Data Science and Management最新文献_第7页

A trajectory data warehouse solution for workforce management decision-making 用于劳动力管理决策的轨迹数据仓库解决方案

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.002

Georgia Garani, Dimitrios Tolis, Ilias K. Savvas

{"title":"A trajectory data warehouse solution for workforce management decision-making","authors":"Georgia Garani, Dimitrios Tolis, Ilias K. Savvas","doi":"10.1016/j.dsm.2023.03.002","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.03.002","url":null,"abstract":"<div><p>In modern workforce management, the demand for new ways to maximize worker satisfaction, productivity, and security levels is endless. Workforce movement data such as those source data from an access control system can support this ongoing process with subsequent analysis. In this study, a solution to attaining this goal is proposed, based on the design and implementation of a data mart as part of a dimensional trajectory data warehouse (TDW) that acts as a repository for the management of movement data. A novel methodological approach is proposed for modeling multiple spatial and temporal dimensions in a logical model. The case study presented in this paper for modeling and analyzing workforce movement data is to support human resource management decision-making and the following discussion provides a representative example of the contribution of a TDW in the process of information management and decision support systems. The entire process of exporting, cleaning, consolidating, and transforming data is implemented to achieve an appropriate format for final import. Structured query language (SQL) queries demonstrate the convenience of dimensional design for data analysis, and valuable information can be extracted from the movements of employees on company premises to manage the workforce efficiently and effectively. Visual analytics through data visualization support the analysis and facilitate decision-making and business intelligence.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 2","pages":"Pages 88-97"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning 与基于深度学习的新冠肺炎分类相关的挑战、机遇和进展

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.005

Abhishek Agnihotri, Narendra Kohli

{"title":"Challenges, opportunities, and advances related to COVID-19 classification based on deep learning","authors":"Abhishek Agnihotri, Narendra Kohli","doi":"10.1016/j.dsm.2023.03.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.03.005","url":null,"abstract":"<div><p>The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches highlighted a future research possibility.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 2","pages":"Pages 98-109"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hotspots and trends of environmental, social and governance (ESG) research: a bibliometric analysis 环境、社会与治理(ESG)研究的热点与趋势:文献计量分析

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.001

Guochao Wan , Ahmad Yahya Dawod , Somsak Chanaim , Siva Shankar Ramasamy

引用次数: 4

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management 一种机器学习方法，利用震级和后果的层次来形成地震类别，以指导应急管理

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.06.005

D. Atsa’am, T. Gbaden, R. Wario

引用次数: 0

Cross-border issues and technology and management solutions during COVID-19 COVID-19期间的跨境问题以及技术和管理解决方案

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.03.004

Jae Kyu Lee , Shengsheng Huang , Yasin Ceran , Haibing Lu , Shan Liu , Wei Huang , Jian Mou

引用次数: 0

Network science approach for identifying disruptive elements of an airline 识别航空公司干扰因素的网络科学方法

Data Science and Management Pub Date : 2023-06-01 DOI: 10.1016/j.dsm.2023.04.001

Vinod Kumar Chauhan , Anna Ledwoch , Alexandra Brintrup , Manuel Herrera , Vaggelis Giannikas , Goran Stojkovic , Duncan Mcfarlane

{"title":"Network science approach for identifying disruptive elements of an airline","authors":"Vinod Kumar Chauhan , Anna Ledwoch , Alexandra Brintrup , Manuel Herrera , Vaggelis Giannikas , Goran Stojkovic , Duncan Mcfarlane","doi":"10.1016/j.dsm.2023.04.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.04.001","url":null,"abstract":"<div><p>Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines’ reputations, waste passengers’ time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 2","pages":"Pages 110-121"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting hourly PM2.5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms 基于结合深度学习算法的分解-集成-重建框架的每小时PM2.5浓度预测

Data Science and Management Pub Date : 2023-03-01 DOI: 10.1016/j.dsm.2023.02.002

Peilei Cai, Chengyuan Zhang, Jian Chai

{"title":"Forecasting hourly PM2.5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms","authors":"Peilei Cai, Chengyuan Zhang, Jian Chai","doi":"10.1016/j.dsm.2023.02.002","DOIUrl":"https://doi.org/10.1016/j.dsm.2023.02.002","url":null,"abstract":"<div><p>Accurate predictions of hourly PM<sub>2.5</sub> concentrations are crucial for preventing the harmful effects of air pollution. In this study, a new decomposition-ensemble framework incorporating the variational mode decomposition method (VMD), econometric forecasting method (autoregressive integrated moving average model, ARIMA), and deep learning techniques (convolutional neural networks (CNN) and temporal convolutional network (TCN)) was developed to model the data characteristics of hourly PM<sub>2.5</sub> concentrations. Taking the PM<sub>2.5</sub> concentration of Lanzhou, Gansu Province, China as the sample, the empirical results demonstrated that the developed decomposition-ensemble framework is significantly superior to the benchmarks with the econometric model, machine learning models, basic deep learning models, and traditional decomposition-ensemble models, within one-, two-, or three-step-ahead. This study verified the effectiveness of the new prediction framework to capture the data patterns of PM<sub>2.5</sub> concentration and can be employed as a meaningful PM<sub>2.5</sub> concentrations prediction tool.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 1","pages":"Pages 46-54"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Cross-border electronic commerce’s new path: from literature review to AI text generation 跨境电子商务的新路径：从文献综述到人工智能文本生成

Data Science and Management Pub Date : 2023-03-01 DOI: 10.1016/j.dsm.2022.12.001

Qing Zhu , Yinglin Ruan , Shan Liu , Sung-Byung Yang , Lin Wang , Jianhua Che

引用次数: 0

Application of support vector machine algorithm for early differential diagnosis of prostate cancer 支持向量机算法在前列腺癌早期鉴别诊断中的应用

Data Science and Management Pub Date : 2023-03-01 DOI: 10.1016/j.dsm.2022.10.001

Boluwaji A. Akinnuwesi , Kehinde A. Olayanju , Benjamin S. Aribisala , Stephen G. Fashoto , Elliot Mbunge , Moses Okpeku , Patrick Owate

{"title":"Application of support vector machine algorithm for early differential diagnosis of prostate cancer","authors":"Boluwaji A. Akinnuwesi , Kehinde A. Olayanju , Benjamin S. Aribisala , Stephen G. Fashoto , Elliot Mbunge , Moses Okpeku , Patrick Owate","doi":"10.1016/j.dsm.2022.10.001","DOIUrl":"https://doi.org/10.1016/j.dsm.2022.10.001","url":null,"abstract":"<div><p>Prostate cancer (PCa) symptoms are commonly confused with benign prostate hyperplasia (BPH), particularly in the early stages due to similarities between symptoms, and in some instances, underdiagnoses. Clinical methods have been utilized to diagnose PCa; however, at the full-blown stage, clinical methods usually present high risks of complicated side effects. Therefore, we proposed the use of support vector machine for early differential diagnosis of PCa (SVM-PCa-EDD). SVM was used to classify persons with and without PCa. We used the PCa dataset from the Kaggle Healthcare repository to develop and validate SVM model for classification. The PCa dataset consisted of 250 features and one class of features. Attributes considered in this study were age, body mass index (BMI), race, family history, obesity, trouble urinating, urine stream force, blood in semen, bone pain, and erectile dysfunction. The SVM-PCa-EDD was used for preprocessing the PCa dataset, specifically dealing with class imbalance, and for dimensionality reduction. After eliminating class imbalance, the area under the receiver operating characteristic (ROC) curve (AUC) of the logistic regression (LR) model trained with the downsampled dataset was 58.4%, whereas that of the AUC-ROC of LR trained with the class imbalance dataset was 54.3%. The SVM-PCa-EDD achieved 90% accuracy, 80% sensitivity, and 80% specificity. The validation of SVM-PCa-EDD using random forest and LR showed that SVM-PCa-EDD performed better in early differential diagnosis of PCa. The proposed model can assist medical experts in early diagnosis of PCa, particularly in resource-constrained healthcare settings and making further recommendations for PCa testing and treatment.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"6 1","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49765234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

The influence of e-commerce live streaming affordance on consumer’s gift-giving and purchase intention 电商直播对消费者送礼和购买意愿的影响

Data Science and Management Pub Date : 2023-03-01 DOI: 10.1016/j.dsm.2022.10.002

Yunfan Lu, Yucheng He, Yifei Ke

引用次数: 5