Literature review on the applications of data mining in power systems

2014 ◽  
Author(s):  
Maryam Kazerooni ◽  
Hao Zhu ◽  
Thomas J. Overbye
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Power Systems

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

Data mining applications in power systems — Case-studies and future trends

2009 ◽  
Author(s):  
Zita A. Vale ◽  
Carlos Ramos ◽  
Sergio Ramos ◽  
Tiago Pinto
Keyword(s):  
Data Mining ◽  
Power Systems ◽  
Case Studies ◽  
Future Trends

Wildfire Mitigation Plans in Power Systems: A Literature Review

2022 ◽  
pp. 1-1
Author(s):  
Daniel Alberto Zuniga Vazquez ◽  
Feng Qiu ◽  
Neng Fan ◽  
Kevin Sharp
Keyword(s):  
Literature Review ◽  
Power Systems

Agent mining approaches: an ontological view

2021 ◽  
Vol 36 ◽  
Author(s):  
Emmanuelle Grislin-Le Strugeon ◽  
Kathia Marcal de Oliveira ◽  
Dorsaf Zekri ◽  
Marie Thilliez
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Knowledge Discovery ◽  
Systematic Literature Review ◽  
Software Agent ◽  
Application Domain ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Agent Mining ◽  
Multi Agent

Abstract Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

scholarly journals Retention and Task Shifting in Human Resources for Health through Data Mining

2017 ◽  
Vol 10 (5) ◽  
pp. 29
Author(s):  
Cheng-Kun Wang
Keyword(s):  
Data Mining ◽  
Literature Review ◽  
Human Resources ◽  
Rural Areas ◽  
Health Workers ◽  
Human Resources For Health ◽  
Task Shifting ◽  
Good Tool ◽  
Medical Manpower ◽  
And Task

Human resources for health (HRH) are the backbone of the healthcare system, but a shortage of medical manpower and the misdistribution of human resources are critical problems in the rural areas of many countries till 2017. The shortage of medical manpower is a big issue between 2004 and 2013. Data mining of bibliometrics is a good tool to find the solutions for shortage of medical manpower. By analyzing 118,092 citations in 2,000 articles published in the SSCI and SCI databases addressing HRH from 2004 to 2013, we plotted the networks among authors in the field. We combine quantitative bibliometrics and a qualitative literature review to determine the important articles and to realize the relationships between important topics in this field. We find that retention and task shifting are the hot topics in HRH field between 2004 and 2013, and find out the solutions for these issues through literature review in later papers. The solution to the HRH shortage is to determine the motivations of health workers and to provide incentives to maintain their retention. Task shifting is another solution to the HRH crisis.

Sign in / Sign up

Export Citation Format

Share Document