Industrial information integration—A literature review 2006–2015

Chen, Yong

doi:10.1016/j.jii.2016.04.004

Cited by 114 publications

(52 citation statements)

References 253 publications

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“…This kind of problem has been a subject in the area of Industrial Information Integration. Chen () conducted a survey over several databases in Web of Science and selected approximately 500 papers on the subject “Industrial Information Integration.” Those papers were reviewed and divided into 37 groups, according to research subject, in order to establish the current stage of development and to detect trends and demands for future work in that field.…”

Section: Introductionmentioning

confidence: 99%

An inference model for combustion diagnostics in an experimental oil furnace

et al. 2017

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The continuous monitoring of the air/fuel ratio, oil/water/air temperatures, and gas/particulate emissions of combustion processes in oil‐based furnaces allows experts to detect anomalies and act to prevent faults and critical conditions. These important but tedious tasks can be performed by an expert system designed to mimic the human abilities of recognizing relevant patterns and finding their most likely causes. In this article, we present the architecture of an expert system that uses flame images grabbed during the combustion process in an experimental oil furnace as input parameters. Computational processing of those images provides feature vectors for analysis by “artificial experts” that correlate changes in flame appearance with typical combustion states. The Dempster–Shafer method is used to build the knowledge base and the inference engine. The results of tests in which flame conditions are suddenly modified by altering the physical variables of the combustion process revealed that the method can properly combine measures from various flame image characteristics to issue diagnostics. Such diagnostics are similar to those given by human experts. This suggests that the proposed approach may fill the gap between models based on features extracted from images and real‐world operating conditions. This is the intended contribution of this work.

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Section: Introductionmentioning

confidence: 99%

An inference model for combustion diagnostics in an experimental oil furnace

et al. 2017

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“…A lack of standards for interoperability without concerning the underlying hardware is a huge issue in IoT. Currently, IoT devices are domain specific and are used for special purposes [14]. Information is presented using various data patterns and forms on the extensive scale.…”

Section: Introductionmentioning

confidence: 99%

Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT

et al. 2017

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Abstract:The collection of raw data is based on sensors embedded in devices or the environment for real-time data extraction. Nowadays, the Internet of Things (IoT) environment is used to support autonomous data collection by reducing human involvement. It is hard to analyze such data, especially when working with the sensors in a real-time environment. On using data analytics in IoT with the help of RDF, many issues can be resolved. Resultant data will have a better chance of quality analytics by reforming data into the semantical annotation. Industrial correspondence through data will be improved by the induction of semantics at large due to efficient data capturing, whereas one popular medium of sensors' data storage is Relational Database (RDB). This study provides a complete automated mechanism to transform from loosely structured data stored in RDB into RDF. These data are obtained from sensors in semantically annotated RDF stores. The given study comprises methodology for improving compatibility by introducing bidirectional transformation between classical DB and RDF data stores to enhance the sustainable capabilities of IoT systems for autonomous analytics. Two case studies, one on weather and another on heart-rate measurement collections through IoT sensors, are used to show the transformation process in operation.

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“…AI models are used in a variety of domains including aeronautics (Wang, Chen, & Zhuming, 2015), human resources (Chanda & Aggarwal, 2016), face recognition (Shen et al, 2015), Internet traffic classification (Yuan, Li, Guan, & Xu, 2010), and software effort estimation (Kultur, Turhan, & Bener, 2009). Integrating information is essential in many domains (Chen, 2016;Deng et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hybrid approaches for detecting credit card fraud

Kultur

Caglayan

2016

Expert Systems

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As a natural consequence of the multibillion dollar annual losses incurred as a result of credit card fraud, banks attach great importance to credit card fraud detection. In this paper, we proposed the use of six known models, namely, decision tree, random forest, Bayesian network, Naïve Bayes, support vector machine, and K* models, to form an ensemble for the detection of credit card fraud. We focused on the voting mechanisms used by the ensemble and proposed optimistic, pessimistic, and weighted voting strategies. The proposed model is called optimistic, pessimistic, and weighted voting in an ensemble of models. A dataset of real credit card transactions from a leading bank in Turkey was used to evaluate the performance of optimistic, pessimistic, and weighted voting in an ensemble of models. The results showed that the optimistic voting strategy enables the detection of 31.59% of fraudulent transactions with a false alarm rate of only 0.10%, the pessimistic voting strategy detects 93.92% of fraudulent transactions with a false alarm rate of 13.72%, and the weighted voting strategy detects 64.02% of fraudulent transactions with a false alarm rate of 0.75%. Banks can choose among these voting mechanisms depending on their preferred strategies for fraud detection and desired false alarm rates.

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Industrial information integration—A literature review 2006–2015

Cited by 114 publications

References 253 publications

An inference model for combustion diagnostics in an experimental oil furnace

An inference model for combustion diagnostics in an experimental oil furnace

Data Compatibility to Enhance Sustainable Capabilities for Autonomous Analytics in IoT

Hybrid approaches for detecting credit card fraud

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