Decision Analytics Journal最新文献_第5页

A sequentially variant Blotto game with one-sided and incomplete information 具有片面和不完全信息的顺序变体布洛托博弈

Decision Analytics Journal Pub Date : 2024-10-19 DOI: 10.1016/j.dajour.2024.100524

Geofferey Jiyun Kim , Jerim Kim

引用次数: 0

A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research 用于评估医疗保健研究中深度学习模型的多标准决策分析框架

Decision Analytics Journal Pub Date : 2024-10-18 DOI: 10.1016/j.dajour.2024.100523

Nidal Drissi , Hadeel El-Kassabi , Mohamed Adel Serhani

{"title":"A multi-criteria decision analysis framework for evaluating deep learning models in healthcare research","authors":"Nidal Drissi , Hadeel El-Kassabi , Mohamed Adel Serhani","doi":"10.1016/j.dajour.2024.100523","DOIUrl":"10.1016/j.dajour.2024.100523","url":null,"abstract":"<div><div>Selecting the appropriate deep learning (DL) model for healthcare research poses a significant challenge due to the diversity of evaluation criteria and the complex nature of health-related tasks, where a single metric like accuracy is often insufficient. Motivated by the need for a structured, multi-criteria approach, this study proposes a Multi-Criteria Decision Analysis (MCDA) framework using the Analytic Hierarchy Process (AHP). Our primary contribution is the development of a comprehensive decision-making framework that integrates multiple evaluation criteria, such as accuracy, sensitivity, specificity, and computational complexity, alongside empirical data from existing literature to systematically compare DL models. The framework was validated through a use case involving the selection of the best DL model for diagnosing COVID-19 using X-ray images, where we compared eight popular models, including ResNet34, SqueezeNet, and AlexNet, and it was also evaluated through comparative scenarios using traditional methods, including weighted sum, weighted average, and accuracy-based evaluation. Quantitative results show that SqueezeNet achieved the highest score in the AHP framework (88.64), while ResNet34 performed best in traditional methods such as weighted sum (588.49) and accuracy ranking (98.33%). A sensitivity analysis further demonstrated the impact of varying criteria weights, showing how changes in the importance of accuracy and precision, influenced model ranking. These findings highlight the flexibility and robustness of the AHP framework in addressing the complexities of model selection in healthcare research. The implications of this work suggest that a structured, data-driven evaluation approach can provide more nuanced and reliable insights compared to traditional methods like single-metric evaluations, ultimately supporting more informed decision-making in healthcare applications.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100523"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554523","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 Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping 具有风险预测和因果映射功能的新型贝叶斯 "即付即开 "保险模型

Decision Analytics Journal Pub Date : 2024-10-03 DOI: 10.1016/j.dajour.2024.100522

Bingyang Wang , Ying Chen , Zichao Li

引用次数: 0

An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence 利用群体智能和蜂群智能解决货运整合车辆路线问题的精确和元智慧优化框架

Decision Analytics Journal Pub Date : 2024-09-19 DOI: 10.1016/j.dajour.2024.100517

Muhammad Khahfi Zuhanda , Hartono , Samsul A. Rahman Sidik Hasibuan , Yose Yefta Napitupulu

{"title":"An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence","authors":"Muhammad Khahfi Zuhanda , Hartono , Samsul A. Rahman Sidik Hasibuan , Yose Yefta Napitupulu","doi":"10.1016/j.dajour.2024.100517","DOIUrl":"10.1016/j.dajour.2024.100517","url":null,"abstract":"<div><div>The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimization techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322317","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 the literature on deep learning approaches for cross-domain recommender systems 关于跨域推荐系统深度学习方法的文献系统回顾

Decision Analytics Journal Pub Date : 2024-09-13 DOI: 10.1016/j.dajour.2024.100518

Matthew O. Ayemowa , Roliana Ibrahim , Yunusa Adamu Bena

{"title":"A systematic review of the literature on deep learning approaches for cross-domain recommender systems","authors":"Matthew O. Ayemowa , Roliana Ibrahim , Yunusa Adamu Bena","doi":"10.1016/j.dajour.2024.100518","DOIUrl":"10.1016/j.dajour.2024.100518","url":null,"abstract":"<div><div>The increase in online information and the expanding diversity of user preferences require developing improved recommender systems. Cross-domain recommender systems (CDRS) have emerged as a favorable solution to solve issues related to cold start, data sparsity, and diversity by leveraging knowledge from the source domains. This systematic literature review delves into the latest deep learning approaches utilized for CDRS, comprehensively analyzing state-of-the-art techniques, methodologies, metrics, datasets, and applications. We systematically review selected primary studies from popular databases covering sixty-eight publications from 2019 to March 2024. The review process involved selecting relevant studies based on the predefined inclusion and exclusion criteria to ensure the inclusion of high-quality research. Key deep learning (DL) models explored include neural collaborative filtering, convolutional neural networks, recurrent neural networks, variational autoencoder, and generative adversarial networks. We also examine the hybrid models that integrate DL with traditional machine learning techniques to enhance recommendation performance. Our findings reveal that DL approaches significantly improve accuracy, cold start, and data sparsity. This review also identifies current trends and future research directions, emphasizing the potential of Artificial Intelligence (AI), transfer learning, and reinforcement learning in advancing CDRS. In our analysis, we discovered that the domains mainly utilized are movies, books, and music, respectively, and the most widely used evaluation metrics are root mean square error (RMSE) and normalized discounted cumulative gain (NDCG). Research challenges and future scope are also highlighted to assist the researchers and practitioners seeking to develop robust cross-domain recommender systems using DL techniques.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322316","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 predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation 采用贝叶斯优化集合决策树的预测分析模型，增强作物推荐效果

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100516

Behnaz Motamedi, Balázs Villányi

{"title":"A predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation","authors":"Behnaz Motamedi, Balázs Villányi","doi":"10.1016/j.dajour.2024.100516","DOIUrl":"10.1016/j.dajour.2024.100516","url":null,"abstract":"<div><p>Researchers have been working on developing new effective, reliable, and environmentally friendly crop recommendation systems. This study introduces a thorough framework for predicting crop recommendations by combining sophisticated machine learning (ML) classifiers with a multi-class classification approach. The study is intended to (1) comprehensively assess the importance of environmental alterations and soil nutrient characteristics across a variety of crop classes, (2) develop effective predictive analytics models using the fine Gaussian support vector machine (FGSVM) and coarse k-nearest neighbors (Coa-KNN) algorithms, (3) reduce the dimension of feature vectors and minimize training time (FGPCASVM-CRP) approach through principal component analysis (PCA), (4) explore and analyze a Bayesian optimized ensemble decision tree for crop recommendation prediction (BOEDT-CRP) model based on assessment specifications, and (5) compare the proposed approach with multiple ML classifiers with various hyperparameter optimization, including FGSVM, coarse Gaussian SVM (Coa-GSVM), wide neural network (WNN), trilayered neural network (TNN), Fine k-nearest neighbors (FKNN), cosine k-nearest neighbors (Cos-KNN), bagged tree ensemble (BTE), and subspace discriminant ensemble (SDE). The proposed models throughout the training and testing stages reveal outstanding results, with comparable accuracy rates of 99.5%, precision rates of 99.49% and 99.55%, recall rates of 99.49% and 98.59%, and f1-scores of 99.5% and 99.54%. The findings support the conclusion that the proposed models can significantly support farmers in intelligent crop management and harvesting, leading to enhanced production and decreased reliance on human labor.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001206/pdfft?md5=5f5e03e9e96a689f6ea7001a74227777&pid=1-s2.0-S2772662224001206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147968","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 prescriptive analytics approach for evaluating two production systems: Simulation optimization algorithm 评估两个生产系统的规范性分析方法：模拟优化算法

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100513

Prashant Tiwari , David Kim , Ava Hajian , Amirehsan Ghasemi

{"title":"A prescriptive analytics approach for evaluating two production systems: Simulation optimization algorithm","authors":"Prashant Tiwari , David Kim , Ava Hajian , Amirehsan Ghasemi","doi":"10.1016/j.dajour.2024.100513","DOIUrl":"10.1016/j.dajour.2024.100513","url":null,"abstract":"<div><p>Production systems influence cost performance and carbon emissions. Environmental concerns compel companies to optimize energy efficiency in their production processes. This study explores the dilemma associated with fixed-time and fixed-lot systems during random disruptions and how these systems can improve performance. We employ simulation optimization models in business analytics using the discrete event simulation provided by the SimPy library within a Python environment. The study is based on the statistical analysis of data collected from 624,000 simulated hours. Our analysis reveals that a higher service level tilts the balance, favoring adopting a fixed-time production system in scenarios characterized by significant disruptions. A system with higher demand variability and lower standalone workstation availability (indicative of more variable production) tends to favor a fixed-time batch production approach. When workstations operate at low-capacity utilization combined with high standalone availability, the fixed-lot batch production system becomes more cost-effective. Overall, the fixed-time system demonstrates a superior capacity to accommodate higher production variability levels than the fixed-lot system. This paper contributes to the existing literature by providing simulation-optimization evidence to assess the relative efficiencies of fixed-size and fixed-time lot batch production systems. This paper considers the impact of random disruptions on operational efficiency within fixed-size lot batch production systems, highlighting the consequences of variability in lot completion times. The study also contributes to strategically selecting production systems to optimize energy usage in manufacturing processes.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100513"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001176/pdfft?md5=4fe2396960bfe74d95d0660370e931fc&pid=1-s2.0-S2772662224001176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095705","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 exploration of the concept of constrained improvement in data envelopment analysis 数据包络分析中受限改进概念的探讨

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100514

Nasim Arabjazi , Pourya Pourhejazy , Mohsen Rostamy-Malkhalifeh

{"title":"An exploration of the concept of constrained improvement in data envelopment analysis","authors":"Nasim Arabjazi , Pourya Pourhejazy , Mohsen Rostamy-Malkhalifeh","doi":"10.1016/j.dajour.2024.100514","DOIUrl":"10.1016/j.dajour.2024.100514","url":null,"abstract":"<div><p>Constrained improvement refers to regulating rivalry between companies in a particular industry by defining a framework or an evaluation mechanism. Such a mechanism results in a more equitable and healthy competitive environment. The primary motivation is that the best-performing players in a particular industry improve their performance such that the rest of the contenders remain competitive. This study investigates the concept of constrained improvement from a frontier analysis perspective, develops a systematic implementation framework, and explores a novel application of sensitivity analysis in Data Envelopment Analysis (DEA). Original programming approaches are developed to discover the stability region considering a variable returns to scale. The objective is to determine the extent to which the input and output of a decision-making unit (DMU) can be improved or worsened before the configuration of the efficient frontier changes. Furthermore, the permissible change radius for the decision-making unit is identified, considering all possible change directions. The applicability of the approach is demonstrated using numerical examples.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100514"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772662224001188/pdfft?md5=3fce8e207aba79fa112da1d52be9049f&pid=1-s2.0-S2772662224001188-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095817","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 performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland 爱尔兰共和国降雨预测机器学习模型的性能和可解释性评估

Decision Analytics Journal Pub Date : 2024-09-01 DOI: 10.1016/j.dajour.2024.100515

Menatallah Abdel Azeem, Soumyabrata Dev

{"title":"A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland","authors":"Menatallah Abdel Azeem, Soumyabrata Dev","doi":"10.1016/j.dajour.2024.100515","DOIUrl":"10.1016/j.dajour.2024.100515","url":null,"abstract":"<div><p>Rainfall prediction significantly impacts agriculture, water reserves, and preparations for flooding conditions. This research examines the performance and interpretability of machine learning (ML) models for rainfall prediction in the Republic of Ireland. The study uses a brute force approach and the Leave One Feature Out (LOFO) methodology to evaluate model performance under highly correlated variables. Results reveal consistent performance across ML algorithms, with average Area Under the Curve Precision–Recall (AUC-PR) scores ranging from 0.987 to 1.000, with certain features such as atmospheric pressure and soil moisture deficits demonstrating significant influence on prediction outcomes.SHapley Additive exPlanations (SHAP) values provide insights into feature importance, reaffirming the significance of atmospheric pressure and soil moisture deficits in rainfall prediction. This study underscores the importance of feature selection and interpretability in enhancing the accuracy and usability of ML models for rainfall prediction in Ireland.</p></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"12 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277266222400119X/pdfft?md5=17b64197d5e0eb48c92141637414cbe4&pid=1-s2.0-S277266222400119X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161899","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 generalization of the Topological Tail Dependence theory: From indices to individual stocks 拓扑尾部依赖理论的一般化：从指数到个股

Decision Analytics Journal Pub Date : 2024-08-26 DOI: 10.1016/j.dajour.2024.100512

Hugo Gobato Souto , Amir Moradi

引用次数: 0