Business & Information Systems Engineering最新文献_第2页

Predict COVID-19 Spreading With C-SMOTE 用C-SMOTE预测COVID-19的传播

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-07-02 DOI: 10.52825/bis.v1i.45

Alessio Bernardo, Emanuele Della Valle

引用次数: 0

An Integrated Group Decision-Making Approach Considering Uncertainty Conditions 考虑不确定性条件的综合群体决策方法

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-07-02 DOI: 10.52825/bis.v1i.52

D. Borissova, Z. Dimitrova

引用次数: 1

Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming 猪精准养殖中深度学习实例分割模型的评价

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-07-02 DOI: 10.52825/bis.v1i.59

J. Witte, Johann Gerberding, Christian Melching, J. Gómez

{"title":"Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming","authors":"J. Witte, Johann Gerberding, Christian Melching, J. Gómez","doi":"10.52825/bis.v1i.59","DOIUrl":"https://doi.org/10.52825/bis.v1i.59","url":null,"abstract":"In this paper, the deep learning instance segmentation architectures DetectoRS, SOLOv2, DETR and Mask R-CNN were applied to data from the field of Pig Precision Livestock Farming to investigate whether these models can address the specific challenges of this domain. For this purpose, we created a custom dataset consisting of 731 images with high heterogeneity and high-quality segmentation masks. For evaluation, the standard metric for benchmarking instance segmentation models in computer vision, the mean average precision, was used. The results show that all tested models can be applied to the considered domain in terms of prediction accuracy. With a mAP of 0.848, DetectoRS achieves the best results on the test set, but is also the largest model with the greatest hardware requirements. It turns out that increasing model complexity and size does not have a large impact on prediction accuracy for instance segmentation of pigs. DETR, SOLOv2, and Mask R-CNN achieve similar results to DetectoRS with a parameter count almost three times smaller. Visual evaluation of predictions shows quality differences in terms of accuracy of segmentation masks. DetectoRS generates the best masks overall, while DETR has advantages in correctly segmenting the tail region. However, it can be observed that each of the tested models has problems in assigning segmentation masks correctly once a pig is overlapped. The results demonstrate the potential of deep learning instance segmentation models in Pig Precision Livestock Farming and lay the foundation for future research in this area.","PeriodicalId":56020,"journal":{"name":"Business & Information Systems Engineering","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75204751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Post-Brexit power of European Union from the world trade network analysis 从世界贸易网络分析脱欧后欧盟的权力

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-05-03 DOI: 10.52825/bis.v1i.48

Justin Loye, K. Jaffrès-Runser, D. Shepelyansky

引用次数: 1

Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data 用于分析运动对象的流处理工具发送大量位置数据

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.52825/bis.v1i.41

Krzysztof Węcel, Marcin Szmydt, Milena Stróżyna

引用次数: 1

Deep Learning for Customer Churn Prediction in E-Commerce Decision Support 电子商务决策支持中客户流失预测的深度学习

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.52825/bis.v1i.42

Maciej Pondel, Maciej Wuczynski, W. Gryncewicz, Lukasz Lysik, Marcin Hernes, Artur Rot, Agata Kozina

{"title":"Deep Learning for Customer Churn Prediction in E-Commerce Decision Support","authors":"Maciej Pondel, Maciej Wuczynski, W. Gryncewicz, Lukasz Lysik, Marcin Hernes, Artur Rot, Agata Kozina","doi":"10.52825/bis.v1i.42","DOIUrl":"https://doi.org/10.52825/bis.v1i.42","url":null,"abstract":"Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.","PeriodicalId":56020,"journal":{"name":"Business & Information Systems Engineering","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84919126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Data Quality Assessment of Comma Separated Values Using Linked Data Approach 使用关联数据方法评估逗号分隔值的数据质量

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-04216-4_22

Aparna Nayak, Bojan Bozic, L. Longo

引用次数: 1

Challenges of Mining Twitter Data for Analyzing Service Performance: A Case Study of Transportation Service in Malaysia 挖掘Twitter数据分析服务绩效的挑战:以马来西亚交通服务为例

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.1007/978-3-031-04216-4_21

Hui Na Chua, Alvin Wei Qiang Liao, Y. Low, A. Lee, M. Ismail

引用次数: 2

Database-Less Extraction of Event Logs from Redo Logs 从重做日志中提取事件日志

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.52825/bis.v1i.66

Dorina Bano, Tom Lichtenstein, Finn Klessascheck, M. Weske

引用次数: 1

Domain-Specific Event Abstraction 特定于领域的事件抽象

IF 7.9 3区管理学

Business & Information Systems Engineering Pub Date : 2021-01-01 DOI: 10.52825/bis.v1i.39

Finn Klessascheck, Tom Lichtenstein, Martin Meier, Simon Remy, Jan-Philipp Sachs, Luise Pufahl, Riccardo Miotto, E. Böttinger, M. Weske

引用次数: 2