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Systems
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Data-Driven Modeling and Predictive Analysis for Business, Social, Economic, and...
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Data-Driven Modeling and Predictive Analysis for Business, Social, Economic, and Engineering Applications

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 684

Special Issue Editor

Dr. Mingche Lee

E-Mail Website
Guest Editor
Applied Artificial Intelligence Department, Ming Chuan University, Taiwan
Interests: deep learning; machine learning; IoT; AIoT

Special Issue Information

Dear Colleagues,

Data-driven modeling and predictive analysis have become indispensable tools in understanding and shaping various aspects of our modern society. In the realms of business, social interactions, economics, and engineering, the integration of data-driven approaches has revolutionized decision-making processes and empowered organizations to anticipate trends, mitigate risks, and seize opportunities with greater precision and efficiency. This Special Issue aims to explore the multifaceted applications of data-driven modeling and predictive analysis within the contexts of business, social dynamics, economic trends, and engineering applications.

Key to this exploration are advanced technologies such as machine learning, deep learning, and predictive analytics. These methodologies have demonstrated remarkable capabilities in processing vast amounts of data, extracting meaningful insights, and predicting future trends with unprecedented accuracy. By harnessing the power of these cutting-edge technologies, researchers and practitioners can unlock new possibilities for innovation and decision-making across a wide range of applications.

This Special Issue invites contributions that delve into the application of data-driven modeling and predictive analysis in diverse contexts, including, but not limited to, the following:

  • Business intelligence and analytics.
  • Social network analysis and prediction.
  • Economic forecasting and trend analysis.
  • Engineering optimization and predictive maintenance.
  • Financial market analysis and prediction.
  • Supply chain optimization and demand forecasting.
  • Customer behavior analysis and prediction.
  • Infrastructure optimization.
  • Smart city, smart traffic, and smart campus applications.
  • Long-term care engineering analysis and prediction.
  • Healthcare engineering analysis and prediction.
  • Environmental monitoring and prediction.
  • Data-driven modeling in social and lifelong education.
  • Environmental protection and sustainable engineering.

By examining these diverse applications, we aim to showcase the versatility and effectiveness of data-driven approaches in addressing real-world challenges and driving innovation across various domains. We invite researchers and practitioners to contribute original research articles, reviews, case studies, and perspectives that highlight the advancements and opportunities in data-driven modeling and predictive analysis.

Dr. Mingche Lee
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. Systems 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 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

  • optimization techniques
  • supply chain management
  • infrastructure planning
  • environmental impact analysis
  • decision support systems
  • data-driven innovation
  • real-time analytics
  • interdisciplinary applications
  • sustainable engineering
  • information-based systems

Published Papers (1 paper)

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Research

26 pages, 4770 KiB  
Article
XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring
by Yuxuan Xia, Shanshan Jiang, Lingyi Meng and Xin Ju
Systems 2024, 12(7), 254; https://doi.org/10.3390/systems12070254 - 14 Jul 2024
Viewed by 265
Abstract
Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature [...] Read more.
Credit evaluation has always been an important part of the financial field. The existing credit evaluation methods have difficulty in solving the problems of redundant data features and imbalanced samples. In response to the above issues, an ensemble model combining an advanced feature selection algorithm and an optimized loss function is proposed, which can be applied in the field of credit evaluation and improve the risk management ability of financial institutions. Firstly, the Boruta algorithm is embedded for feature selection, which can effectively reduce the data dimension and noise and improve the model’s capacity for generalization by automatically identifying and screening out features that are highly correlated with target variables. Then, the GHM loss function is incorporated into the XGBoost model to tackle the issue of skewed sample distribution, which is common in classification, and further improve the classification and prediction performance of the model. The comparative experiments on four large datasets demonstrate that the proposed method is superior to the existing mainstream methods and can effectively extract features and handle the problem of imbalanced samples. Full article
(This article belongs to the Special Issue Data-Driven Modeling and Predictive Analysis for Business, Social, Economic, and Engineering Applications)
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