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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 July 2025 | Viewed by 27195

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Dr. Mingche Lee

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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

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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

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Published Papers (12 papers)

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Research

22 pages, 941 KiB

Open AccessArticle

Systematically Formulating Investments for Carbon Offset by Multiple-Objective Portfolio Selection: Classifying, Evolving, and Optimizing

by Long Lin and Yue Qi

Systems 2025, 13(6), 441; https://doi.org/10.3390/systems13060441 - 6 Jun 2025

Viewed by 128

Abstract

Our society is facing serious challenges from global warming and environmental degradation. Scientists have identified carbon dioxide as one of the causes. Our society is embracing carbon offset as a way to field the challenges. The purpose of carbon offset is trying to cancel out the large amounts of carbon dioxide by investing in projects that reduce or remove emissions elsewhere. Examples of carbon offset projects are planting trees, renewable energy projects, and capturing methane from landfills or farms. Not all carbon offset projects are equally effective. In stock markets, investors eagerly pursue carbon offset. Namely, investors favor carbon offset in addition to risk and return when investing. Therefore, investors supervise risk, return, and carbon offset. Investors’ pursuits raise the question of how to model carbon offset for investments. The traditional answer is to adopt carbon offset screening and engineer portfolios by stocks with good carbon offset ratings. However, Nobel Laureate Markowitz emphasizes portfolio selection rather than stock selection. Moreover, carbon offset is composed of multiple components, ranging from business, social, economic, and environmental aspects. This multifaceted nature requires more advanced models than carbon offset screening and portfolio selection. Within this context, we systematically formulate multiple-objective portfolio selection models that include carbon offset. Firstly, we extend portfolio selection and treat carbon offset as a whole. Secondly, we separate carbon offsets into different components and build models to monitor each component. Thirdly, we innovate a model to monitor each component’s expectation and mitigate each component’s risk. Lastly, we optimize the series of models and prove the models’ properties in theorems. Mathematically, this paper makes theoretical contributions to multiple-objective optimization, particularly by proving the consistency of efficient solutions during objective classification and model evolution, describing the structure of properly efficient sets for multiple quadratic objectives, and elucidating the optimization’s sensitivity analyses. Moreover, by coordinating the abstract objective function, our formulation is generalizable. Overall, this paper’s contribution is to model carbon offset investments through multiple-objective portfolio selection. This paper’s methodology is multiple-objective optimization. This paper’s achievements are to provide investors with greater precision and effectiveness than carbon offset screening and portfolio selection through engineering means and to mathematically prove the properties of the model. 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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24 pages, 4341 KiB

Open AccessArticle

Intraday and Post-Market Investor Sentiment for Stock Price Prediction: A Deep Learning Framework with Explainability and Quantitative Trading Strategy

by Guowei Sun and Yong Li

Systems 2025, 13(5), 390; https://doi.org/10.3390/systems13050390 - 18 May 2025

Cited by 1 | Viewed by 1072

Abstract

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. 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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23 pages, 571 KiB

Open AccessArticle

Primary Determinants and Strategic Implications for Customer Loyalty in Pet-Related Vertical E-Commerce: A Machine Learning Approach

by YongHyun Lee, Kwangtek Na, Jungwook Rhim and Eunchan Kim

Systems 2025, 13(3), 175; https://doi.org/10.3390/systems13030175 - 4 Mar 2025

Cited by 1 | Viewed by 1350

Abstract

In the contemporary and dynamic business landscape, the establishment of a loyal customer base is a fundamental imperative for long-term organizational viability. This research undertakes a comprehensive exploration into the formation of customer loyalty within the niche of pet-related vertical e-commerce, focusing on South Korea, and leverages advanced machine learning methodologies. We identify key factors that significantly impact customer loyalty development using various machine learning models, including logistic regression analysis, decision trees, support vector machines, random forests, and XGBoost. Our empirical study shows that encouraging customer transactions plays a crucial and transformative role in building loyalty regardless of the day of the week. Furthermore, the strategic promotion of mobile application notifications and the active encouragement of customer participation through product reviews are indispensable strategies for strengthening and solidifying customer loyalty. These findings have crucial implications not only for enterprises within the pet-related e-commerce sector but also for the broader e-commerce domain. We hereby propose a methodology to identify loyal customers and systematically analyze the key factors that influence their formation using machine learning in the vertical e-commerce pet industry. 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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20 pages, 767 KiB

Open AccessArticle

Assessing the Influence of Business Intelligence and Analytics and Data-Driven Culture on Managerial Performance: Evidence from Romania

by Luminita Hurbean, Florin Militaru, Valentin Partenie Munteanu, Doina Danaiata, Doina Fotache and Mihaela Muntean

Systems 2025, 13(1), 2; https://doi.org/10.3390/systems13010002 - 24 Dec 2024

Viewed by 1520

Abstract

Business intelligence and analytics (BI&A) have recently emerged as a strategic approach to managerial tasks, providing opportunities to improve work performance. Despite the growing interest in evaluating cases of BI&A adoption, to the best of our knowledge, few studies have addressed the influence of data-driven culture and the effects of BI&A adoption specifically on the work performance of managers. The aim of this study is to assess whether a data-driven culture predicts the adoption of BI&A in companies and its impact on decision-making effectiveness and managerial performance. This novel research model was tested with 180 managers from Romanian companies that work with BI&A tools. Based on PLS-SEM data analysis, our findings suggest that a data-oriented culture is a strong predictor of BI&A adoption and decision-making effectiveness. The results also confirm that BI&A utilization positively impacts decision-making effectiveness and individual work performance. The primary implication drawn from empirical evidence is that executives should prioritize the cultivation of a data-driven culture within their organizations, as this is essential for enhancing managerial performance through the adoption of business intelligence and analytics. 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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20 pages, 6952 KiB

Open AccessArticle

Development of a Forecasting Framework Based on Advanced Machine Learning Algorithms for Greenhouse Gas Emissions

by Seval Ene Yalçın

Systems 2024, 12(12), 528; https://doi.org/10.3390/systems12120528 - 27 Nov 2024

Cited by 2 | Viewed by 1294

Abstract

The reduction of greenhouse gas emissions, in order to effectively address the issue of climate change, has critical importance worldwide. To achieve this aim and implement the necessary strategies and policies, the projection of greenhouse gas emissions is essential. This paper presents a forecasting framework for greenhouse gas emissions based on advanced machine learning algorithms: multivariable linear regression, random forest, k-nearest neighbor, extreme gradient boosting, support vector, and multilayer perceptron regression algorithms. The algorithms employ several input variables associated with greenhouse gas emission outputs. In order to evaluate the applicability and performance of the developed framework, nationwide statistical data from Turkey are employed as a case study. The dataset of the case study includes six input variables and annual sectoral and total greenhouse gas emissions in CO₂ eq. as output variables. This paper provides a scenario-based approach for future forecasts of greenhouse gas emissions and a sector-based analysis of greenhouse gas emissions in the case country considering multiple input variables. The present study indicates that the stated machine learning algorithms can be successfully applied to the forecasting of greenhouse gas emissions. 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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26 pages, 10679 KiB

Open AccessArticle

Urban Ecological Economic Resilience in China: Spatio-Temporal Evolution, Influencing Factors, and Trend Prediction

by Kexin Wang, Bowen Zhang, Shuyue Jiang and Rui Ding

Systems 2024, 12(12), 525; https://doi.org/10.3390/systems12120525 - 26 Nov 2024

Cited by 2 | Viewed by 900

Abstract

This article adopted exploratory spatio-temporal data analysis (ESTDA), geographic detector, and spatial Markov chain model to analyze the spatio-temporal evolution characteristics, main influencing factors, and future trend predictions of urban ecological economic resilience (EER). The results show that EER has been significantly enhanced, and high-level cities have a “rhombus” spatial distribution pattern. EER has a noticeable spatial agglomeration effect and the range of high–high agglomeration areas has gradually expanded. The LISA time path reflects that the spatial structure of EER is relatively stable, and urban units and neighboring cities show a more apparent synergistic growth trend. Social development, economic support, ecological restoration, and innovation and transformation strongly influence the development of EER, and the interaction between factors is more significant. In the future, EER will still tend to maintain the existing stable and unchanged state, and cross-grade leapfrogging development will not be achieved. 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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19 pages, 4321 KiB

Open AccessArticle

Bitcoin Trend Prediction with Attention-Based Deep Learning Models and Technical Indicators

by Ming-Che Lee

Systems 2024, 12(11), 498; https://doi.org/10.3390/systems12110498 - 18 Nov 2024

Viewed by 4957

Abstract

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors. 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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26 pages, 3916 KiB

Open AccessArticle

Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China

by Kangye Tan, Yumeng Wu, Fang Xu, Xuanyu Ji and Chunsheng Li

Systems 2024, 12(11), 458; https://doi.org/10.3390/systems12110458 - 30 Oct 2024

Cited by 2 | Viewed by 1885

Abstract

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals. 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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27 pages, 2585 KiB

Open AccessArticle

Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

by Hao Huang

Systems 2024, 12(10), 416; https://doi.org/10.3390/systems12100416 - 8 Oct 2024

Cited by 4 | Viewed by 4696

Abstract

This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development. 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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29 pages, 1354 KiB

Open AccessArticle

An Approach for Multi-Item Product Sales Forecasting Based on Advancing the BCG Matrix with Matrix-Clustering and Time Modeling Techniques

by Che-Yu Hung and Chien-Chih Wang

Systems 2024, 12(10), 388; https://doi.org/10.3390/systems12100388 - 25 Sep 2024

Cited by 1 | Viewed by 2478

Abstract

Customized production has greatly diversified product categories, which has altered product life cycles and added complexity to business management. This paper introduces a matrix-clustering technique that integrates k-means clustering with the BCG Matrix, enhanced by time modeling, to offer a comprehensive framework for multi-item product sales forecasting. The approach builds upon existing BCG Matrix outcomes, re-clustering high-selling products more precisely and redefining their relationship with other product lines more objectively. This method addresses the challenge of forecasting situations with limited historical data, providing more accurate sales predictions. Using Taiwan’s sales data, an empirical study on integrated circuit tray products demonstrated the effectiveness of the matrix clustering technique. The results showed improved data utilization, increasing from 35.93% with the original BCG analysis to 52.43% with the combined matrix-clustering and time modeling methods. This study contributes to academic research by presenting a portfolio analysis approach rooted in matrix clustering, which systematically enhances traditional BCG Matrix methods. The proposed framework is adaptable to the unique traits of different portfolios, offering businesses workflows that are efficient, reliable, sustainable, and scalable. 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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23 pages, 1919 KiB

Open AccessArticle

A Novel Intelligent Prediction Model for the Containerized Freight Index: A New Perspective of Adaptive Model Selection for Subseries

by Wendong Yang, Hao Zhang, Sibo Yang and Yan Hao

Systems 2024, 12(8), 309; https://doi.org/10.3390/systems12080309 - 19 Aug 2024

Viewed by 1348

Abstract

The prediction of the containerized freight index has important economic and social significance. Previous research has mostly applied sub-predictors directly for integration, which cannot be optimized for different datasets. To fill this research gap and improve prediction accuracy, this study innovatively proposes a new prediction model based on adaptive model selection and multi-objective ensemble to predict the containerized freight index. The proposed model comprises the following four modules: adaptive data preprocessing, model library, adaptive model selection, and multi-objective ensemble. Specifically, an adaptive data preprocessing module is established based on a novel modal decomposition technology that can effectively reduce the impact of perturbations in historical data on the prediction model. Second, a new model library is constructed to predict the subseries, consisting of four basic predictors. Then, the adaptive model selection module is established based on Lasso feature selection to choose valid predictors for subseries. For the subseries, different predictors can produce different effects; thus, to obtain better prediction results, the weights of each predictor must be reconsidered. Therefore, a multi-objective artificial vulture optimization algorithm is introduced into the multi-objective ensemble module, which can effectively improve the accuracy and stability of the prediction model. In addition, an important discovery is that the proposed model can acquire different models, adaptively varying with different extracted data features in various datasets, and it is common for multiple models or no model to be selected for the subseries.The proposed model demonstrates superior forecasting performance in the real freight market, achieving average MAE, RMSE, MAPE, IA, and TIC values of 9.55567, 11.29675, 0.44222%, 0.99787, and 0.00268, respectively, across four datasets. These results indicate that the proposed model has excellent predictive ability and robustness. 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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26 pages, 4783 KiB

Open AccessArticle

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

Cited by 6 | Viewed by 3575

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 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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