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Electronics
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Applications of Big Data and AI
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Applications of Big Data and AI

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 7899

Special Issue Editors

Prof. Dr. Defu Zhang

E-Mail Website
Guest Editor
School of Informatics, Xiamen University, Xiamen 361005, China
Interests: big data; computational intelligence; data mining; deep learning; knowledge graph; combinatorial optimization
Prof. Dr. Yimin Mao

E-Mail Website
Guest Editor
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Interests: big data; data mining

Special Issue Information

Dear Colleagues,

Against the background of China’s strategy of promoting rural revitalization, big data have become a hot topic in agricultural research and application. This Special Issue calls for papers related to the collection of agricultural big data, the application system, and key technologies of agricultural big data, agricultural decision making, intelligent agriculture, food safety, and so on.

Prof. Dr. Defu Zhang
Prof. Dr. Yimin Mao
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 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.

Published Papers (6 papers)

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Research

22 pages, 5088 KiB  
Article
Traditional Chinese Medicine Knowledge Graph Construction Based on Large Language Models
by Yichong Zhang and Yongtao Hao
Electronics 2024, 13(7), 1395; https://doi.org/10.3390/electronics13071395 - 07 Apr 2024
Viewed by 518
Abstract
This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships [...] Read more.
This study explores the use of large language models in constructing a knowledge graph for Traditional Chinese Medicine (TCM) to improve the representation, storage, and application of TCM knowledge. The knowledge graph, based on a graph structure, effectively organizes entities, attributes, and relationships within the TCM domain. By leveraging large language models, we collected and embedded substantial TCM–related data, generating precise representations transformed into a knowledge graph format. Experimental evaluations confirmed the accuracy and effectiveness of the constructed graph, extracting various entities and their relationships, providing a solid foundation for TCM learning, research, and application. The knowledge graph has significant potential in TCM, aiding in teaching, disease diagnosis, treatment decisions, and contributing to TCM modernization. In conclusion, this paper utilizes large language models to construct a knowledge graph for TCM, offering a vital foundation for knowledge representation and application in the field, with potential for future expansion and refinement. Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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17 pages, 6149 KiB  
Article
An Automatic Generation and Verification Method of Software Requirements Specification
by Xiaoyang Wei, Zhengdi Wang and Shuangyuan Yang
Electronics 2023, 12(12), 2734; https://doi.org/10.3390/electronics12122734 - 19 Jun 2023
Viewed by 1190
Abstract
The generation of standardized requirements specification documents plays a crucial role in software processes. However, the manual composition of software requirements specifications is a laborious and time-consuming task, often leading to errors that deviate from the actual requirements. To address this issue, this [...] Read more.
The generation of standardized requirements specification documents plays a crucial role in software processes. However, the manual composition of software requirements specifications is a laborious and time-consuming task, often leading to errors that deviate from the actual requirements. To address this issue, this paper proposes an automated method for generating requirements specifications utilizing a knowledge graph and graphviz. Furthermore, in order to overcome the limitations of the existing automated requirement generation process, such as inadequate emphasis on data information and evaluation, we enhance the traditional U/C matrix by introducing an S/U/C matrix. This novel matrix represents the outcomes of data/function systematic analysis, and verification is facilitated through the design of inspection rules. Experimental results demonstrate that the requirements specifications generated using this method achieve standardization and adherence to regulations, while the devised S/U/C inspection rules facilitate the updating and iteration of the requirements specifications. Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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18 pages, 5695 KiB  
Article
Chinese Brand Identity Management Based on Never-Ending Learning and Knowledge Graphs
by Dalin Li, Yijin Wang, Guansu Wang, Jiadong Lu, Yong Zhu, Gábor Bella and Yanchun Liang
Electronics 2023, 12(7), 1625; https://doi.org/10.3390/electronics12071625 - 30 Mar 2023
Viewed by 1310
Abstract
Brand identity (BI) refers to the individual characteristics of an enterprise or a certain brand in the market and in the mind of the public. It reflects the evaluation and recognition of the public on the brand and is the core of the [...] Read more.
Brand identity (BI) refers to the individual characteristics of an enterprise or a certain brand in the market and in the mind of the public. It reflects the evaluation and recognition of the public on the brand and is the core of the market strategy. Successful BI management can bring great business value. Nowadays, the BI management methods based on Internet, big data, and AI are widely adopted. However, they are also confronted with problems, such as accuracy, effectiveness, and sustainability, especially for the Chinese BI. Our work applies the knowledge graph (KG) and never-ending learning (NEL) for exploring efficient Chinese BI management methods. We adapt the NEL framework for the sustainability. In order to improve the accuracy and effectiveness, we express the BI knowledge with KGs and propose two methods in the subsystem components of NEL: (1) the BI evaluation model based on KG and two-dimensional bag-of-words; (2) the Apriori based on KG. In the knowledge integrator of NEL, we propose the synonym KGs for suppressing the concept duplication and drift. The experimental results show that our method reached high consistency with the experts of BI management and the industry reports. Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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18 pages, 4663 KiB  
Article
Incremental Connected Component Detection for Graph Streams on GPU
by Kyoungsoo Bok, Namyoung Kim, Dojin Choi, Jongtae Lim and Jaesoo Yoo
Electronics 2023, 12(6), 1465; https://doi.org/10.3390/electronics12061465 - 20 Mar 2023
Cited by 1 | Viewed by 1250
Abstract
Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we [...] Read more.
Studies on the real-time detection of connected components in graph streams have been carried out. The existing connected component detection method cannot process connected components incrementally, and the performance deteriorates due to frequent data transmission when GPU is used. In this paper, we propose a new incremental processing method to solve the problems found in the existing methods for detecting connected components on GPUs. The proposed method minimizes the amount of data to be sent to the GPU by determining the subgraph affected by the graph stream update and by detecting the part to be recalculated. We consider the number of vertices to quickly determine the connected components of a graph stream on the GPU. An asynchronous execution method is used to shorten the transfer time between the CPU and the GPU according to real-time graph stream changes. In order to show that the proposed method provides fast incremental connected component detection on the GPU, we evaluated its performance using various datasets. Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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18 pages, 2198 KiB  
Article
Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing
by Kun Wang, Zhigang Chen, Lizhong Zhang, Jiaqi Liu and Bin Li
Electronics 2023, 12(4), 1037; https://doi.org/10.3390/electronics12041037 - 19 Feb 2023
Cited by 1 | Viewed by 1136
Abstract
Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure [...] Read more.
Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.  Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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18 pages, 1689 KiB  
Article
Music Recommendation Based on “User-Points-Music” Cascade Model and Time Attenuation Analysis
by Tuntun Wang, Junke Li, Jincheng Zhou, Mingjiang Li and Yong Guo
Electronics 2022, 11(19), 3093; https://doi.org/10.3390/electronics11193093 - 28 Sep 2022
Cited by 1 | Viewed by 1597
Abstract
Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music according to the specific music in the user’s [...] Read more.
Music has an increasing impact on people’s daily lives, and a sterling music recommendation algorithm can help users find their habitual music accurately. Recent research on music recommendation directly recommends the same type of music according to the specific music in the user’s historical favorite list. However, users’ behavior towards a certain cannot reflect the preference for this type of music and possibly provides music the listener dislikes. A recommendation model, MCTA, based on “User-Point-Music” structure is proposed. By clustering users’ historical behavior, different interest points are obtained to further recommend high-quality music under interest points. Furthermore, users’ interest points will decay over time. Combined with the number of music corresponding to each interest point and the liking degree of each music, a multi-interest point attenuation model is constructed. Based on the real data after desensitization and encoding, including 100,000 users and 12,028 pieces of music, a series of experimental results show that the effect of the proposed MCTA model has improved by seven percentage points in terms of accuracy compared with existing works. It came to the conclusion that the multi-interest point attenuation model can more accurately simulate the actual music consumption behavior of users and recommend music better. Full article
(This article belongs to the Special Issue Applications of Big Data and AI)
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