Fuzzy logic applied to opinion mining: A review

Serrano-Guerrero, Jesús; Romero, Francisco P.; Olivas, José Á.

doi:10.1016/j.knosys.2021.107018

Cited by 79 publications

(28 citation statements)

References 150 publications

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“…Different functions can represent each set that will be used by inference rules to reach a conclusion [52]. This versatility allows for a variety of usages to help computers solve real world problems [53] [55] [56].…”

Section: B Fuzzy Logicmentioning

confidence: 99%

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

et al. 2022

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Nowadays, it is common for applications to require servers to run constantly and aim as close as possible to zero downtime. The slightest failure might cause significant financial losses and sometimes even lives. For this reason, security and management measures against network threats are fundamental and have been researched for years. Software-defined networks (SDN) are an advancement in network management due to their centralization of the control plane, as it facilitates equipment setup and administration over the local network. However, this centralization makes the controller a target to denial of service attacks (DoS). In this study, we aim to develop a network anomaly detection and mitigation system that uses gated recurrent unit (GRU) neural networks combined with fuzzy logic. The neural network is trained to forecast future traffic, and anomalies are detected when the forecasting fails. The system is designed to operate in software-defined networks since they provide network flow information and tools to manage forwarding tables. We also demonstrate how the neural network's hyperparameters affect the detection module. The system was tested using two datasets: one with emulated traffic generated by the data communication and networking research group called Orion, from computer science department at state university of Londrina, and CICDDoS2019, a well-known dataset by the anomaly detection community. The results show that GRU networks combined with fuzzy logic are a viable option to detect anomalies in SDN and possibly in other anomaly detection applications. We compared our model with other deep learning techniques.

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Section: B Fuzzy Logicmentioning

confidence: 99%

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

et al. 2022

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“…Such statements have lost strength due to satisfactory applications of FL in sensor technology, electronics, and railways, for instance [23,24]. Recent works have pointed out the application of Fuzzy Logic as a tool that, associated with techniques such as Artificial Intelligence, Machine Learning, Deep Learning and Multicriteria Decision Analysis, can contribute to mine opinions, in addition to supporting decisions and analyzing trends [25].…”

Section: Fuzzy Logic Promising Applicationsmentioning

confidence: 99%

Application of the QFD-fuzzy-SERVQUAL methodology as a quality planning tool at the surgical centre of a public teaching hospital

Galdino

Hékis

Costa

et al. 2022

BMC Med Inform Decis Mak

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Background In Brazil, many public hospitals face constant problems related to high demand vis-à-vis an overall scarcity of resources, which hinders the operations of different sectors such as the surgical centre, as it is considered one of the most relevant pillars for the proper hospital functioning, due to its complexity, criticality as well as economic and social importance. Proper asset management based on well-founded decisions is, therefore, a sine-qua-non condition for addressing such demands. However, subjectivity and other difficulties present in decisions make the management of hospital resources a constant challenge. Methods Thus, the present work proposes the application of a hybrid approach, formed by the QFD tools, fuzzy logic and SERVQUAL as a decision support tool for the quality planning of the surgical centre of the Onofre Lopes Teaching Hospital (Hospital Universitário Onofre Lopes—HUOL). To accomplish such objective, it was necessary to discover and analyse the main needs of the medical team working in the operating room, through the application of the SERVQUAL questionnaire, associated with fuzzy logic. Results Then, the most relevant deficiencies were transformed into entries for the QFD-fuzzy, where they were translated into project requirements. Soon after, the analysis of the existing relationships between the inputs and these requirements was carried out, generating the ranking of actions with the greatest impact on the improvement of the surgical centre overall quality. Conclusions As a result, it was found that the proposed methodology can optimize the decision process to which hospital managers are submitted, improving the surgical centre operation efficiency.

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“…There are also many extensions of fuzzy sets, such as fuzzy soft set, 39 intuitionistic fuzzy soft sets, 40 spherical fuzzy sets, 41 type‐3 fuzzy set, 42 and so forth. These different kinds of fuzzy sets themselves have been mostly used in research topics, such as decision making, 43 image processing, 44 signal processing, 45 opinion mining, 46 and so forth. Although various fuzzy sets are getting increasingly sophisticated and capable of handling more information, the focus of this research is on innovative operations, relations, and operators in fuzzy set theories, which essentially expand the operators stated over type‐1 fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

Ding

Wen

et al. 2021

Int J of Intelligent Sys

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This paper deals with the trajectory tracking control problem of a planar underactuated tendon‐driven truss‐like manipulator (UTTM) by using fuzzy logic control methods, including type‐1 fuzzy and interval type‐2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon‐driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic‐based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type‐1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type‐1 fuzzy controllers, an interval type‐2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type‐1 and interval type‐2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type‐2 fuzzy‐based trajectory tracking controller provides better performance than the type‐1 counterpart in terms of tracking accuracy and capacity against uncertainties.

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Fuzzy logic applied to opinion mining: A review

Cited by 79 publications

References 150 publications

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

A Gated Recurrent Unit Deep Learning Model to Detect and Mitigate Distributed Denial of Service and Portscan Attacks

Application of the QFD-fuzzy-SERVQUAL methodology as a quality planning tool at the surgical centre of a public teaching hospital

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

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