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Computational and Mathematical Methods in Information Science and Engineering, 2nd...
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Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6968

Special Issue Editors

Prof. Dr. Wen Zhang

E-Mail Website
Guest Editor
Department of Economics and Management, Beijing University of Technology, Beijing 100124, China
Interests: data mining; machine learning; business intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaofeng Xu

E-Mail Website
Guest Editor
College of Economics and Management, China University of Petroleum, Qingdao 266580, China
Interests: Industry 4.0; intelligent transportation systems and logistics; big data analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Wu

E-Mail Website
Guest Editor
College of Economics and Management, Beijing University of Chemical Technology, Beijing 100013, China
Interests: supply chain management; big data and AI applications; game theory; system dynamics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kaijian He

E-Mail Website
Guest Editor
College of Tourism, Hunan Normal University, Changsha 410081, China
Interests: forecasting; time series; intelligent energy management; big data analytics; wavelet analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this booming information age, massive amounts of complicated and diverse data resources are being produced over time. Information science and engineering is one of the most attractive research fields due to the need to collect, store, analyze, and visualize these complicated and diverse data to address the challenges of human beings to date. In this area, computational and mathematical methods provide effective tools to handle data and information for pattern recognition, knowledge discovery and utilization, and decision-making for complex problems.

This Special Issue is a follow-up on the first edition, titled "Computational and Mathematical Methods in Information Science and Engineering”, with focuses on recent advances in computational and mathematical methods in information science and engineering to address problems that occur in practice, including theory and potential applications. Topics include, but are not limited to, the following:

  1. Computational intelligence theory and applications;
  2. Intelligent modeling, control, and optimization;
  3. Complex network modeling;
  4. Heterogeneous data mining and fusion;
  5. Big data analytics and artificial intelligence;
  6. Knowledge discovery, inference, and optimization;
  7. Complicated high-dimensional data representation and visualization;
  8. Classification and clustering models in applications;
  9. Heuristic intelligence optimization methods;
  10. Deep learning methods;
  11. Applied mathematics in open-source software analysis;
  12. Modeling and analysis in E-commerce;
  13. Information systems;
  14. Intelligent manufacturing and Industry 4.0;
  15. Intelligent transportation systems and logistics;
  16. Recommendation systems;
  17. Knowledge management processes and systems;
  18. Intelligent tourism and intelligent education.

Prof. Dr. Wen Zhang
Prof. Dr. Xiaofeng Xu
Prof. Dr. Jun Wu
Prof. Dr. Kaijian He
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. Mathematics 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 2600 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

  • computational intelligence
  • mathematical modeling
  • data mining
  • text/document analysis
  • optimization
  • big data analysis
  • deep learning
  • complex networks
  • software defects detection
  • e-commerce
  • social network
  • information systems
  • recommendation systems
  • intelligent logistics
  • intelligent energy management
  • intelligent education
  • intelligent tourism

Related Special Issue

Published Papers (6 papers)

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Research

25 pages, 2911 KiB  
Article
A Double-Layer Coupled Network Model of Network Density Effects on Multi-Stage Innovation Efficiency Dynamics: Agent-Based Modeling Methods
by Jing Han, Wenjing Zhang, Jiutian Wang and Songmei Li
Mathematics 2024, 12(2), 337; https://doi.org/10.3390/math12020337 - 19 Jan 2024
Viewed by 957
Abstract
This paper proposes a double-layer coupled network model to analyze the multi-stage innovation activities of online, and the model consists of two layers: the online layer, which represents the virtual interactions among innovators, and the offline layer, which represents the physical interactions among [...] Read more.
This paper proposes a double-layer coupled network model to analyze the multi-stage innovation activities of online, and the model consists of two layers: the online layer, which represents the virtual interactions among innovators, and the offline layer, which represents the physical interactions among innovators. The model assumes that the innovation activities are influenced by both the online and offline network structures, as well as the coupling effect between them. And it simulates the entire innovation process including knowledge diffusion and knowledge recombination. The model also incorporates the concept of network density, which measures the degree of network connectivity and cohesion (network structure). Observing the network density influence on innovation efficiency during the innovation process is realized through setting the selection mechanism and the knowledge recombination mechanism. The coupling relationship between the two layers of network density on the three stages of innovation is further discussed under the theoretical framework of the innovation value chain. Simulation and experimental results suggest that when the offline network density is constant, a higher online network density is not always better. When the online network density is low, the sparse structure of the online network reduces innovation efficiency. When the online network density is high, the structural redundancy caused by the tight network structure prevents innovation efficiency from improving. The results of the study help enterprises to adjust and optimize the internal cooperation network structure at different stages of innovation in order to maximize its effectiveness and improve the innovation efficiency of enterprises. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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20 pages, 2919 KiB  
Article
Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities
by Yingjie Lu, Xinwei Wang, Lin Su and Han Zhao
Mathematics 2023, 11(21), 4412; https://doi.org/10.3390/math11214412 - 24 Oct 2023
Viewed by 916
Abstract
Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics [...] Read more.
Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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16 pages, 4459 KiB  
Article
Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising
by Qingliang Zhao, Xiaobin Feng, Liwen Zhang and Yiduo Wang
Mathematics 2023, 11(19), 4204; https://doi.org/10.3390/math11194204 - 09 Oct 2023
Viewed by 925
Abstract
Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part [...] Read more.
Urban rail transit offers advantages such as high safety, energy efficiency, and environmental friendliness. With cities rapidly expanding, travelers are increasingly using rail systems, heightening demands for passenger capacity and efficiency while also pressuring these networks. Passenger flow forecasting is an essential part of transportation systems. Short-term passenger flow forecasting for rail transit can estimate future station volumes, providing valuable data to guide operations management and mitigate congestion. This paper investigates short-term forecasting for Suzhou’s Shantang Street station. Shantang Street’s high commercial presence and distinct weekday versus weekend ridership patterns make it an interesting test case, making it a representative subway station. Wavelet denoising and Long Short Term Memory (LSTM) were combined to predict short-term flows, comparing the results to those of standalone LSTM, Support Vector Regression (SVR), Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average Model (ARIMA). This study illustrates that the algorithms adopted exhibit good performance for passenger prediction. The LSTM model with wavelet denoising proved most accurate, demonstrating applicability for short-term rail transit forecasting and practical significance. The research findings can provide fundamental recommendations for implementing appropriate passenger flow control measures at stations and offer effective references for predicting passenger flow and mitigating traffic pressure in various cities. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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16 pages, 3304 KiB  
Article
A Multi-Stage Model for Perceived Quality Evaluation of Clothing Brands
by Minhui Ren, Yu Fan, Jindong Chen and Jian Zhang
Mathematics 2023, 11(18), 3928; https://doi.org/10.3390/math11183928 - 15 Sep 2023
Viewed by 852
Abstract
Perceived quality is crucial for the functioning of clothing brands. However, accurate evaluation of the perceived quality of clothing brands remains a common challenge. To achieve a multidimensional evaluation of the perceived quality of clothing brands, an index system is derived based on [...] Read more.
Perceived quality is crucial for the functioning of clothing brands. However, accurate evaluation of the perceived quality of clothing brands remains a common challenge. To achieve a multidimensional evaluation of the perceived quality of clothing brands, an index system is derived based on perceived quality theory. Then, by combining a fine-grained sentiment analysis approach with stochastic dominance criteria, a multi-stage model ECRM is proposed for the perceived quality evaluation of clothing brands based on online user reviews. ECRM comprises three stages: Extraction, Classification, and Ranking. To begin with, Contrastive Attention and dependency parsing are used to extract attribute–viewpoint phrases from online reviews. Subsequently, the pre-trained models are employed to classify the indexes and sentiment levels of these phrases. Furthermore, the perceived quality indexes are ranked using stochastic dominance criteria and the PROMETHEE-II method. Empirical analysis is conducted for the clothing brands of ALDB, AND, BNL, and QPL; the results show that, based on online user reviews, ECRM enables accurate evaluation of the perceived quality of clothing brands. Based on the evaluation results, it is found that Comfort, External, Protection, and Fineness are highly valued by consumers; moreover, the four brands focus on different indexes. Specific strategies for perceived quality improvements are proposed depending on the current status of the brands. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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16 pages, 1041 KiB  
Article
A Study on the Effects of Digital Finance on Green Low-Carbon Circular Development Based on Machine Learning Models
by Xuewei Zhang, Xiaoqing Ai, Xiaoxiang Wang, Gang Zong and Jinghao Zhang
Mathematics 2023, 11(18), 3903; https://doi.org/10.3390/math11183903 - 13 Sep 2023
Cited by 3 | Viewed by 1413
Abstract
With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow [...] Read more.
With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally-friendly enterprises, thereby promoting green low-carbon circular development (GLCD). However, few studies have explored the coupling mechanism between DF and GLCD. To fill this gap, this paper explores the effect of DF on GLCD, and established a mediating effect model to investigate the mechanism of DF in promoting GLCD. Additionally, this paper established a random forest model and a CatBoost model based on machine learning to examine the relative importance of DF and the factors affecting GLCD. The results show that DF has significant positive effects on GLCD, and technological innovation plays a key role in the effect of DF on GLCD; meanwhile, the effect of DF on GLCD shows nonlinear features with an increasing “marginal effect”; moreover, both DF and conventional factors have significant impacts on GLCD. Our study highlights the effect of DF on GLCD and underscores the importance of developing policies for DF and GLCD. This study provides an empirical basis and path reference for DF to achieve “carbon peak, carbon neutralization” in China. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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15 pages, 2206 KiB  
Article
Enhancing Sensor-Based Mobile User Authentication in a Complex Environment by Deep Learning
by Zhengqiu Weng, Shuying Wu, Qiang Wang and Tiantian Zhu
Mathematics 2023, 11(17), 3708; https://doi.org/10.3390/math11173708 - 29 Aug 2023
Viewed by 1364
Abstract
With the advent of smart mobile devices, end users get used to transmitting and storing their individual privacy in them, which, however, has aroused prominent security concerns inevitably. In recent years, numerous researchers have primarily proposed to utilize motion sensors to explore implicit [...] Read more.
With the advent of smart mobile devices, end users get used to transmitting and storing their individual privacy in them, which, however, has aroused prominent security concerns inevitably. In recent years, numerous researchers have primarily proposed to utilize motion sensors to explore implicit authentication techniques. Nonetheless, for them, there are some significant challenges in real-world scenarios. For example, depending on the expert knowledge, the authentication accuracy is relatively low due to some difficulties in extracting user micro features, and noisy labels in the training phrase. To this end, this paper presents a real-time sensor-based mobile user authentication approach, ST-SVD, a semi-supervised Teacher–Student (TS) tri-training algorithm, and a system with client–server (C-S) architecture. (1) With S-transform and singular value decomposition (ST-SVD), we enhance user micro features by transforming time-series signals into 2D time-frequency images. (2) We employ a Teacher–Student Tri-Training algorithm to reduce label noise within the training sets. (3) To obtain a set of robust parameters for user authentication, we input the well-labeled samples into a CNN (convolutional neural network) model, which validates our proposed system. Experimental results on large-scale datasets show that our approach achieves authentication accuracy of 96.32%, higher than the existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering, 2nd Edition)
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