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Data Push and Data Mining in the Age of Artificial...
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Data Push and Data Mining in the Age of Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 5578

Special Issue Editor

Prof. Dr. Fan Wu

E-Mail Website
Guest Editor
Department of Information Management, National Chung Cheng University, 168 University Rd., Minhsiung 62102, Taiwan
Interests: big data analysis; distributed database system; interconnection network; data mining

Special Issue Information

Dear Colleagues,

Artificial Intelligence (A.I.) is one of the ultimate goal of computers. A.I. can passively obtain knowledge and rules from data mining technology, and also can actively push data, alarm, remind, or even make the decision from user’s devices when some criterion is fulfilled. Recently, the big data collecting from IOT, stock, healthcare, automobile vision, and so on. Streaming data as well as its auto control is a mature technology. The intervention of A.I. plays the role of solving the most tedious and timely problems.

The aim of this Special Issue is to focus on the “data push and data mining of the age of artificial intelligence”, providing new approaches and technologies for better understanding knowledge-based artificial intelligence. Submissions to this Special Issue are solicited to represent an innovative and creative snapshot of the field’s development by covering a range of topics that include, but are not limited to: data push; data mining (such as association rule, time sequence, outlier, etc.); knowledge representation; topology; AI algorithms, solutions, and applications.

We look forward to receiving your contributions.

Prof. Dr. Fan Wu
Guest Editor

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 submissions that pass pre-check are 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • data mining
  • big data
  • knowledge reprsentation
  • agents
  • soft computing
  • data push
  • deep learning

Published Papers (6 papers)

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Research

14 pages, 1465 KiB  
Article
Trustworthiness of Review Opinions on the Internet for 3C Commodities
by Ying-Chia Hsieh, Long-Chuan Lu and Ruen-Jung Yang
Electronics 2024, 13(7), 1346; https://doi.org/10.3390/electronics13071346 - 03 Apr 2024
Viewed by 351
Abstract
The rapid development of the internet has resulted in rapid e-business growth, with online malls attracting many shoppers due to the privacy and convenience they offer. Like traditional malls, online malls can provide photos, specifications, prices, etc. However, consumers cannot touch the products [...] Read more.
The rapid development of the internet has resulted in rapid e-business growth, with online malls attracting many shoppers due to the privacy and convenience they offer. Like traditional malls, online malls can provide photos, specifications, prices, etc. However, consumers cannot touch the products in reality, which creates risks for the purchase. To date, there has been no research focusing on topic-specific search engines for 3C product reviews based on the trustworthiness of the reviews. This study is the first to sort the reviews of electronic products according to the degree of trust, by analyzing the characteristics of the reviews and the reviewers. This study proposes the criteria for features of the reviews and reviewers to consider to evaluate the trustworthiness of the reviews; builds a search engine to collect the product reviews scattered in opinion websites; and sorts the results by trustworthiness to provide a reliable e-commerce experience. To demonstrate the effectiveness of the proposed method, we conducted a set of experiments, and we adopted the Spearman’s rank correlation coefficient to evaluate the similarity between our method and experts’ opinions. The experimental results showed a high correlation coefficient with the opinions of experts, demonstrating that our method is effective at finding trustworthy reviews on the internet. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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22 pages, 2734 KiB  
Article
Improving the Precision of Image Search Engines with the Psychological Intention Diagram
by Meng-Qian Alexander Wu, Fan Wu and Wen-Bin Lin
Electronics 2024, 13(1), 208; https://doi.org/10.3390/electronics13010208 - 02 Jan 2024
Viewed by 593
Abstract
With the increase in the amount of images online, the whole Internet is becoming an image database. Since there are so many available images, it is difficult for users to find the desired images. Unlike text search engines, image search engines cannot fully [...] Read more.
With the increase in the amount of images online, the whole Internet is becoming an image database. Since there are so many available images, it is difficult for users to find the desired images. Unlike text search engines, image search engines cannot fully recognize the visual meaning of an image. In addition, it is difficult to obtain the desired images from the keywords provided by the user, since a keyword may contain multiple meanings. To solve these problems, this paper proposes a psychological intention diagram of past users, if inquiring using a keyword, to predict the images that these users want. Based upon the novel psychological diagram, this paper proposes a search engine that analyzes images in the sequential probing of the current user if he/she inquires after the same keywords as previous users. Moreover, this paper also constructs a psychological intention diagram of the designers of the web pages containing the keyword. This type of psychological intention diagram is used when a query is not issued by past users. To the best of our knowledge, this paper is the first one considering the psychological viewpoint of users and web designers in guiding the retrieval of the search engine. The experimental results show that the proposed image search engine has high precision; therefore, the method of providing images can help users to find their desired image more easily. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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18 pages, 1688 KiB  
Article
Review Evaluation for Hotel Recommendation
by Ying-Chia Hsieh, Long-Chuan Lu and Yi-Fan Ku
Electronics 2023, 12(22), 4673; https://doi.org/10.3390/electronics12224673 - 16 Nov 2023
Viewed by 539
Abstract
With the prevalence of backpacking and the convenience of using the Internet, many travelers like sharing their experiences in online communities. The development of online communities has changed the decision-making process of consumer purchasing, especially for travel, i.e., some travelers reconsider their decisions [...] Read more.
With the prevalence of backpacking and the convenience of using the Internet, many travelers like sharing their experiences in online communities. The development of online communities has changed the decision-making process of consumer purchasing, especially for travel, i.e., some travelers reconsider their decisions because they believe that the reviews of online communities are more valuable than advertisements. However, these reviews are not completely reliable since most reviews are provided without specific author information and the review data are too large to be observed. In this paper, we propose a novel approach (named ET) to evaluate the trustworthiness of reviews in online travel communities. Our method considers three concepts, including the sentiment similarity of reviewers in the social network, features of the reviews, and behaviors of the reviewers. The experimental results demonstrate that our method is effective in evaluating the trustworthiness of reviews. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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22 pages, 2676 KiB  
Article
Enhanced Social Recommendation Method Integrating Rating Bias Offsets
by Lu Han, Jiwei Qin and Boshen Xia
Electronics 2023, 12(18), 3926; https://doi.org/10.3390/electronics12183926 - 18 Sep 2023
Cited by 1 | Viewed by 934
Abstract
Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating standards and a high average rating [...] Read more.
Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating standards and a high average rating does not always indicate a real preference for the item. This situation highlights inherent flaws in existing recommendation algorithms that do not adequately account for bias in user and item ratings and rating trends. To address this problem, this paper proposes an enhanced social recommendation method based on GNNs with integrated rating bias offsets (SR-BS). Firstly, we obtain rating bias from users and items by subtracting their average rating value from the historical rating value for each user/item. To enhance the model’s learning capability, we transform the rating biases into vector representations. Secondly, in the model learning, diverse meta-paths are predefined for modeling interaction relations between graph nodes (e.g., user–item–user, user–user). The aggregation of semantic information from these relational paths is achieved by stacking multiple GNN layers, enabling the fusion of higher-order information. Finally, the experimental results on four datasets—Ciao, Epinions, Douban, and FilmTrust—show that our method outperforms other state-of-the-art methods in social recommendation tasks, exhibiting high stability and personalization. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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16 pages, 660 KiB  
Article
Stylometric Fake News Detection Based on Natural Language Processing Using Named Entity Recognition: In-Domain and Cross-Domain Analysis
by Chih-Ming Tsai
Electronics 2023, 12(17), 3676; https://doi.org/10.3390/electronics12173676 - 31 Aug 2023
Cited by 2 | Viewed by 1632
Abstract
Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, [...] Read more.
Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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19 pages, 865 KiB  
Article
Supervised Dimensionality Reduction of Proportional Data Using Exponential Family Distributions
by Walid Masoudimansour and Nizar Bouguila
Electronics 2023, 12(15), 3355; https://doi.org/10.3390/electronics12153355 - 05 Aug 2023
Viewed by 799
Abstract
Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data properly since they construct neighborhood graphs that do not discriminate between multi-modal [...] Read more.
Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data properly since they construct neighborhood graphs that do not discriminate between multi-modal classes of data and single-modal ones. In this paper, a novel method that mitigates the above problems is proposed. In this method, assuming the data is from two classes, they are projected into the low-dimensional space in the first step which removes sparsity from the data and reduces the time complexity of any operation drastically afterwards. These projected data are modeled using a mixture of exponential family distributions for each class, allowing the modeling of multi-modal data. A measure for the similarity between the two projected classes is used as an objective function for constructing an optimization problem, which is then solved using a heuristic search algorithm to find the best separating projection. The conducted experiments show that the proposed method outperforms the rest of the compared algorithms and provides a robust effective solution to the problem of dimensionality reduction even in the presence of multi-modal and sparse data. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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