Decision Analytics Journal最新文献_第8页

A novel predictive analytics model for forecasting short-term trends in equity assets prices 一种新的预测分析模型，用于预测股票资产价格的短期趋势

Decision Analytics Journal Pub Date : 2024-12-20 DOI: 10.1016/j.dajour.2024.100534

Fabián Achury-Calderón , John A. Arredondo , Leidy Catherinne Sánchez Ascanio

引用次数: 0

Asymmetric impacts of the World Uncertainty Index and exchange rates on tourism using non-linear Autoregressive Distributed Lag models 基于非线性自回归分布滞后模型的世界不确定性指数和汇率对旅游业的不对称影响

Decision Analytics Journal Pub Date : 2024-12-10 DOI: 10.1016/j.dajour.2024.100530

Xuefeng Zhang , Yueyi Chen , Xiangqing Lu , Woraphon Yamaka

{"title":"Asymmetric impacts of the World Uncertainty Index and exchange rates on tourism using non-linear Autoregressive Distributed Lag models","authors":"Xuefeng Zhang , Yueyi Chen , Xiangqing Lu , Woraphon Yamaka","doi":"10.1016/j.dajour.2024.100530","DOIUrl":"10.1016/j.dajour.2024.100530","url":null,"abstract":"<div><div>The tourism industry is highly susceptible to global economic fluctuations and uncertainties. Understanding the asymmetric effects of macroeconomic factors on tourism demand is crucial for developing effective policies and strategies in the sector. This study examines the asymmetric impacts of the World Uncertainty Index (WUI) and exchange rates on outbound tourism demand from Thailand to five Association of Southeast Asian Nations (ASEAN) destinations. We employ linear and non-linear Autoregressive Distributed Lag (ARDL) models using quarterly data from 2003Q1 to 2023Q4. The results demonstrate that the asymmetric ARDL model outperforms its linear counterpart, revealing significant non-linear dynamics in tourism demand. We find that Thai outbound tourism responds more strongly to currency depreciations in destination countries than to appreciations. This asymmetry aligns with prospect theory, suggesting that tourists are more sensitive to potential cost savings than equivalent increases. The study also shows that increases in global economic uncertainty, as measured by the WUI, have a more pronounced negative impact on tourism flows than decreases in uncertainty. These findings have important implications for policymakers and tourism stakeholders in ASEAN countries. They highlight the need for tailored strategies to capitalize on favorable exchange rate movements and mitigate the impacts of global economic uncertainty on regional tourism flows.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100530"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136009","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 machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires 森林大火点火和严重程度的机器学习预测模型：澳大利亚夏季黑色森林大火的研究

Decision Analytics Journal Pub Date : 2024-12-06 DOI: 10.1016/j.dajour.2024.100529

Kasinda Henderson , Ripon K. Chakrabortty

{"title":"A machine learning predictive model for bushfire ignition and severity: The Study of Australian black summer bushfires","authors":"Kasinda Henderson , Ripon K. Chakrabortty","doi":"10.1016/j.dajour.2024.100529","DOIUrl":"10.1016/j.dajour.2024.100529","url":null,"abstract":"<div><div>Australian bushfires are catastrophic, and their impacts span social, economic, and environmental factors. To reduce the damages experienced by bushfires, predicting Australian bushfire ignition allows for an early warning system to give first responders and disaster managers prompt and accurate information. Traditional methods of bushfire ignition prediction suffer from incorporating large-dimensional data and take extensive computational time. Applying machine learning (ML) models enhances accuracy and reduces the computational time required to predict bushfire ignition. This study proposes a predictive model that can take meteorological and topographical data and determine the probability of Australian bushfire ignition and severity using historical fire detection gathered from the Black Summer Bushfire Disaster. The Black Summer Bushfire Disaster occurred between December 2019 and February 2020. The fires affected numerous towns throughout Victoria, New South Wales, and the Australian Capital Territory; hence, the varying topographical and meteorological conditions allow fire ignition and severity influences to be explored. The proposed methodology incorporates Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Convolutional Neural Networks (CNN), and K-nearest Neighbour (kNN) Algorithms. The proposed method relies on five datasets. Meteorological data is sourced from the Bureau of Meteorology (BOM), Australia. Topographic data is sourced from Geoscience Australia and the National Aeronautics and Space Administration’s (NASA’s) Aqua and Terra satellites, which utilize a Moderate Resolution Imaging Spectroradiometer (MODIS). Active Fire Point Data is also sourced from NASA MODIS, which can detect fires. The proposed methodology aims to act as an early warning system by providing a fire occurrence and fire intensity warning map and the probability of fire occurrence and fire intensity depending on the current meteorological climate.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"14 ","pages":"Article 100529"},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136011","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 evaluation of machine learning and deep learning algorithms for wind speed and power prediction 综合评估用于风速和功率预测的机器学习和深度学习算法

Decision Analytics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.dajour.2024.100527

Haytham Elmousalami , Hadi Hesham Elmesalami , Mina Maxi , Ahmed Abdel Kader Mohamed Farid , Nehal Elshaboury

{"title":"A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction","authors":"Haytham Elmousalami , Hadi Hesham Elmesalami , Mina Maxi , Ahmed Abdel Kader Mohamed Farid , Nehal Elshaboury","doi":"10.1016/j.dajour.2024.100527","DOIUrl":"10.1016/j.dajour.2024.100527","url":null,"abstract":"<div><div>Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100527"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168124","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 prescriptive action plan development model for information systems through project prioritization and roadmap planning 通过项目优先排序和路线图规划，为信息系统提供一个全面的说明性行动计划开发模型

Decision Analytics Journal Pub Date : 2024-12-01 DOI: 10.1016/j.dajour.2024.100528

Nicolas Ribeiro Pires de Sousa , João Eduardo Quintela Alves de Sousa Varajão

{"title":"A comprehensive prescriptive action plan development model for information systems through project prioritization and roadmap planning","authors":"Nicolas Ribeiro Pires de Sousa , João Eduardo Quintela Alves de Sousa Varajão","doi":"10.1016/j.dajour.2024.100528","DOIUrl":"10.1016/j.dajour.2024.100528","url":null,"abstract":"<div><div>Information technology/information systems investments are critical for business success. However, managers often face the challenge of prioritizing and selecting projects that better satisfy their needs, mainly due to the high number of options available and limited resources. Since investment decisions can strongly impact the organizational path, well-defined processes are required to guide them. At present, there is a lack of clarity about how organizations should proceed in information systems planning since most of the research literature focuses on decision models and criteria that only produce a prioritized list of initiatives without addressing the process and the subsequent need to define a plan for project implementation. In this article, we propose a new method for supporting the definition of information systems action plans resulting from a design science research project carried out in a public organization. We contribute to the theory and practice by providing a method that outlines the process comprehensively and describes the definition and application of a straightforward decision model for project prioritization in a real case, aiming to assist managers in conducting an efficient decision and prioritization of information technology investments.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100528"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168126","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 artificial intelligence multitier system with lightweight classifier for automated helmetless biker detection 带轻量级分类器的人工智能多层系统，用于自动检测无头盔骑车人

Decision Analytics Journal Pub Date : 2024-11-26 DOI: 10.1016/j.dajour.2024.100526

Nur Mohammad Fahad , Sadman Sakib , Md. Ibrahim Ratul , Md. Jakarea , Abdul Karim Ibne Mohon , Shahinur Alam Bhuiyan , Md. Reduan Sarker

{"title":"An artificial intelligence multitier system with lightweight classifier for automated helmetless biker detection","authors":"Nur Mohammad Fahad , Sadman Sakib , Md. Ibrahim Ratul , Md. Jakarea , Abdul Karim Ibne Mohon , Shahinur Alam Bhuiyan , Md. Reduan Sarker","doi":"10.1016/j.dajour.2024.100526","DOIUrl":"10.1016/j.dajour.2024.100526","url":null,"abstract":"<div><div>Bike accidents on roads have become a significant concern nowadays. People suffer due to the tragic consequences of road accidents due to the reluctance of bike riders to wear proper helmets, reflecting their lack of awareness, leading to fatalities. This study addresses classification and detection tasks by constructing three custom datasets to identify and categorize bikers riding with or without helmets and assess helmet quality. Photometric data augmentation is applied to balance the dataset images. The primary goal of this study is to develop a computationally efficient convolutional neural network (CNN)-based model named ‘BikeNet-12’ for accurate classification tasks. Various performance metrics are employed to evaluate the model’s overall effectiveness. The BikeNet-12 model achieves a test accuracy of 99.32% in dataset 1. The effectiveness of the proposed approach is validated by experiments on a Safety Helmet Classifier dataset Performance comparison with various transfer learning models demonstrates the applicability of the model in terms of performance metrics. Dataset 2 is utilized to assess helmet quality as safe, unsafe, or inappropriate, and the model achieves the highest accuracy of 98.93%, showcasing its efficacy. Additionally, dataset 3 is employed with the YOLOv8 model to detect non-helmet headwear, such as caps, hijabs, and turbans, among riders, yielding satisfactory results with a mean average precision of 93.7%. Integrating classification and detection tasks positions the model as a potential application to enhance biker safety, promote precaution, and contribute to increased sustainability.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100526"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721451","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 multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times 一种多目标生产调度模型和动态调度规则，适用于设置时间取决于顺序的不相关并行机器

Decision Analytics Journal Pub Date : 2024-11-04 DOI: 10.1016/j.dajour.2024.100525

Pham Duc Tai, Papimol Kongsri, Prasal Soeurn, Jirachai Buddhakulsomsiri

{"title":"A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times","authors":"Pham Duc Tai, Papimol Kongsri, Prasal Soeurn, Jirachai Buddhakulsomsiri","doi":"10.1016/j.dajour.2024.100525","DOIUrl":"10.1016/j.dajour.2024.100525","url":null,"abstract":"<div><div>This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented <span><math><mi>ɛ</mi></math></span>-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others.</div></div>","PeriodicalId":100357,"journal":{"name":"Decision Analytics Journal","volume":"13 ","pages":"Article 100525"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662863","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 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