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Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey (2018)
Journal Article
Deveci, M., Özcan, E., John, R., & Öner, S. C. (2018). Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey. Journal of Air Transport Management, 69, doi:10.1016/j.jairtraman.2018.01.008

This study investigates the level of service quality of domestic airlines in Turkey travelling between Istanbul and London and compares those airline companies according to a set of predetermined criteria. A practical multi-criteria decision making a... Read More about Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey.

A first approach for handling uncertainty in citizen science (2018)
Conference Proceeding
Jiménez, M., Triguero, I., & John, R. (in press). A first approach for handling uncertainty in citizen science

Citizen Science is coming to the forefront of scientific research as a valuable method for large-scale processing of data. New technologies in fields such as astronomy or bio-sciences generate tons of data, for which a thorough expert analysis is no... Read More about A first approach for handling uncertainty in citizen science.

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs (2018)
Journal Article
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2018). Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs. IEEE/ASME Transactions on Mechatronics, 23(2), doi:10.1109/TMECH.2018.2810947

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interactio... Read More about Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs.

Type-2 fuzzy elliptic membership functions for modeling uncertainty (2018)
Journal Article
Kayacan, E., Sarabakha, A., Coupland, S., John, R., & Khanesard, M. A. (2018). Type-2 fuzzy elliptic membership functions for modeling uncertainty. Engineering Applications of Artificial Intelligence, 70, doi:10.1016/j.engappai.2018.02.004

Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of t... Read More about Type-2 fuzzy elliptic membership functions for modeling uncertainty.

Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems (2018)
Journal Article
Eyoh, I., John, R., de Maere, G., & Kayacan, E. (2018). Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems. IEEE Transactions on Fuzzy Systems, 26(5), 2672-2685. doi:10.1109/TFUZZ.2018.2803751

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and non-membership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi-Sugeno-Kang (TSK) fuzzy... Read More about Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems.

Impact of oil composition on microwave heating behavior of heavy oils (2018)
Journal Article
Zhang, Y., Adam, M., Hart, A., Wood, J., Rigby, S. P., & Robinson, J. P. (in press). Impact of oil composition on microwave heating behavior of heavy oils. Energy and Fuels, doi:10.1021/acs.energyfuels.7b03675

Electromagnetic heating techniques have recently received significant attention as alternatives to conventional heating methods for thermal processing of viscous and heavy oils. One of the benefits of electromagnetic heating is that the electromagnet... Read More about Impact of oil composition on microwave heating behavior of heavy oils.

A learning automata based multiobjective hyper-heuristic (2017)
Journal Article
Li, W., Özcan, E., & John, R. (2017). A learning automata based multiobjective hyper-heuristic. IEEE Transactions on Evolutionary Computation, doi:10.1109/TEVC.2017.2785346

Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimisation problems. However, once implemented, their application to a new problem domain or a slight change in the pro... Read More about A learning automata based multiobjective hyper-heuristic.

Interval type-2 A-intuitionistic fuzzy logic for regression problems (2017)
Journal Article
Eyoh, I., John, R., & de Maere, G. (in press). Interval type-2 A-intuitionistic fuzzy logic for regression problems. IEEE Transactions on Fuzzy Systems, doi:10.1109/TFUZZ.2017.2775599. ISSN 1063-6706

This paper presents an approach to prediction based on a new interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) of Takagi-Sugeno-Kang (TSK) fuzzy inference with neural network learning capability. The gradient descent (GD) algorit... Read More about Interval type-2 A-intuitionistic fuzzy logic for regression problems.

Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system (2017)
Conference Proceeding
Eyoh, I., John, R., & De Maere, G. (2017). Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)doi:10.1109/SMC.2017.8122694

Fuzzy logic systems have been extensively applied for solving many real world application problems because they are found to be universal approximators and many methods, particularly, gradient descent (GD) methods have been widely adopted for the opt... Read More about Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system.

Interval type-2 defuzzification using uncertainty weights (2017)
Journal Article
Runkler, T. A., Coupland, S., John, R., & Chen, C. (in press). Interval type-2 defuzzification using uncertainty weights. Studies in Computational Intelligence, 739, doi:10.1007/978-3-319-67789-7_4

One of the most popular interval type-2 defuzzification methods is the Karnik-Mendel (KM) algorithm. Nie and Tan (NT) have proposed an approximation of the KM method that converts the interval type-2 membership functions to a single type-1 membership... Read More about Interval type-2 defuzzification using uncertainty weights.

Vehicle incident hot spots identification: an approach for big data (2017)
Conference Proceeding
Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. (2017). Vehicle incident hot spots identification: an approach for big data. doi:10.1109/Trustcom/BigDataSE/ICESS.2017.329

In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a nu... Read More about Vehicle incident hot spots identification: an approach for big data.

Machine learning and statistical approaches to classification – a case study (2017)
Conference Proceeding
Eyoh, I., & John, R. (2017). Machine learning and statistical approaches to classification – a case study

The advent of information technology has led to the proliferation of data in disparate databases. Organisations have become data rich but knowledge poor. Users need efficient analysis tools to help them understand their data, predict future trends an... Read More about Machine learning and statistical approaches to classification – a case study.

An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots (2017)
Journal Article
Figueredo, G. P., Triguero, I., Mesgarpour, M., Maciel Guerra, A., Garibaldi, J. M., & John, R. (2017). An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(4), 248-258. https://doi.org/10.1109/TETCI.2017.2721960

We report on the adaptation of an immune-inspired instance selection technique to solve a real-world big data problem of determining vehicle incident hot spots. The technique, which is inspired by the Immune System self-regulation mechanism, was orig... Read More about An Immune-Inspired Technique to Identify Heavy Goods Vehicles Incident Hot Spots.

A new dynamic approach for non-singleton fuzzification in noisy time-series prediction (2017)
Conference Proceeding
Pourabdollah, A., John, R., & Garibaldi, J. M. (in press). A new dynamic approach for non-singleton fuzzification in noisy time-series prediction

Non-singleton fuzzification is used to model uncertain (e.g. noisy) inputs within fuzzy logic systems. In the standard approach, assuming the fuzzification type is known, the observed [noisy] input is usually considered to be the core of the input fu... Read More about A new dynamic approach for non-singleton fuzzification in noisy time-series prediction.

Time series forecasting with interval type-2 intuitionistic fuzzy logic systems (2017)
Conference Proceeding
Eyoh, I., John, R., & de Maere, G. (in press). Time series forecasting with interval type-2 intuitionistic fuzzy logic systems

Conventional fuzzy time series approaches make use of type-1 or type-2 fuzzy models. Type-1 models with one index (membership grade) cannot fully handle the level of uncertainty inherent in many real world applications. The type-2 models with upper a... Read More about Time series forecasting with interval type-2 intuitionistic fuzzy logic systems.

Type-1 and interval type-2 ANFIS: a comparison (2017)
Conference Proceeding
Chen, C., John, R., Twycross, J., & Garibaldi, J. M. (2017). Type-1 and interval type-2 ANFIS: a comparison

In a previous paper, we proposed an extended ANFIS architecture and showed that interval type-2 ANFIS produced larger errors than type-1 ANFIS on the well-known IRIS classification problem. In this paper, more experiments on both synthetic and real-w... Read More about Type-1 and interval type-2 ANFIS: a comparison.

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs (2017)
Conference Proceeding
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., & Garibaldi, J. M. (2017). Similarity-based non-singleton fuzzy logic control for improved performance in UAVs. https://doi.org/10.1109/FUZZ-IEEE.2017.8015440

© 2017 IEEE. As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle... Read More about Similarity-based non-singleton fuzzy logic control for improved performance in UAVs.

Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction (2017)
Conference Proceeding
Kayacan, E., Coupland, S., John, R., & Khanesar, M. A. (2017). Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)doi:10.1109/FUZZ-IEEE.2017.8015457

In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function–”Elliptic membersh... Read More about Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction.


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