Meta-heuristic improvements applied for steel sheet incremental cold shaping

Villar, José R.; González, Silvia; Sedano, Javier; Corchado, Emilio; Puigpinós, Laura; Ciurana, Joaquim

doi:10.1007/s12293-012-0100-4

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In this case, the FS method is an adapted version [45][46][47] of the Steady State Genetic Algorithm FS [10]. The wrapper makes use of a genetic algorithm to learn a GFFSM; a relaxed set of parameters is used in this learning, in order to reduce the computational cost.…”

Section: Human Activity Recognition Using Gffsmmentioning

confidence: 99%

A hybrid intelligent recognition system for the early detection of strokes

Villar

Chira

Sedano

et al. 2015

ICA

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Abstract. The increasing prevalence of wearable sensors and low-cost mobile devices have prompted the development of systems for automated diagnosis. Here we focus on models and algorithms for the early detection of strokes that are implanted in a wearable device that generates warning alarms and automatically connects to e-health services, ensuring timely interventions at the onset of a stroke. The proposed approach employs two wearable devices to monitor movement data that involve two main stages: Human Activity Recognition (HAR) and alarm generation. Two different HAR methods capable of classifying current human activity are developed and compared: one uses genetic fuzzy finite-state machines, and the other relies on Time Series (TS) analysis. Furthermore, an algorithm using Symbolic Aggregate approXimation (SAX) TS representation is proposed for alarm generation purposes, which is triggered by the detection of anomalous movements. The proposed methods are evaluated using realistic data gathered from healthy individuals. A discussion of topics related to the learning issues involved in these techniques is included. It is worth mentioning that the proposed algorithms can be easily transferred to embedded systems and can benefit from reduced learning costs.

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Section: Human Activity Recognition Using Gffsmmentioning

confidence: 99%

A hybrid intelligent recognition system for the early detection of strokes

Villar

Chira

Sedano

et al. 2015

ICA

Self Cite

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“…For a more comprehensive study, two different CV schemes have been applied in this work, namely 10K-fold and 5 × 2 [30]. They are applied to check how suitable are the learning models for the addressed problem (detection of SSH malicious connections).…”

Section: Validation Schemesmentioning

confidence: 99%

Different approaches for the detection of SSH anomalous connections

et al. 2015

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The Secure Shell Protocol (SSH) is a well-known standard protocol, mainly used for remotely accessing shell accounts on Unix-like operating systems to perform administrative tasks. As a result, the SSH service has been an appealing target for attackers, aiming to guess root passwords performing dictionary attacks or to directly exploit the service itself. To identify such situations, this article addresses the detection of SSH anomalous connections from an intrusion detection perspective. The main idea is to compare several strategies and approaches for a better detection of SSH-based attacks. To test the classification performance of different classifiers and combinations of them, SSH data coming from a real-world honeynet are gathered and analysed. For comparison purposes and to draw conclusions about data collection, both packet-based and flow data are analysed. A wide range of classifiers and ensembles are applied to these data, as well as different validation schemes for better analysis of the obtained results. The high-rate classification results lead to positive conclusions about the identification of malicious SSH connections.

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“…It is known that the complexity inherited in most of the new real-world problems increases with the computing capabilities [18]. Higher performance requirements with a lower amount of data examples are needed due to the costs of gathering new instances, especially in those processes where new technology arises.…”

Section: Introductionmentioning

confidence: 99%

Modelling Dental Milling Process with Machine Learning-Based Regression Algorithms

Jackowski

Jankowski

Quintián

et al. 2016

Advances in Intelligent Systems and Computing

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Control of dental milling processes is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective functions, while model of relationship between the parameters and outputs (such as costs or time) is not known. Therefore, the model must be discovered in advance to use it for optimisation. Machine learning algorithms serve this purpose perfectly. There are plethoras of competing methods and the question is which shall be selected. In this paper, we present results of extensive investigation on this question. We evaluated several well-known classical regression algorithms, ensemble approaches and feature selection techniques in order to find the best model for dental milling model.

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Meta-heuristic improvements applied for steel sheet incremental cold shaping

Cited by 8 publications

References 26 publications

A hybrid intelligent recognition system for the early detection of strokes

A hybrid intelligent recognition system for the early detection of strokes

Different approaches for the detection of SSH anomalous connections

Modelling Dental Milling Process with Machine Learning-Based Regression Algorithms

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