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Mathematics
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Computational Intelligence: Theory and Applications, 2nd Edition
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Computational Intelligence: Theory and Applications, 2nd Edition

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1823

Special Issue Editors

Prof. Dr. Zuguo Yu

E-Mail Website
Guest Editor
School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China
Interests: fractal theory and applications; time series analysis; complex network analysis; biological and environmental data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xueshuang Xiang

E-Mail Website
Guest Editor
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China
Interests: numerical PDE; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Jiang

E-Mail Website
Guest Editor
School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China
Interests: materials modeling and computation; artificial intelligence; scientific computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) is an intellectual mode of low-level cognition. If a system only involves numerical (low-level) data and does not use knowledge in the sense of artificial intelligence, then it can be regarded as a CI system. The areas covered by computational intelligence include fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The theories and techniques of CI allow us to find solutions to problems in pattern recognition, control, automated decision-making, optimization, statistical modeling, and many other areas. The research and development of CI reflect the important interdisciplinary and integrative development trend of present science and technology. This Special Issue will focus on new developments and advances in the various areas of computational intelligence, including the theory and applications to the fields of engineering, scientific computing, computer science, physics and life sciences.

Prof. Dr. Zuguo Yu
Dr. Xueshuang Xiang
Prof. Dr. Kai Jiang
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

  • topics include, but are not limited to, the following: pattern recognition
  • prediction systems
  • process and system control
  • bioinformatics
  • cloud computing
  • data mining
  • decision-support systems
  • intelligent information retrieval
  • noise analysis
  • real-time systems
  • signal and image processing
  • system modelling and optimization
  • time-series prediction
  • deep learning
  • scientific computing

Published Papers (2 papers)

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Research

26 pages, 4745 KiB  
Article
Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms
by Manuela Panoiu, Caius Panoiu and Petru Ivascanu
Mathematics 2024, 12(6), 839; https://doi.org/10.3390/math12060839 - 13 Mar 2024
Viewed by 668
Abstract
The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. [...] Read more.
The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term. Full article
(This article belongs to the Special Issue Computational Intelligence: Theory and Applications, 2nd Edition)
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17 pages, 5467 KiB  
Article
Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy
by Jianhua Guan, Zuguo Yu, Yongan Liao, Runbin Tang, Ming Duan and Guosheng Han
Mathematics 2024, 12(2), 272; https://doi.org/10.3390/math12020272 - 14 Jan 2024
Viewed by 781
Abstract
The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step [...] Read more.
The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step when it is successfully resolved. In recent years, due to the increasing rights awareness of employees, the number of labor disputes has been rising annually. However, resolving labor disputes is time-consuming and labor-intensive, which brings a heavy burden to employees and dispute resolution institutions. Using artificial intelligence algorithms to identify and predict the critical path of labor dispute resolution is helpful for saving resources and improving the efficiency of, and reducing the cost of dispute resolution. In this study, a machine learning approach based on Shapley Additive exPlanations (SHAP) and a soft voting strategy is applied to predict the critical path of labor dispute resolution. We name our approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting). This approach employs three machine learning models (Random Forest, Extra Trees, and CatBoost) and then integrates them using a soft voting strategy. Additionally, SHAP is used to explain the model and analyze the feature contribution. Based on the ranking of feature importance obtained from SHAP and an incremental feature selection method, we obtained an optimal feature subset comprising 33 features. The LDMLSV achieves an accuracy of 0.90 on this optimal feature subset. Therefore, the proposed approach is a highly effective method for predicting the critical path of labor dispute resolution. Full article
(This article belongs to the Special Issue Computational Intelligence: Theory and Applications, 2nd Edition)
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