Decision Analytics Journal最新文献

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Advanced outlier detection methods for enhancing beta regression robustness 提高β回归稳健性的先进离群点检测方法
Decision Analytics Journal Pub Date : 2025-03-01 DOI: 10.1016/j.dajour.2025.100557
Oktsa Dwika Rahmashari, Wuttichai Srisodaphol
{"title":"Advanced outlier detection methods for enhancing beta regression robustness","authors":"Oktsa Dwika Rahmashari,&nbsp;Wuttichai Srisodaphol","doi":"10.1016/j.dajour.2025.100557","DOIUrl":"10.1016/j.dajour.2025.100557","url":null,"abstract":"<div><div>Beta regression is a valuable statistical technique for modeling response variables within the standard unit interval (0, 1), where values represent rates, proportions, or probabilities. However, outliers in beta regression can severely impact parameter estimates and model performance, leading to predicted values that deviate significantly from actual observations. Detecting and managing these outliers is essential to ensure model reliability and accuracy. In this study, we propose three novel outlier detection methods: Tukey-Pearson Residual (TPR), Iterative Tukey-Pearson Residual (ITPR), and Iterative Tukey-MinMax Pearson Residual (ITMPR). These methods integrate the principles of Tukey’s boxplot with Pearson residuals, providing robust frameworks for detecting outliers in beta regression models. Extensive simulation studies and real-world data applications were conducted to evaluate their performance against existing outlier detection techniques in the literature. The results indicate that the ITPR method achieves the highest levels of precision and reliability, making it the most effective among the proposed methods. The TPR and ITMPR methods also exhibit strong performance, closely aligning with existing techniques. These findings highlight the potential of the proposed methods to enhance the robustness of beta regression analysis and its practical applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An expert system for modeling skill levels for corporate power relations in an entropy-based environment using SPIRIT 在基于熵的环境中使用SPIRIT对公司权力关系的技能水平进行建模的专家系统
Decision Analytics Journal Pub Date : 2025-03-01 DOI: 10.1016/j.dajour.2025.100556
Maximilian Schröer , Elmar Reucher
{"title":"An expert system for modeling skill levels for corporate power relations in an entropy-based environment using SPIRIT","authors":"Maximilian Schröer ,&nbsp;Elmar Reucher","doi":"10.1016/j.dajour.2025.100556","DOIUrl":"10.1016/j.dajour.2025.100556","url":null,"abstract":"<div><div>Collaboration and organizational structures in companies are changing, especially with the ‘New Way of Working’ post-COVID-19. From both a theoretical and practical perspective, a quantitative analysis of how skill levels evolve within the ‘New Way of Working’ provides valuable insights into power structures and potential changes. The expert system Shell-SPIRIT can be used to conduct these quantitative analyses. This system has already been applied to several articles to support faithful knowledge processing and to model power structures while measuring their power potentials. This article discusses the theoretical view and a case study about power structures modeled with an entropy-based approach using Shell-SPIRIT. Compared to previous studies, this article presents a novel approach that enhances realism by incorporating more variables. This means that participants’ skill levels are reflected directly in the quantitative model of quantitative power structures.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100556"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An extended simple additive weighting decision support system with application in the food industry 扩展的简单添加剂加权决策支持系统在食品工业中的应用
Decision Analytics Journal Pub Date : 2025-03-01 DOI: 10.1016/j.dajour.2025.100553
Peyman Zandi , Mehdi Ajalli , Narges Soleiman Ekhtiyati
{"title":"An extended simple additive weighting decision support system with application in the food industry","authors":"Peyman Zandi ,&nbsp;Mehdi Ajalli ,&nbsp;Narges Soleiman Ekhtiyati","doi":"10.1016/j.dajour.2025.100553","DOIUrl":"10.1016/j.dajour.2025.100553","url":null,"abstract":"<div><div>This study aims to expand the application of the multi-criteria decision-making (MCDM) technique based on expanded information on the alternatives from sub-alternatives. For this purpose, some initial information is collected at the sub-alternative level. Then, based on the scores obtained for the sub-alternative level, the main alternatives are ranked using the simple additive weighting (SAW) method. The goal is to analyze decision alternatives and sub-alternatives, rank the alternatives according to criteria and sub-criteria, and analyze sensitivity based on their criteria and weights. A program is developed in MS Excel to dynamically explore a large amount of information. The results confirm the designed model’s ability to rank all alternatives and sub-alternatives. The model has been used to rank 220 products and 12 product portfolios in a food industry company. Five categories of decision criteria, including production, procurement, finance, product and sales, and competitors, were selected with 36 quantitative and qualitative sub-criteria. The results show that the market indicators and competitors directly impact the product portfolio’s priority. Some of the contributions of this research can be considered as a method for ranking alternatives based on the expanded information from sub-alternatives. As a management tool, the proposed model can be used in other fields and with different techniques to manage the portfolio of alternatives and sub-alternatives.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100553"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A systematic review of simulation models in medicine supply chain management: Current state and emerging trends. 医药供应链管理仿真模型的系统综述:现状和新趋势。
Decision Analytics Journal Pub Date : 2025-02-21 DOI: 10.1016/j.dajour.2025.100555
Hellen Nabayiga, Robert Van Der Meer, Mouhamad Shaker Ali Agha
{"title":"A systematic review of simulation models in medicine supply chain management: Current state and emerging trends.","authors":"Hellen Nabayiga,&nbsp;Robert Van Der Meer,&nbsp;Mouhamad Shaker Ali Agha","doi":"10.1016/j.dajour.2025.100555","DOIUrl":"10.1016/j.dajour.2025.100555","url":null,"abstract":"<div><div>Simulation modelling has widely been applied in healthcare supply chain management, focusing on blood and vaccine supply chains with less attention on the medicine supply chains. This study presents a systematic review of studies applying simulation methods, namely agent-based modelling, discrete event simulation, and system dynamics, to address problems in the medicine supply chain. We adopt the Search, Appraisal, Synthesis, and Analysis (SALSA) approach to collect data from three databases (Scopus, Web of Science, and PubMed) from 2000 to 2023. 320 journal publications qualified for the initial screening and filtration and were extracted for further analysis. Only 31 studies met the inclusion criteria, with the first publication identified in 2010 and the last in 2023. The paper shows the usefulness of applying simulation in identifying medicine supply chain bottlenecks pertaining to stockouts (19%, n <span><math><mo>=</mo></math></span> 6), and falsified or counterfeit (16%, n <span><math><mo>=</mo></math></span> 5). System dynamics was the most applied approach with 42% (n <span><math><mo>=</mo></math></span> 13) and 6% (n <span><math><mo>=</mo></math></span> 2) employing a hybrid simulation approach. 32% (n <span><math><mo>=</mo></math></span> 10) of the studies reported verification and validation at either a conceptual or operational level with insufficient data from the real-world system reported as a challenge. The study suggests a gradually increasing interest in simulation applications in medicine supply chains informing decision-making. Combining multiple simulation approaches is recommended to address complex medicine supply chain issues, such as availability. In order to understand the usefulness of the model in decision-making, more effort is needed to validate developed models.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100555"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process 一个集成的人工智能和优化模型,以提高信用证审查过程的操作效率和降低风险
Decision Analytics Journal Pub Date : 2025-02-18 DOI: 10.1016/j.dajour.2025.100552
Mounaf Asaad Khalil, Majed Hadid, Regina Padmanabhan, Adel Elomri, Laoucine Kerbache
{"title":"An integrated Artificial Intelligence and optimization model for operational efficiency and risk reduction in Letter of Credit examination process","authors":"Mounaf Asaad Khalil,&nbsp;Majed Hadid,&nbsp;Regina Padmanabhan,&nbsp;Adel Elomri,&nbsp;Laoucine Kerbache","doi":"10.1016/j.dajour.2025.100552","DOIUrl":"10.1016/j.dajour.2025.100552","url":null,"abstract":"<div><div>Digital transformation in banking has significantly improved efficiency, including in the critical Letter of Credit (LC) examination area. Despite these advancements, LC examination remains complex, labor-intensive, and error-prone, leading to operational risks and inefficiencies. Integrating Artificial Intelligence (AI) offers a promising solution but requires human checkers to verify AI-generated decisions, ensuring accuracy and compliance. Assigning these verification tasks is essential to fully capitalize on AI’s potential, balancing time savings with risk reduction. This paper explores the underexamined challenge of optimizing the hybrid process of AI-assisted LC examination to enhance trade finance. The research aims to minimize examination risk and maximize checker capacity utilization by offering practical strategies for improvement. Through data-driven research collaboration with international banks and FinTech companies and benchmarking relevant literature, an Integer Linear Programming model was developed to assign review tasks for LC documents indexed by AI based on their criticality and discrepancies to human checkers. The model also considers monetary value, checker expertise, and availability factors. Real case studies evaluated improvements over baseline practices, objectives prioritization, trade-off analysis, and varying supply–demand scenarios. The model achieved a 68.3% reduction in operational risks, improving compliance and trustworthiness while minimizing errors and financial losses. Utilization rates ranged from 34% for risk-focused strategies to 73% for efficiency-driven approaches, providing flexibility for resource allocation aligned with organizational priorities. Using the proposed AI-optimization framework and results, the study offers actionable insights for managers and guidance for researchers.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100552"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions 综述了矮猫鼬优化算法的发展趋势和未来的研究方向
Decision Analytics Journal Pub Date : 2025-02-11 DOI: 10.1016/j.dajour.2025.100551
Olanrewaju Lawrence Abraham , Md Asri Ngadi
{"title":"A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions","authors":"Olanrewaju Lawrence Abraham ,&nbsp;Md Asri Ngadi","doi":"10.1016/j.dajour.2025.100551","DOIUrl":"10.1016/j.dajour.2025.100551","url":null,"abstract":"<div><div>The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various optimization challenges across multiple domains by balancing global and local searches, resulting in near-optimal solutions. Numerous DMO variants have been developed since its inception. A comprehensive survey of recent DMO research from 2022 to August 2024 is provided in this study, beginning with the natural inspiration and conceptual framework of the DMO. It then explores various modifications, hybridizations, and algorithm applications across different fields. Lastly, a meta-analysis of DMO advancements and potential directions for further research are provided.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100551"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A hybrid multi-objective optimization approach with NSGA-II for feature selection 基于NSGA-II的特征选择混合多目标优化方法
Decision Analytics Journal Pub Date : 2025-01-31 DOI: 10.1016/j.dajour.2025.100550
Praveen Vijai, Bagavathi Sivakumar P.
{"title":"A hybrid multi-objective optimization approach with NSGA-II for feature selection","authors":"Praveen Vijai,&nbsp;Bagavathi Sivakumar P.","doi":"10.1016/j.dajour.2025.100550","DOIUrl":"10.1016/j.dajour.2025.100550","url":null,"abstract":"<div><div>This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate the strengths of filter and wrapper methodologies to enhance the efficacy of addressing feature selection. The information gain, random forest, and relief F-based approach are used to evaluate the significance of features concerning the labels. Subsequently, the information derived from feature scoring is utilized to initialize the population. In addition, the work introduces a new operator for crossover and mutation that uses feature scores to guide these processes. This strategy improves the convergence efficiency and sharpens the search direction of the proposed model within the search space. As part of our empirical research, we compare the suggested model to three different multi-objective feature selection techniques on five different high-dimensional datasets. Our proposed model outperforms state-of-the-art algorithms, as shown by the empirical data. It achieves higher classification accuracy across a range of datasets and exhibits robustness in performance while substantially reducing the feature space.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100550"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143305938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems 求解多属性决策问题的全乘法数据包络分析模型
Decision Analytics Journal Pub Date : 2025-01-27 DOI: 10.1016/j.dajour.2025.100549
Narong Wichapa , Atchara Choompol , Ronnachai Sangmuenmao
{"title":"A novel Full Multiplicative Data Envelopment Analysis Model for solving Multi-Attribute Decision-Making problems","authors":"Narong Wichapa ,&nbsp;Atchara Choompol ,&nbsp;Ronnachai Sangmuenmao","doi":"10.1016/j.dajour.2025.100549","DOIUrl":"10.1016/j.dajour.2025.100549","url":null,"abstract":"<div><div>This study presents a novel Full Multiplicative Data Envelopment Analysis (FMDEA) for solving Multi-Attribute Decision-Making (MADM) problems. The proposed model offers an innovative approach to solving MADM problems by integrating the principles of Data Envelopment Analysis (DEA) with Full Multiplicative Form (FMF). This approach effectively addresses the significant limitations of traditional MADM methods, particularly concerning data normalization and computational complexity. We demonstrate the robustness and reliability of the proposed FMDEA model through its application across various decision-making scenarios. We demonstrate the perfect alignment of FMDEA with various decision-making scenarios, such as Multi-Objective Optimization with Full Multiplicative Form (MOOFMF), Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Aggregated Sum Product Assessment (WASPAS), and Complex Proportional Assessment (COPRAS) in flexible manufacturing. The model exhibited high correlations with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS. The FMDEA model consistently aligned with MOORA, MOOFMF, TOPSIS, WASPAS, and COPRAS in Computer Numerical Control (CNC) lathe selection. These results confirm the FMDEA model’s effectiveness in addressing MADM challenges by offering a simple, versatile, and user-friendly framework compatible with various optimization solvers, thus enhancing its practical applicability in complex decision-making contexts.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100549"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An investigation of supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles 对用于预测自动驾驶汽车驾驶员道德决策的监督机器学习模型的研究
Decision Analytics Journal Pub Date : 2025-01-22 DOI: 10.1016/j.dajour.2025.100548
Amandeep Singh, Yovela Murzello, Sushil Pokhrel, Siby Samuel
{"title":"An investigation of supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles","authors":"Amandeep Singh,&nbsp;Yovela Murzello,&nbsp;Sushil Pokhrel,&nbsp;Siby Samuel","doi":"10.1016/j.dajour.2025.100548","DOIUrl":"10.1016/j.dajour.2025.100548","url":null,"abstract":"<div><div>Vehicle-pedestrian interactions in autonomous vehicles (AVs) present complex challenges that require advanced decision-making algorithms. Understanding the factors influencing ethical decision-making (EDM) in critical situations is essential as AVs become more prevalent. This study addresses a gap in AV research by using predictive analytics methods to develop models that assess decision-making outcomes under varying time pressures. We recruited 204 participants from North America, aged 18-30 years and 65 years and above, for an online experiment. Participants viewed video clips from a driving simulator that simulated ethical dilemmas. They had to decide whether the AV should stay in its lane or change lanes by pressing the spacebar. The principal component analysis identified age, distraction, and trust in automation as the key factors influencing decision-making. Several machine learning models were optimized to predict decision outcomes, with the Gaussian Naive Bayes model demonstrating strong performance across different time pressures. Feature importance analysis highlighted the significant roles of age and trust in automation. Partial dependence plots illustrated the interaction between these factors and their influence on decision-making outcomes under time constraints. These findings contribute to the development of personalized decision-making algorithms for AVs. Predictive analytics provides valuable insights into improving AV systems’ safety, trust, and ethical behavior by accounting for individual differences in decision-making.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100548"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An outlier detection framework for Air Quality Index prediction using linear and ensemble models 使用线性和集合模型预测空气质量指数的离群值检测框架
Decision Analytics Journal Pub Date : 2025-01-16 DOI: 10.1016/j.dajour.2025.100546
Pradeep Kumar Dongre , Viral Patel , Upendra Bhoi , Nilesh N. Maltare
{"title":"An outlier detection framework for Air Quality Index prediction using linear and ensemble models","authors":"Pradeep Kumar Dongre ,&nbsp;Viral Patel ,&nbsp;Upendra Bhoi ,&nbsp;Nilesh N. Maltare","doi":"10.1016/j.dajour.2025.100546","DOIUrl":"10.1016/j.dajour.2025.100546","url":null,"abstract":"<div><div>The Air Quality Index (AQI) is a key indicator for assessing air quality and its associated health impacts. Accurate AQI calculations are crucial for reliable air quality assessments, but outliers in air quality data can distort these calculations, leading to inaccurate predictions. This paper presents a comprehensive framework for air quality prediction that integrates multiple outlier detection methods with machine learning models, focusing on enhancing the accuracy and robustness of predictions. The study investigates various outlier detection techniques, including the Interquartile Range (IQR), robust Z-score, and Mahalanobis distance, and evaluates their impact when integrated into machine learning models. Unlike traditional approaches that remove outliers without considering seasonal effects, this research proposes retaining extreme data points after seasonal validation to improve model generalization and prediction accuracy for unseen data. The framework is evaluated using a dataset from Jaipur city, testing multiple machine learning models, including linear regression, ensemble methods, and K-Nearest Neighbor (KNN) regression. Results show that the integrated framework significantly improves model performance, with the Extra Trees Regressor achieving the best results (MAE = 11.9161, RMSE = 16.1660, and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.8884) after refinement, compared to baseline performance (MAE = 12.6765, RMSE = 17.8452, and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> = 0.8737). This study demonstrates the empirical effectiveness of the proposed framework and provides practical guidelines for air quality prediction in real-world applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100546"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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