Data Mining Definitions and Applications for the Management of Production Complexity

Schuh, Günther; Reinhart, Gunther; Prote, Jan-Philipp; Sauermann, Frederick; Horsthofer, Julia; Oppolzer, Florian; Knoll, Dino

doi:10.1016/j.procir.2019.03.217

Cited by 67 publications

(37 citation statements)

References 23 publications

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“…For instance, a common application is the development of computational algorithms to distinguish between healthy and unhealthy subjects. AI seeks to develop algorithms to sort out real problems more efficiently than can humans ( 94 ) and has applications in several areas; however, in the context of this document the main implementations are related to expert systems, data mining or knowledge extraction, and knowledge representation ( 95 ).…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Industry 4.0 concept involves collection, storage, management, and analysis of the data generated by production systems to manage (for automated identification of failures, assessment of operating conditions and quality of products, identification of the unplanned stops, etc.). However, many companies refuse to apply DM because of the poor quality of the outcomes, which is mainly due to the reasons raised in this paper (Schuh et al, 2019;Kozjeka et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 98%

Data mining as a cognitive tool: Capabilities and limits

Polyakov

Khanin

Shevchenko

et al. 2021

Knowledge and Performance Management

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Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.

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“…For example, the protocol_type attribute consists of three categorical values (tcp, udp, icmp), and these values are converted to (10, 20, and 30), respectively. For instance, if an attribute consists of 100 categorical values, these values are converted to (10,20,30, …, 1000), respectively.…”

Section: Data Preparationmentioning

confidence: 99%

“…Knowledge Discovery in Databases (KDD) is defined as the operation of extracting patterns and models from large databases. Data mining is often used as a synonym for the KDD process, and it refers to the process of applying the discovery algorithm to the data (Schuh, 2019). One of the most important data mining techniques for IDSs is the rule discovery which tries to locate a collection of rules that can recognize the specific class from various groups of classes.…”

Section: Introductionmentioning

confidence: 99%

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

Eesa¹,

sadiq²,

Hassan³

et al. 2021

Uludağ University Journal of the Faculty of Engineering

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Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are increasingly in demand. Consequently, numerous illicit activities by external and internal attackers need to be detected. Thus, earlier detection of such activities is necessary for protecting data and information. In this paper, we investigated the use of the Cuttlefish optimization algorithm as a new rule generation method for the classification task to deal with the intrusion detection problem. The effectiveness of the proposed method was tested using KDD Cup 99 dataset based on different evaluation methods. The obtained results were also compared with the results obtained by some classical well-known algorithms namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighborhood (K-NN). Our experimental results showed that the proposed method demonstrates a good classification performance and provides significantly preferable results when compared with the performance of other traditional algorithms. The proposed method produced 93.9%, 92.2%, and 94.7% in terms of precision, recall, and area under curve, respectively.

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Data Mining Definitions and Applications for the Management of Production Complexity

Cited by 67 publications

References 23 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Data mining as a cognitive tool: Capabilities and limits

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

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