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All Outputs (32)

A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification (2019)
Conference Proceeding
Jimenez, M., Torres, M. T., John, R., & Triguero, I. (2019). A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). https://doi.org/10.1109/FUZZ-IEEE.2019.8858830

Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are task... Read More about A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification.

On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets (2019)
Conference Proceeding
D'Alterio, P., Garibaldi, J. M., & John, R. (2019). On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)https://doi.org/10.1109/FUZZ-IEEE.2019.8858942

Constrained type-2 fuzzy sets have been proposed as a tool to model type-2 fuzzy sets starting from a type-1 generator set with uncertainty. This constrained representation only defines as acceptable the embedded sets that have the same shape as the... Read More about On the Concept of Meaningfulness in Constrained Type-2 Fuzzy Sets.

OPTIMISED – Developing a State of the Art System for Production Planning for Industry 4.0 in the Construction Industry -Based Optimisation (2018)
Conference Proceeding
Teufl, S., Owa, K., Steinhauer, D., Castro, E., Herries, G., John, R., & Ratchev, S. (2018). OPTIMISED – Developing a State of the Art System for Production Planning for Industry 4.0 in the Construction Industry -Based Optimisation. In M. Peruzzini, M. Pellicciari, C. Bil, J. Stjepandić, & N. Wognum (Eds.), Transdisciplinary Engineering Methods for Social Innovation of Industry 4.0 : Proceedings of the 25th ISPE Inc. International Conference on Transdisciplinary Engineering, July 3 – 6, 2018 (731-740). https://doi.org/10.3233/978-1-61499-898-3-731

© 2018 The authors and IOS Press. Although it is not uncommon to have a predictive model of a factory, these models are often simplistic in nature. Such models rarely reflect the current operating performance of the system, use simple and separate da... Read More about OPTIMISED – Developing a State of the Art System for Production Planning for Industry 4.0 in the Construction Industry -Based Optimisation.

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks (2018)
Conference Proceeding
Huynh, V. S. H., Radenkovic, M., & John, R. (2018). Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks.

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges rela... Read More about Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks.

Interval type-2 intuitionistic fuzzy logic systems - A comparative evaluation (2018)
Conference Proceeding
Eyoh, I., John, R., & De Maere, G. (2018). Interval type-2 intuitionistic fuzzy logic systems - A comparative evaluation. In J. Medina, M. Ojeda-Aciego, J. L. Verdegay, D. A. Pelta, I. P. Cabrera, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018 (687-698). https://doi.org/10.1007/978-3-319-91473-2_58

Several fuzzy modeling techniques have been employed for handling uncertainties in data. This study presents a comparative evaluation of a new class of interval type-2 fuzzy logic system (IT2FLS) namely: interval type-2 intuitionistic fuzzy logic sys... Read More about Interval type-2 intuitionistic fuzzy logic systems - A comparative evaluation.

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) (728-733). https://doi.org/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.

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. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications; 11th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE); and 14th IEEE International Conference on Embedded Software and Systems, (901-908). https://doi.org/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.

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.

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.

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.

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) (1-7). https://doi.org/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.

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. In Proceedings - 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). 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.

Tuning a Simulated Annealing metaheuristic for cross-domain search (2017)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. (in press). Tuning a Simulated Annealing metaheuristic for cross-domain search.

Simulated Annealing is a well known local search metaheuristic used for solving computationally hard optimization problems. Cross-domain search poses a higher level issue where a single solution method is used with minor, preferably no modification f... Read More about Tuning a Simulated Annealing metaheuristic for cross-domain search.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Conference Proceeding
Tyasnurita, R., Özcan, E., & John, R. (2017). Learning heuristic selection using a time delay neural network for open vehicle routing.

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

A modified indicator-based evolutionary algorithm (mIBEA) (2017)
Conference Proceeding
Li, W., Özcan, E., John, R., Drake, J. H., Neumann, A., & Wagner, M. (2017). A modified indicator-based evolutionary algorithm (mIBEA).

Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the sea... Read More about A modified indicator-based evolutionary algorithm (mIBEA).

R-fuzzy sets and grey system theory (2017)
Conference Proceeding
Singh Khuman, A., Yang, Y., John, R., & Liu, S. (2017). R-fuzzy sets and grey system theory. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). https://doi.org/10.1109/SMC.2016.7844949

This paper investigates the use of grey theory to en- hance the concept of an R-fuzzy set, with regards to the precision of the encapsulating set of returned significance values. The use of lower and upper approximations from rough set theory, allow... Read More about R-fuzzy sets and grey system theory.

A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts (2016)
Conference Proceeding
Alqahtani, S. M., & John, R. (2016). A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts.

The use of Internet has been increasing day by day and the internet traffic is exponentially increasing. The services providers such as web services providers, email services providers, and cloud service providers have to deal with mi... Read More about A comparative study of different fuzzy classifiers for cloud intrusion detection systems' alerts.