Proceedings of the 2021 4th International Conference on Computers in Management and Business最新文献

Analysis on E-commerce Order Cancellations Using Market Segmentation Approach 基于市场细分的电子商务订单取消分析

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450596

Jingyi Ye

引用次数: 2

Why Supply Chain Collaboration Matters for Indonesian Dry Port Firms? 为什么供应链合作对印尼干港公司很重要?

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450601

E. Kuncoro, H. Saroso, Darjat Sudrajat, Anisa Larasati, Dennis Moeke

引用次数: 0

Transfer Learning for Classification of Fruit Ripeness Using VGG16 基于VGG16的水果成熟度迁移学习分类

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450943

Asep Nana Hermana, Dewi Rosmala, M. G. Husada

引用次数: 1

Avoiding Counterfeits and Achieving Privacy in Supply Chain: A Blockchain Based Approach 在供应链中避免假冒和实现隐私:基于区块链的方法

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450597

Prem Ratan Baranwal

{"title":"Avoiding Counterfeits and Achieving Privacy in Supply Chain: A Blockchain Based Approach","authors":"Prem Ratan Baranwal","doi":"10.1145/3450588.3450597","DOIUrl":"https://doi.org/10.1145/3450588.3450597","url":null,"abstract":"Counterfeiting is one of the biggest threats to the current RFID-based supply chain industry. There are several blockchain solutions suggested which preserve RFID tag uniqueness and bring all the parties to a common platform. However, they are based on some common assumptions such as an adversary may need a large number of tags to retrieve the product details. Besides, these tags can be easily cloned post supply chain and reused for counterfeiting. Lack of proper mechanisms to preserve trade secrets such as volumes and supplier relationships is also a major challenge faced by most of these systems in this competitive market. We propose a novel product ownership tracking system based on blockchain for the supply chain industry which solves these problems while allowing access to the trade secrets only to its supply chain participants. The proposed protocol is based on Shamir's threshold Scheme where a secret key is used to decrypt the trade secrets and its shares are distributed among the supply chain parties during the ownership transfer process. Product verification is done by the consumer as well with the help of the retailer at the time of sale.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114641888","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

A Framework for developing Teamwork Enabled Services in Smart City Domains 在智慧城市领域开发团队协作服务的框架

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450595

Paraskevi Tsoutsa, Omiros Iatrellis, O. Ragos, P. Fitsilis

{"title":"A Framework for developing Teamwork Enabled Services in Smart City Domains","authors":"Paraskevi Tsoutsa, Omiros Iatrellis, O. Ragos, P. Fitsilis","doi":"10.1145/3450588.3450595","DOIUrl":"https://doi.org/10.1145/3450588.3450595","url":null,"abstract":"Services that collaborate alongside other services or systems for performing tasks, need to be aware of either predetermined or other abrupt and unexpected behaviors in other to adapt theirs. The service behaviors we consider are performed in smart city domains which are dynamic environments where continuously new services appear, that usually have a large variation in the way they perform the same task. We use roles having teamwork behavior to represent the composition procedure in such domains, and we model the team of services through the individual behavior of each participant as well as their group goal. In this paper, we present a framework, which consists of the approach and IT system in order to serve the choreography between a large number of heterogeneous services so as to achieve a seamless and cooperative environment suitable for a smart city. This enables composite city services to adapt their behavior during execution and themselves intervene from inside the team if a possible unexpected behavior happens during the service activity, in order to run proactively and avoid obstacles or collisions. A scenario from a smart city domain illustrates that, services having different teamwork abilities are composed to a new one which inherits teamwork features and combines them to something novel.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123524251","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

Effect of Supply Chain Collaboration and Service Stakeholder Commitment on Dry Port Firm Performance 供应链协作和服务利益相关者承诺对干港企业绩效的影响

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450602

D. H. Syahchari, E. Kuncoro, H. Saroso, Darjat Sudrajat, E. V. Zanten

引用次数: 0

Applications of Big Data Analytics in Supply Chain Management: Findings from Expert Interviews 大数据分析在供应链管理中的应用:来自专家访谈的发现

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450603

P. Brandtner, Chibuzor Udokwu, Farzaneh Darbanian, T. Falatouri

{"title":"Applications of Big Data Analytics in Supply Chain Management: Findings from Expert Interviews","authors":"P. Brandtner, Chibuzor Udokwu, Farzaneh Darbanian, T. Falatouri","doi":"10.1145/3450588.3450603","DOIUrl":"https://doi.org/10.1145/3450588.3450603","url":null,"abstract":"The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133676370","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: 11

The Application of Machine Learning Models in the Prediction of PM2.5/PM10 Concentration 机器学习模型在PM2.5/PM10浓度预测中的应用

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450605

Xinzhi Lin

{"title":"The Application of Machine Learning Models in the Prediction of PM2.5/PM10 Concentration","authors":"Xinzhi Lin","doi":"10.1145/3450588.3450605","DOIUrl":"https://doi.org/10.1145/3450588.3450605","url":null,"abstract":"The current world economy and science are in an era of rapid development, and Beijing is experiencing chronic air pollution. The air quality is important to the travel of people, development of enterprise and normal operation of traffic. PM2.5 and PM10 are the main components which cause the air pollution, and it's very meaningful to predict their concentration in the air [1]. Although some traditional models (like basic linear regression) have been proposed to predict the content of PM2.5/PM10, the quantities of variables included to predict the concentration are few and it executes with low efficiency and low accuracy. In the big data era, it's necessary to build the model which can execute the big data kinds and sets. With the adequate data sets from different meteorological stations in Beijing, we can use the more abundant variables such as mass of SO2, NO2, wind direction and other weather observations to predict the content of PM2.5/PM10. We build the machine learning models with higher efficiency, accuracy and stronger learning ability, whose primary algorithms include: multiple linear regression, decision tree, boosting and random forest based on decision tree and neural network. The result demonstrates that the prediction effect of the models is based on neural network and ensemble learning. Boosting performs best among these models, which achieves R-square 84.2% and 75.7% on the test set for the PM2.5 and PM10, respectively.","PeriodicalId":150426,"journal":{"name":"Proceedings of the 2021 4th International Conference on Computers in Management and Business","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993485","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

Logging Multi-Component Supply Chain Production in Blockchain 在区块链中记录多组件供应链生产

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450604

Y. Madhwal, Ivan Chistiakov, Y. Yanovich

引用次数: 4

The Role of End Users in Efficient Business Intelligence Solutions: A Preliminary Study 终端用户在高效商业智能解决方案中的作用:初步研究

Proceedings of the 2021 4th International Conference on Computers in Management and Business Pub Date : 2021-01-30 DOI: 10.1145/3450588.3450590

Marianne Buus Jorgensen, Tobias Christensen, Tanja Svarre

引用次数: 0