication medium that exists between the wearers and viewers in a community. The key to fashion recommendation is to capture the semantics behind customers’ fit feedback as well as fashion visual style. Existing methods have been developed with the item similarity demonstrated by user interactions like ratings and purchases. By identifying user interests, it is efficient to deliver marketing messages to the right customers. Since the style of clothing contains rich visual information such as color and shape, and the shape has symmetrical structure and asymmetrical structure, and users with different backgrounds have different feelings on clothes, therefore affecting their way of dress. In this paper, we propose a new method to model user preference jointly with user review information and image region-level features to make more accurate recommendations. Specifically, the proposed method is based on scene images to learn the compatibility from fashion or interior design images. Extensive experiments have been conducted on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Extensive experiments indicate that the proposed method effectively improves the performance of items prediction as well as of outfits matching. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks
Symmetry 2020, 12(5), 843; https://doi.org/10.3390/sym12050843 - 21 May 2020
Cited by 1 | Viewed by 1070
Abstract
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information [...] Read more.
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
A New LSB Attack on Special-Structured RSA Primes
Symmetry 2020, 12(5), 838; https://doi.org/10.3390/sym12050838 - 20 May 2020
Cited by 1 | Viewed by 936
Abstract
Asymmetric key cryptosystem is a vital element in securing our communication in cyberspace. It encrypts our transmitting data and authenticates the originality and integrity of the data. The Rivest–Shamir–Adleman (RSA) cryptosystem is highly regarded as one of the most deployed public-key cryptosystem today. [...] Read more.
Asymmetric key cryptosystem is a vital element in securing our communication in cyberspace. It encrypts our transmitting data and authenticates the originality and integrity of the data. The Rivest–Shamir–Adleman (RSA) cryptosystem is highly regarded as one of the most deployed public-key cryptosystem today. Previous attacks on the cryptosystem focus on the effort to weaken the hardness of integer factorization problem, embedded in the RSA modulus, N = p q . The adversary used several assumptions to enable the attacks. For examples, p and q which satisfy Pollard’s weak primes structures and partial knowledge of least significant bits (LSBs) of p and q can cause N to be factored in polynomial time, thus breaking the security of RSA. In this paper, we heavily utilized both assumptions. First, we assume that p and q satisfy specific structures where p = a m + r p and q = b m + r q for a , b are positive integers and m is a positive even number. Second, we assume that the bits of r p and r q are the known LSBs of p and q respectively. In our analysis, we have successfully factored N in polynomial time using both assumptions. We also counted the number of primes that are affected by our attack. Based on the result, it may poses a great danger to the users of RSA if no countermeasure being developed to resist our attack. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel
Symmetry 2020, 12(4), 667; https://doi.org/10.3390/sym12040667 - 23 Apr 2020
Viewed by 1016
Abstract
Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic [...] Read more.
Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Symmetry Analysis in Analyzing Cognitive and Emotional Attitudes for Tourism Consumers by Applying Artificial Intelligence Python Technology
Symmetry 2020, 12(4), 606; https://doi.org/10.3390/sym12040606 - 11 Apr 2020
Cited by 2 | Viewed by 1124
Abstract
Symmetries play very important roles in the analysis of cognitive and emotional attitudes. The analysis with Python technology, including optimized artificial intelligence technology, is designed on the basis of symmetry principles. Destination image perception as a branch of destination image research is of [...] Read more.
Symmetries play very important roles in the analysis of cognitive and emotional attitudes. The analysis with Python technology, including optimized artificial intelligence technology, is designed on the basis of symmetry principles. Destination image perception as a branch of destination image research is of great significance to tourists’ decision-making and destination image building. Ice-snow tourism is a hot topic nowadays, and research on perceptions of images of ice-snow tourism has become a focus. In this paper, python programming was used to crawl online travel journals and reviews about Jilin province’s ice-snow tourism on the Internet to analyze the frequency of frequently used words, their classification, word cloud and co-occurrence network, and other aspects of image perception, and proceed to the emotional perception of and emotional attitude to the emotional images and an overall image analysis. The study found that: (1) Perceptions of images of ice-snow tourism can be divided into five categories: tourism attractions, tourism activities, tourism facilities, tourism features and the tourism service environment. The frequency of tourism attractions is the highest, followed by tourism facilities and the tourism service environment. “Changbai Mountain” and “rime” are the core words, that is, tourists are most impressed by the scenic spot and landscape of “Changbai Mountain and rime.” (2) Positive emotional expressions accounted for 67.23% of perceptions of images of ice-snow tourism. Tourists gave a positive evaluation for Changbai Mountain, the snow landscape of Tianchi and skiing facilities. Meanwhile, passive emotional expressions accounted for 21.07% and tourists gave passive evaluations for travel, transportation, accommodation and catering. (3) Tourists spoke highly of overall images of ice-snow tourism in Jilin Province but few were willing to revisit. In the conclusion, strategies are put forward to improve image perceptions of ice-snow tourism and promote the sustainable development of ice and snow tourism. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Analysis of Structural Changes in Financial Datasets Using the Breakpoint Test and the Markov Switching Model
Symmetry 2020, 12(3), 401; https://doi.org/10.3390/sym12030401 - 04 Mar 2020
Viewed by 885
Abstract
The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that [...] Read more.
The price movements of commodities are determined by changes in the expectations about future economic variables. Crude oil price is non-stationary, highly volatile, and unstructured in nature, which makes it very difficult to predict over short-to-medium time horizons. Some analysts have indicated that the difficulty in forecasting the crude oil price is due to the fact that economic models cannot consistently show evidence of a strong connection between commodities and economic fundamentals, and, as a result, regarded the idea that economic fundamentals help predict price values as random luck. This study aimed to overcome the limitations of the economic models through the detection of structural changes as well as breaks in the data, using a breakpoint test. The Markov switching model is used to address the price patterns that led to a different market state. The results show that there are several changes as well as breaks in the estimated model. Moreover, there is an asymmetric correlation between the crude oil price and the GDP. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results
Symmetry 2020, 12(2), 290; https://doi.org/10.3390/sym12020290 - 17 Feb 2020
Cited by 2 | Viewed by 10490
Abstract
To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. [...] Read more.
To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. Turkish is a language with limited resources for ASR. Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data. In the presented approach, three different methods were used. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the second step, data were collected via a mobile application. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
A Feasible Community Detection Algorithm for Multilayer Networks
Symmetry 2020, 12(2), 223; https://doi.org/10.3390/sym12020223 - 02 Feb 2020
Cited by 1 | Viewed by 887
Abstract
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer [...] Read more.
As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
A Feasible Temporal Links Prediction Framework Combining with Improved Gravity Model
Symmetry 2020, 12(1), 100; https://doi.org/10.3390/sym12010100 - 05 Jan 2020
Viewed by 751
Abstract
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous [...] Read more.
Social network analysis is a multidisciplinary study covering informatics, mathematics, sociology, management, psychology, etc. Link prediction, as one of the fundamental studies with a variety of applications, has attracted increasing focus from scientific society. Traditional research based on graph theory has made numerous achievements, whereas suffering from incapability of dealing with dynamic behaviors and low predicting accuracy. Aiming at addressing the problem, this paper employs a diagonally symmetrical supra-adjacency matrix to represent the dynamic social networks, and proposes a temporal links prediction framework combining with an improved gravity model. Extensive experiments on several real-world datasets verified the superiority on competitors, which benefits recommending friends in social networks. It is of remarkable significance in revealing the evolutions in temporal networks and promoting considerable commercial interest for social applications. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification
Symmetry 2020, 12(1), 92; https://doi.org/10.3390/sym12010092 - 03 Jan 2020
Cited by 6 | Viewed by 966
Abstract
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture [...] Read more.
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis
Symmetry 2019, 11(12), 1471; https://doi.org/10.3390/sym11121471 - 03 Dec 2019
Cited by 1 | Viewed by 967
Abstract
When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built [...] Read more.
When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Algorithm for Detecting Communities in Complex Networks Based on Hadoop
Symmetry 2019, 11(11), 1382; https://doi.org/10.3390/sym11111382 - 07 Nov 2019
Cited by 1 | Viewed by 724
Abstract
With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the [...] Read more.
With the explosive growth of the scale of complex networks, the existing community detection algorithms are unable to meet the needs of rapid analysis of the community structure in complex networks. A new algorithm for detecting communities in complex networks based on the Hadoop platform (called Community Detection on Hadoop (CDOH)) is proposed in this paper. Based on the basic idea of modularity increment, our algorithm implements parallel merging and accomplishes a fast and accurate detection of the community structure in complex networks. Our extensive experimental results on three real datasets of complex networks demonstrate that the CDOH algorithm can improve the efficiency of the current memory-based community detection algorithms significantly without affecting the accuracy of the community detection. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Article
Centrality Metrics’ Performance Comparisons on Stock Market Datasets
Symmetry 2019, 11(7), 916; https://doi.org/10.3390/sym11070916 - 15 Jul 2019
Cited by 5 | Viewed by 1238
Abstract
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data [...] Read more.
The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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Review

Jump to: Research

Review
A Review of Unsupervised Keyphrase Extraction Methods Using Within-Collection Resources
Symmetry 2020, 12(11), 1864; https://doi.org/10.3390/sym12111864 - 12 Nov 2020
Viewed by 616
Abstract
An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth [...] Read more.
An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
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