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A survey of image labelling for computer vision applications 图像标记在计算机视觉中的应用综述
Journal of Business Analytics Pub Date : 2021-04-18 DOI: 10.1080/2573234X.2021.1908861
Christoph Sager, Christian Janiesch, Patrick Zschech
{"title":"A survey of image labelling for computer vision applications","authors":"Christoph Sager, Christian Janiesch, Patrick Zschech","doi":"10.1080/2573234X.2021.1908861","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1908861","url":null,"abstract":"ABSTRACT Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"19 1","pages":"91 - 110"},"PeriodicalIF":0.0,"publicationDate":"2021-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82668843","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: 35
Short-term prediction of opioid prescribing patterns for orthopaedic surgical procedures: a machine learning framework 骨科手术过程中阿片类药物处方模式的短期预测:机器学习框架
Journal of Business Analytics Pub Date : 2021-01-02 DOI: 10.1080/2573234X.2021.1873078
E. Mortaz, Ali Dağ, L. Hutzler, C. Gharibo, Lisa Anzisi, J. Bosco
{"title":"Short-term prediction of opioid prescribing patterns for orthopaedic surgical procedures: a machine learning framework","authors":"E. Mortaz, Ali Dağ, L. Hutzler, C. Gharibo, Lisa Anzisi, J. Bosco","doi":"10.1080/2573234X.2021.1873078","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1873078","url":null,"abstract":"ABSTRACT Overprescribing of opioids after surgical procedures can increase the risk of addiction in patients, and under prescribing can lead to poor quality of care. In this study, we propose a machine learning-based predictive framework to identify the varying effects of factors that are related to the opioid prescription amount after orthopaedic surgery. To predict the prescription classes, we train multiple classifiers combined with random and SMOTE over-sampling and weight-balancing techniques to cope with the imbalance state of the dataset. Our results show that the gradient boosting machines (XGB) with SMOTE achieve the highest classification accuracy. Our proposed analytical framework can be employed to assist and therefore, enable the surgeons to determine the timely changing effects of these variables.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"4 1","pages":"1 - 13"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75112932","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
Using Scouting Reports Text To Predict NCAA → NBA Performance 利用球探报告文本预测NCAA→NBA的表现
Journal of Business Analytics Pub Date : 2021-01-02 DOI: 10.1080/2573234X.2021.1873077
Philip Z. Maymin
{"title":"Using Scouting Reports Text To Predict NCAA → NBA Performance","authors":"Philip Z. Maymin","doi":"10.1080/2573234X.2021.1873077","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1873077","url":null,"abstract":"ABSTRACT Draft decisions by National Basketball Association (NBA) teams are notoriously poor. Analytics can help but are often dismissed for being too overfit, complex, risky, and incomplete. To address these concerns, we train separate leave-one-out random forests machine learning models for each collegiate NBA prospect from 2006 through 2019 with a conservative utility function on a novel comprehensive dataset including the raw text of scouting reports, combine measurements, on-court stats, mock draft placements, and more. Despite being unable to draft high school or international players, the resulting model outperforms the actual decisions of all but one NBA team, with an average gain of $100 million. Target shuffling shows that the model does not overfit and feature shuffling shows that handedness and ESPN mock draft rating, but not other mock drafts, are most important. NBA teams may be missing value by not following a disciplined, model-driven, prescriptive analytics approach to decision making.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"12 1","pages":"40 - 54"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78907326","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
Classifying insincere questions on Question Answering (QA) websites: meta-textual features and word embedding 问答网站上不真实问题的分类:元文本特征和词嵌入
Journal of Business Analytics Pub Date : 2021-01-02 DOI: 10.1080/2573234X.2021.1895681
M. Al-Ramahi, I. Alsmadi
{"title":"Classifying insincere questions on Question Answering (QA) websites: meta-textual features and word embedding","authors":"M. Al-Ramahi, I. Alsmadi","doi":"10.1080/2573234X.2021.1895681","DOIUrl":"https://doi.org/10.1080/2573234X.2021.1895681","url":null,"abstract":"ABSTRACT The power of information and information exchange defines the current Internet and Online Social Networks (OSNs). With such power and influence, individuals and entities expose those networks to different types of false information. This paper proposes several classification models based on Quora insincere questions; a dataset released by Kaggle. We evaluated several models including word embeddings based on meta and word-level features. Best results were achieved using the BERT transformer with an overall accuracy of more than 95% on several individual classifiers. Overall, results indicated that the meta-textual features are important predictors for whether a question is sincere or not. In one implication, we noticed that users are putting more cognitive efforts into writing more readable sincere questions compared to insincere questions. Moreover, a dictionary is assembled from several explicit dictionaries and significant words selected from Quora questions. The dictionary showed a good performance in predicting insincere questions.","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"30 1","pages":"55 - 66"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83845999","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: 2
Classification with Supervised Learning Methods 基于监督学习方法的分类
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_11
W. Paczkowski
{"title":"Classification with Supervised Learning Methods","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_11","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_11","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91367525","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
Time Series Analysis 时间序列分析
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_7
W. Paczkowski
{"title":"Time Series Analysis","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_7","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_7","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85898412","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
Data Visualization: The Basics 数据可视化:基础
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_4
W. Paczkowski
{"title":"Data Visualization: The Basics","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_4","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_4","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78736729","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
Advanced Data Handling for Business Data Analytics 高级数据处理业务数据分析
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_9
W. Paczkowski
{"title":"Advanced Data Handling for Business Data Analytics","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_9","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_9","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84854562","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
Basic Data Handling 基本数据处理
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_3
W. Paczkowski
{"title":"Basic Data Handling","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_3","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_3","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81595122","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
Data Sources, Organization, and Structures 数据源、组织和结构
Journal of Business Analytics Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-87023-2_2
W. Paczkowski
{"title":"Data Sources, Organization, and Structures","authors":"W. Paczkowski","doi":"10.1007/978-3-030-87023-2_2","DOIUrl":"https://doi.org/10.1007/978-3-030-87023-2_2","url":null,"abstract":"","PeriodicalId":36417,"journal":{"name":"Journal of Business Analytics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88582673","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
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