SYSTEMATIC LITERATURE REVIEW ON THE APPLICATION OF DATA MINING METHODS TO MONITOR THE OPERATOR FUNCTIONAL STATE IN HUMAN-MACHINE SYSTEMS

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.

Download Full-text

Data Mining Techniques for Disease Risk Prediction Model: A Systematic Literature Review

Advances in Intelligent Systems and Computing - Recent Trends in Data Science and Soft Computing ◽

10.1007/978-3-319-99007-1_4 ◽

2018 ◽

pp. 40-46 ◽

Cited By ~ 1

Author(s):

Wan Muhamad Taufik Wan Ahmad ◽

Nur Laila Ab Ghani ◽

Sulfeeza Mohd Drus

Keyword(s):

Data Mining ◽

Literature Review ◽

Prediction Model ◽

Risk Prediction ◽

Systematic Literature Review ◽

Disease Risk ◽

Risk Prediction Model ◽

Data Mining Techniques

Download Full-text

Agent mining approaches: an ontological view

The Knowledge Engineering Review ◽

10.1017/s0269888921000114 ◽

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.

Download Full-text

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

Management Decision ◽

10.1108/md-01-2020-0035 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Roberto Salazar-Reyna ◽

Fernando Gonzalez-Aleu ◽

Edgar M.A. Granda-Gutierrez ◽

Jenny Diaz-Ramirez ◽

Jose Arturo Garza-Reyes ◽

...

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Literature Review ◽

Systematic Literature Review ◽

Data Analytics ◽

Research Area ◽

Engineering Systems ◽

Content Type ◽

Healthcare Engineering

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

Download Full-text

Data Mining Research with In-copyright and Use-limited Text Datasets: Preliminary Findings from a Systematic Literature Review and Stakeholder Interviews

International Journal of Digital Curation ◽

10.2218/ijdc.v13i1.620 ◽

2018 ◽

Vol 13 (1) ◽

pp. 183-194 ◽

Cited By ~ 2

Author(s):

Megan Senseney ◽

Eleanor Dickson ◽

Beth Namachchivaya ◽

Bertram Ludäscher

Keyword(s):

Data Mining ◽

Intellectual Property ◽

Literature Review ◽

Systematic Literature Review ◽

Best Practice ◽

Digital Publishing ◽

Text Data ◽

Text Data Mining ◽

Distributed Approach ◽

Public Campaign

Text data mining and analysis has emerged as a viable research method for scholars, following the growth of mass digitization, digital publishing, and scholarly interest in data re-use. Yet the texts that comprise datasets for analysis are frequently protected by copyright or other intellectual property rights that limit their access and use. This article discusses the role of libraries at the intersection of data mining and intellectual property, asserting that academic libraries are vital partners in enabling scholars to effectively incorporate text data mining into their research. We report on activities leading up to an IMLS-funded National Forum of stakeholders and discuss preliminary findings from a systematic literature review, as well as initial results of interviews with forum stakeholders. Emerging themes suggest the need for a multi-pronged distributed approach that includes a public campaign for building awareness and advocacy, development of best practice guides for library support services and training, and international efforts toward data standardization and copyright harmonization.

Download Full-text