Data Science and Management最新文献

{"title":"Development of a machine learning model for predicting abnormalities of commercial airplanes","authors":"","doi":"10.1016/j.dsm.2024.03.002","DOIUrl":"10.1016/j.dsm.2024.03.002","url":null,"abstract":"<div><p>Airplanes are a social necessity for movement of humans, goods, and other. They are generally safe modes of transportation; however, incidents and accidents occasionally occur. To prevent aviation accidents, it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data. This study combined data-quality detection, anomaly detection, and abnormality-classification-model development. The research methodology involved the following stages: problem statement, data selection and labeling, prediction-model development, deployment, and testing. The data labeling process was based on the rules framed by the international civil aviation organization for commercial, jet-engine flights and validated by expert commercial pilots. The results showed that the best prediction model, the quadratic-discriminant-analysis, was 93% accurate, indicating a “good fit”. Moreover, the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96, respectively, thus confirming its “good fit”.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 256-265"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000146/pdfft?md5=72d22c62c77d91a47de3980ce379bce3&pid=1-s2.0-S2666764924000146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140270485","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}

{"title":"Original optimal method to solve the all-pairs shortest path problem: Dhouib-matrix-ALL-SPP","authors":"Souhail Dhouib","doi":"10.1016/j.dsm.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.dsm.2024.01.005","url":null,"abstract":"<div><p>The All-pairs shortest path problem (ALL-SPP) aims to find the shortest path joining all the vertices in a given graph. This study proposed a new optimal method, Dhouib-matrix-ALL-SPP (DM-ALL-SPP) to solve the ALL-SPP based on column-row navigation through the adjacency matrix. DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges. Even for graphs with a negative cycle, DM-ALL-SPP reported a negative cycle. In addition, DM-ALL-SPP continues to work for directed, undirected and mixed graphs. Furthermore, it is characterized by two phases: the first phase consists of adding by column repeated (<em>n</em>) iterations (where <em>n</em> is the number of vertices), and the second phase resides in adding by row executed in the worst case <em>(n∗log(n))</em> iterations. The first phase, focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value. The second phase is emphasized by rows only for the elements modified in the first phase. Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method, which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 206-217"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000109/pdfft?md5=d538a0a331fded270406098b5f8fd6f2&pid=1-s2.0-S2666764924000109-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141243490","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}

{"title":"Engaging in sports via the metaverse? an examination through analysis of metaverse research trends in sports","authors":"Ahyun Kim ,&nbsp;Sang-Soo Kim","doi":"10.1016/j.dsm.2024.01.002","DOIUrl":"10.1016/j.dsm.2024.01.002","url":null,"abstract":"<div><p>In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse,” “augmented reality,” “virtual reality,” “lifelogging,” and “mixed reality.” First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog (y/ies)”,“experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious; however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 181-188"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266676492400002X/pdfft?md5=116807c54c64af0697387e036e1948c7&pid=1-s2.0-S266676492400002X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139633801","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}

{"title":"Machine acceleration time series prediction for dimensional accuracy of 3D printed parts","authors":"","doi":"10.1016/j.dsm.2024.02.002","DOIUrl":"10.1016/j.dsm.2024.02.002","url":null,"abstract":"<div><p>This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional (3D) printing, particularly focusing on extrusion technology. Our primary objective was to develop a long short-term memory (LSTM) network capable of assessing this impact. We conducted an extensive analysis involving 12 distinct infill patterns, collecting time-series data to examine their effects on the acceleration of the printer’s bed. The LSTM network was trained using acceleration data from the adaptive cubic infill pattern, while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy. This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model. Specifically, the LSTM model was devised to predict the acceleration of a fused deposition modeling (FDM) printer using data from the adaptive cubic infill pattern. Rigorous testing yielded a root mean square error (RMSE) of 0.007144, reflecting the model’s precision. Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern, resulting in an RMSE of 0.007328. Notably, the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network (RNN) in predicting machine acceleration data. The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 3","pages":"Pages 218-227"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764924000122/pdfft?md5=5279d6a024ea6a759468bdafb34bcc56&pid=1-s2.0-S2666764924000122-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463746","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}

{"title":"Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets","authors":"Qing Zhu ,&nbsp;Chenyu Han ,&nbsp;Yuze Li","doi":"10.1016/j.dsm.2024.07.003","DOIUrl":"10.1016/j.dsm.2024.07.003","url":null,"abstract":"<div><div>Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851279","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}

{"title":"Hybrid scientific article recommendation system with COOT optimization","authors":"R. Sivasankari,&nbsp;J. Dhilipan","doi":"10.1016/j.dsm.2023.11.002","DOIUrl":"10.1016/j.dsm.2023.11.002","url":null,"abstract":"<div><p>Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and Mean Reciprocal Rank (MRR) by 5.7%.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 99-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000516/pdfft?md5=a8f578b0b252fb9a7cc519cb31df8416&pid=1-s2.0-S2666764923000516-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614110","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}

{"title":"Topic prevalence and trends of metaverse in healthcare: A bibliometric analysis","authors":"Pei Wu ,&nbsp;Donghua Chen ,&nbsp;Runtong Zhang","doi":"10.1016/j.dsm.2023.12.003","DOIUrl":"10.1016/j.dsm.2023.12.003","url":null,"abstract":"<div><p>Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare; in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 129-143"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000607/pdfft?md5=4a86705d550b96068560f4c1c4c8bbee&pid=1-s2.0-S2666764923000607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195946","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}

{"title":"Data- and management-driven metaverse research","authors":"Zhigeng Pan ,&nbsp;Jiaqi Yan ,&nbsp;Hirotoshi Takeda ,&nbsp;Haibing Lu ,&nbsp;Shan Liu ,&nbsp;Wei Huang ,&nbsp;Jian Mou ,&nbsp;James Christopher Westland","doi":"10.1016/j.dsm.2023.11.003","DOIUrl":"10.1016/j.dsm.2023.11.003","url":null,"abstract":"<div><p>The metaverse has become a very important phenomenon in society because of the emergence of new technologies. The widespread adoption of the metaverse has generated significant discussions about the challenges and opportunities it presents. We invited three panelists to present their personal viewpoints on the metaverse in the 2022 AIS-SIG-ISAP Workshop on Information Systems in Asia-Pacific (ISAP). The discussion indicated that metaverse research is being conducted. Furthermore, it highlighted new research directions and offered research topics related to the advantages or disadvantages of the metaverse. The proposed research topics will offer new insights to academics and practitioners.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 75-78"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000528/pdfft?md5=6125369c75ab32b2ac7de63a7009367c&pid=1-s2.0-S2666764923000528-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139299444","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}

{"title":"Using multi-criteria decision-making and machine learning for football player selection and performance prediction: A systematic review","authors":"Abdessatar Ati ,&nbsp;Patrick Bouchet ,&nbsp;Roukaya Ben Jeddou","doi":"10.1016/j.dsm.2023.11.001","DOIUrl":"10.1016/j.dsm.2023.11.001","url":null,"abstract":"<div><p>Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 79-88"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000504/pdfft?md5=4dfe252f3db079e14d75e256bf48da67&pid=1-s2.0-S2666764923000504-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614979","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}

{"title":"Secure approach to sharing digitized medical data in a cloud environment","authors":"Kukatlapalli Pradeep Kumar ,&nbsp;Boppuru Rudra Prathap ,&nbsp;Michael Moses Thiruthuvanathan ,&nbsp;Hari Murthy ,&nbsp;Vinay Jha Pillai","doi":"10.1016/j.dsm.2023.12.001","DOIUrl":"10.1016/j.dsm.2023.12.001","url":null,"abstract":"<div><p>Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"7 2","pages":"Pages 108-118"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764923000589/pdfft?md5=7be31440af8c8ec0561c9c1630c7620b&pid=1-s2.0-S2666764923000589-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139018223","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}