Customer Shopping Experience in a South Korea’s Government-Run Home Shopping Channel for Small and Medium Enterprises Based on Critical Incident Technique and Unsupervised Machine Learning Analysis

2022 ◽  
pp. 101777
Author(s):  
Jieon Lee ◽  
Jong-ho Won ◽  
Daeho Lee ◽  
Kyu Tae Kwak
Keyword(s):  
Machine Learning ◽  
Critical Incident ◽  
Small And Medium Enterprises ◽  
Critical Incident Technique ◽  
Unsupervised Machine Learning ◽  
Home Shopping ◽  
Shopping Experience ◽  
Medium Enterprises ◽  
Learning Analysis

scholarly journals Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi Bian ◽  
Yue Le ◽  
Han Du ◽  
Junfang Chen ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Organ ◽  
Hospital Death ◽  
Matched Cohort ◽  
Multiple Organ Dysfunction ◽  
Bleeding Events ◽  
Full Cohort ◽  
Unsupervised Machine Learning ◽  
Death Risk ◽  
Learning Analysis

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning.Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC.Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC.Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.

Unsupervised Machine Learning Analysis of Urinary Transcriptome Reveals Distinct Genotypic Clustering in Surgical Sepsis

2017 ◽  
Vol 225 (4) ◽  
pp. S66-S67
Author(s):  
Nicholas Lysak ◽  
Ashkan Ebadi ◽  
Sabyasachi Bandyopadhyay ◽  
Tezcan Ozrazgat-Baslanti ◽  
Larysa Sautina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Unsupervised Machine Learning ◽  
Learning Analysis

scholarly journals La inteligencia artificial al rescate de las pequeñas y medianas empresas

2019 ◽  
pp. 22-34
Author(s):  
Nestor de Jesús Ramírez-Solano ◽  
Ana Laura Nieto-Rosales ◽  
Ana Stephany Martínez-González ◽  
Cecilia Vidal-Hernández
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Electronic Commerce ◽  
Mobile Application ◽  
Small And Medium Enterprises ◽  
Economic Sector ◽  
Automatic Learning ◽  
Medium Enterprises

The present work seeks to show the use of artificial intelligence as an option within electronic commerce, this in order to contribute to the growth and promotion of sales of small and medium enterprises. The application of artificial intelligence in the economic sector is very broad, so this time is intervened with a proposal aimed at this sector. Through the present development, a mobile application was designed, where it is proposed to apply the use of Machine Learning or automatic learning through patterns, to implement an application capable of recognizing images and text provided by users to link them directly with small and medium companies, which will offer their services and products through it, allowing them to compete on a par with large companies that have the economic solvency to develop their own applications individually. The process for the development of the application was the Scrum methodology, since derived from the nature of the project, it was required to make constant changes in the development of the product, by the Sprint.

Tailoring FOS-ERP Packages

Data Mining ◽  
2013 ◽  
pp. 1979-1996
Author(s):  
Klaus Wölfel ◽  
Jean-Paul Smets
Keyword(s):  
Machine Learning ◽  
Enterprise Resource Planning ◽  
Knowledge Engineering ◽  
Small And Medium Enterprises ◽  
Implementation Process ◽  
Resource Planning ◽  
Technical Documentation ◽  
Medium Enterprises ◽  
Overseas Subsidiaries ◽  
Free Open Source

Free/Open Source software (FOSS) has made Enterprise Resource Planning (ERP) systems more accessible for Small and Medium Enterprises (SMEs) including overseas subsidiaries of large companies. However, the consulting required to configure an ERP to meet the specific needs of an organization remains a major financial and organizational burden for SMEs. Automatic ERP package configuration based on knowledge engineering, machine learning and data mining could be a solution to lessen the burden of the implementation process. This chapter presents two approaches to an automation of selected configuration options of the FOS-ERP package ERP5. These approaches are based on knowledge engineering with decision trees and machine learning with classifiers. The design of the ERP5 Artificial intelligence Toolkit (EAT) aims at the integration of these approaches into ERP5. The chapter also shows how FOS-ERP can boost Information System (IS) research. The investigation of the automation approaches was only possible because the free source code and technical documentation of ERP5 was accessible for TU Dresden researchers.

ICU staffing feature phenotypes and their relationship with patients’ outcomes: an unsupervised machine learning analysis

2019 ◽  
Vol 45 (11) ◽  
pp. 1599-1607 ◽  
Author(s):  
Fernando G. Zampieri ◽  
◽  
Jorge I. F. Salluh ◽  
Luciano C. P. Azevedo ◽  
Jeremy M. Kahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Unsupervised Machine Learning ◽  
Learning Analysis

Tailoring FOS-ERP Packages

2012 ◽  
pp. 116-133 ◽  
Author(s):  
Klaus Wölfel ◽  
Jean-Paul Smets
Keyword(s):  
Machine Learning ◽  
Enterprise Resource Planning ◽  
Knowledge Engineering ◽  
Small And Medium Enterprises ◽  
Implementation Process ◽  
Resource Planning ◽  
Technical Documentation ◽  
Medium Enterprises ◽  
Overseas Subsidiaries ◽  
Free Open Source

Free/Open Source software (FOSS) has made Enterprise Resource Planning (ERP) systems more accessible for Small and Medium Enterprises (SMEs) including overseas subsidiaries of large companies. However, the consulting required to configure an ERP to meet the specific needs of an organization remains a major financial and organizational burden for SMEs. Automatic ERP package configuration based on knowledge engineering, machine learning and data mining could be a solution to lessen the burden of the implementation process. This chapter presents two approaches to an automation of selected configuration options of the FOS-ERP package ERP5. These approaches are based on knowledge engineering with decision trees and machine learning with classifiers. The design of the ERP5 Artificial intelligence Toolkit (EAT) aims at the integration of these approaches into ERP5. The chapter also shows how FOS-ERP can boost Information System (IS) research. The investigation of the automation approaches was only possible because the free source code and technical documentation of ERP5 was accessible for TU Dresden researchers.

Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis

2018 ◽  
Vol 24 (4) ◽  
pp. 879-891 ◽  
Author(s):  
Buranee Kanchanatawan ◽  
Sira Sriswasdi ◽  
Supaksorn Thika ◽  
Drozdstoy Stoyanov ◽  
Sunee Sirivichayakul ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stable Phase ◽  
New Classification ◽  
Unsupervised Machine Learning ◽  
Learning Analysis
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