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International Journal of Business Intelligence and Data Mining最新文献

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Hybrid Automation of Student Activity Records in Virtual Learning Environments (VLEs) in Semi-dark Scenarios 半黑暗场景下虚拟学习环境(VLEs)学生活动记录的混合自动化
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10050463
S. Rizvi, M. Hashmani, M. Memon, K. Raza
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引用次数: 0
Mining Models for Predicting Product Quality Properties of Petroleum Products 预测石油产品质量特性的采矿模型
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10043436
D. Kunda, Ng'ambilani Zulu
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引用次数: 0
A deep regression convolutional neural network using whole image-based inferencing for dynamic visual crowd estimation 基于全图像推理的深度回归卷积神经网络用于动态视觉人群估计
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2022.10044713
Teoh Shen Khang, Yap Vooi Voon, H. Nisar
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引用次数: 0
Data quality-based view selection in big data integration system 基于数据质量的大数据集成系统视图选择
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.133139
Samir Anter
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引用次数: 0
Variable item value-based high utility itemset recommendation using statistical approach 基于变量项目价值的高效用项目集推荐
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10047036
Abdullah Bokir, V. Narasimha
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引用次数: 0
A novel classification-based parallel frequent pattern discovery model for decision making and strategic planning in retailing 基于分类的零售决策与战略规划并行频繁模式发现模型
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10046447
Rajiv Senapati
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引用次数: 0
Using unstructured logs generated in complex large-scale micro-service-based architecture for data analysis 利用复杂的大规模微服务架构中生成的非结构化日志进行数据分析
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.127294
Anukampa Behera, Sitesh Behera, C. Panigrahi, T. Weng
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引用次数: 0
Data quality-based view selection in big data integration system 基于数据质量的大数据集成系统视图选择
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10048381
S. Anter
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引用次数: 0
Evaluation of factors involved in predicting Indian stock price using machine learning algorithms 使用机器学习算法评估预测印度股票价格所涉及的因素
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.10048648
Archit A. Vohra, Paresh Tanna
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引用次数: 0
Exploring outliers in global economic dataset having the impact of COVID-19 pandemic 探索受新冠肺炎大流行影响的全球经济数据异常值
Q3 Decision Sciences
International Journal of Business Intelligence and Data Mining
Pub Date : 2023-01-01 DOI: 10.1504/ijbidm.2023.129877
Anindita Desarkar, Ajanta Das, C. Chaudhuri
Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.
离群值是位于数据集中大多数其他值之外的值。异常值探测在几乎所有的行业应用中都非常重要，比如医疗诊断、信用卡欺诈和入侵检测系统。同样，在经济领域，它可以用于分析许多突发事件，如股票市场的突然崩溃，国家人均收入与整体发展的不匹配，失业率的突变和银行利率的急剧下降，以获得新的知识。这些情况可能是由多种原因造成的，其中当前的COVID-19大流行是一个主要原因。这促使目前的研究人员在经济领域确定几个这样的脆弱领域，并为每个领域找出受影响最严重的国家。两种著名的机器学习技术DBSCAN和Z-score被用来获得这些见解，这可以作为随后改善整体场景的指导方针。版权所有©2023 Inderscience Enterprises Ltd。
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引用次数: 0
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