Data Science and Management最新文献_第7页

Factors influencing readiness for artificial intelligence: a systematic literature review 影响人工智能准备程度的因素：系统文献综述

Data Science and Management Pub Date : 2025-06-01 Epub Date: 2024-10-09 DOI: 10.1016/j.dsm.2024.09.005

Wajid Ali, Abdul Zahid Khan

{"title":"Factors influencing readiness for artificial intelligence: a systematic literature review","authors":"Wajid Ali, Abdul Zahid Khan","doi":"10.1016/j.dsm.2024.09.005","DOIUrl":"10.1016/j.dsm.2024.09.005","url":null,"abstract":"<div><div>Public-and private-sector organizations have adopted artificial intelligence (AI) to meet the challenges of the Fourth Industrial Revolution. The successful implementation of AI is a challenging task, and previous research has advocated the need to explore key readiness before AI implementation. The objective of this study is to identify the AI readiness factors explored by different authors in past research. To achieve this, we conducted a rigorous literature review. The approach used in the systematic literature review is also discussed. A rigorous review of 52 studies from various journals and databases (Science Direct, Springer Link, Institute of Electrical and Electronics Engineers, Emerald, and Google Scholar) identified 23 AI readiness factors. The key factors identified were mainly related to organizational information technology infrastructure, top management support, resource availability, collaborative culture, organizational size, organizational capability, compatibility, data quality, and financial budget, whereas the other 15 were potential factors in AI readiness. All of these factors should be considered before the implementation of AI in any organization. The findings also reflect a high failure rate, including AI readiness factors, which are intended to facilitate AI adoption in organizations and reduce the frequency of failures. These factors will aid management in developing an effective strategy for AI implementation in organizations.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 224-236"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263659","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

Triadic concept analysis for insights extraction from longitudinal studies in health 从健康纵向研究中提取见解的三合一概念分析

Data Science and Management Pub Date : 2025-06-01 Epub Date: 2024-10-12 DOI: 10.1016/j.dsm.2024.10.001

João Pedro Santos, Atílio Ferreira Silva, Henrique Fernandes Viana Mendes, Mark Alan Junho Song, Luis Enrique Zárate

{"title":"Triadic concept analysis for insights extraction from longitudinal studies in health","authors":"João Pedro Santos, Atílio Ferreira Silva, Henrique Fernandes Viana Mendes, Mark Alan Junho Song, Luis Enrique Zárate","doi":"10.1016/j.dsm.2024.10.001","DOIUrl":"10.1016/j.dsm.2024.10.001","url":null,"abstract":"<div><div>In the health field, longitudinal studies involve the recording of clinical observations of the same sample of patients over successive periods, referred to as waves. This type of database serves as a valuable source of information and insights, particularly when examining the temporal aspect, allowing the extraction of relevant and non-obvious knowledge. The triadic concept analysis theory has been proposed to describe the ternary relationships between objects, attributes, and conditions. In this study, we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules, which are similar to association rules but incorporate temporal relations. Through four case studies, we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns, enhance decision-making processes, and deepen our understanding of temporal dynamics. These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 160-173"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222283","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

Surprisal-based algorithm for detecting anomalies in categorical data 基于surprisar的分类数据异常检测算法

Data Science and Management Pub Date : 2025-06-01 Epub Date: 2025-01-29 DOI: 10.1016/j.dsm.2025.01.005

Ossama Cherkaoui, Houda Anoun, Abderrahim Maizate

{"title":"Surprisal-based algorithm for detecting anomalies in categorical data","authors":"Ossama Cherkaoui, Houda Anoun, Abderrahim Maizate","doi":"10.1016/j.dsm.2025.01.005","DOIUrl":"10.1016/j.dsm.2025.01.005","url":null,"abstract":"<div><div>Anomaly detection is an important research area in a diverse range of real-world applications. Although many algorithms have been proposed to address anomaly detection for numerical datasets, categorical and mixed datasets remain a significant challenge, primarily because a natural distance metric is lacking. Consequently, the methods proposed in the literature implement entirely different assumptions regarding the definition of categorical anomalies. This paper presents a novel categorical anomaly detection approach, offering two key contributions to existing methods. First, a novel surprisal-based anomaly score is introduced, which provides a more accurate assessment of anomalies by considering the full distribution of categorical values. Second, the proposed method considers complex correlations in the data beyond the pairwise interactions of features. This study proposed and tested the novel categorical surprisal anomaly detection algorithm (CSAD) by comparing and evaluating it against six competitors. The experimental results indicate that CSAD produced the best overall performance, achieving the highest average ROC-AUC and PR-AUC values of 0.8 and 0.443, respectively. Furthermore, CSAD's execution time is satisfactory even when processing large, high-dimensional datasets.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 185-195"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222282","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

Does the application of industrial robots reduce the intensity of CO2 emissions embodied in manufacturing exports? 工业机器人的应用是否降低了制造业出口所体现的二氧化碳排放强度？

Data Science and Management Pub Date : 2025-06-01 Epub Date: 2024-10-05 DOI: 10.1016/j.dsm.2024.09.003

Yaya Li , Yun Zhang , Xiaoli Wu

{"title":"Does the application of industrial robots reduce the intensity of CO2 emissions embodied in manufacturing exports?","authors":"Yaya Li , Yun Zhang , Xiaoli Wu","doi":"10.1016/j.dsm.2024.09.003","DOIUrl":"10.1016/j.dsm.2024.09.003","url":null,"abstract":"<div><div>Industrial robot application (IRA) provides an opportunity for the low-carbon development of trade. This study focuses on the green revolution of manufacturing export trade, analyzes the mechanism by which IRA affects CO<sub>2</sub> emissions embodied in manufacturing exports (CIE), and conducts an empirical test based on panel data from 37 countries from 2000 to 2019. This study found that first, IRA can significantly reduce CIE, but there is a U-shaped nexus between the two, which shows a rebound effect. Second, the heterogeneity test demonstrates that in comparison to both the low-tech and high-tech sectors, IRA in the medium-tech industry can significantly reduce CIE; compared with the low-IRA sectors, the high-IRA sectors exhibit a more obvious reduction. In addition, IRA has a stronger effect on high-carbon-intensity areas. Third, the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects. Moreover, environmental regulations and the manufacturing intelligence process positively moderate the nexus between IRA and CIE. Finally, these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 2","pages":"Pages 117-126"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139014","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

Forecast uncertainties real-time data-driven compensation scheme for optimal storage control 预测不确定性实时数据驱动的优化存储控制补偿方案

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2024-07-24 DOI: 10.1016/j.dsm.2024.07.002

Arbel Yaniv, Yuval Beck

{"title":"Forecast uncertainties real-time data-driven compensation scheme for optimal storage control","authors":"Arbel Yaniv, Yuval Beck","doi":"10.1016/j.dsm.2024.07.002","DOIUrl":"10.1016/j.dsm.2024.07.002","url":null,"abstract":"<div><div>This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 59-71"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846117","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

Comparative study of IoT- and AI-based computing disease detection approaches 基于物联网和人工智能的计算疾病检测方法比较研究

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2024-07-18 DOI: 10.1016/j.dsm.2024.07.004

Wasiur Rhmann , Jalaluddin Khan , Ghufran Ahmad Khan , Zubair Ashraf , Babita Pandey , Mohammad Ahmar Khan , Ashraf Ali , Amaan Ishrat , Abdulrahman Abdullah Alghamdi , Bilal Ahamad , Mohammad Khaja Shaik

{"title":"Comparative study of IoT- and AI-based computing disease detection approaches","authors":"Wasiur Rhmann , Jalaluddin Khan , Ghufran Ahmad Khan , Zubair Ashraf , Babita Pandey , Mohammad Ahmar Khan , Ashraf Ali , Amaan Ishrat , Abdulrahman Abdullah Alghamdi , Bilal Ahamad , Mohammad Khaja Shaik","doi":"10.1016/j.dsm.2024.07.004","DOIUrl":"10.1016/j.dsm.2024.07.004","url":null,"abstract":"<div><div>The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 94-106"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841404","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

Corrigendum regarding previously published articles 关于以前发表的文章的更正

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2025-01-17 DOI: 10.1016/j.dsm.2024.12.006

引用次数: 0

A model for predicting dropout of higher education students 预测高校学生辍学的模型

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2024-07-23 DOI: 10.1016/j.dsm.2024.07.001

Anaíle Mendes Rabelo, Luis Enrique Zárate

引用次数: 0

Enhancing cyber threat detection with an improved artificial neural network model 利用改进的人工神经网络模型加强网络威胁检测

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2024-06-01 DOI: 10.1016/j.dsm.2024.05.002

Toluwase Sunday Oyinloye , Micheal Olaolu Arowolo , Rajesh Prasad

引用次数: 0

Creating non-fungible token (NFT)-backed emoji art from user conversations on blockchain 从区块链上的用户对话中创建非可替换令牌（NFT）支持的表情符号艺术

Data Science and Management Pub Date : 2025-03-01 Epub Date: 2024-06-28 DOI: 10.1016/j.dsm.2024.06.002

Maedeh Mosharraf, Mohammad Hossein Khorrami

引用次数: 0