Fuzzy adaptive parameter control of a late acceptance hyper-heuristic

A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of the heuristic application. In this paper, we present an initial study of a fuzzy system to control the list-size parameter of late- acceptance move acceptance method as a selection hyper-heuristic component. The performance of the fuzzy controlled selection hyper-heuristic is compared to its fixed parameter version and the best hyper-heuristic from a competition on the MAX-SAT problem domain. The results illustrate that a fuzzy control system can potentially be effective within a hyper-heuristic improving its performance

Exploring Constrained Type-2 fuzzy sets

Fuzzy logic has been widely used to model human reasoning thanks to its inherent capability of handling uncertainty. In particular, the introduction of Type-2 fuzzy sets added the possibility of expressing uncertainty even on the definition of the membership functions. Type-2 sets, however, don’t pose any restrictions on the continuity or convexity of their embedded sets while these properties may be desirable in certain contexts. To overcome this problem, Constrained Type-2 fuzzy sets have been proposed. In this paper, we focus on Interval Constrained Type-2 sets to see how their unique structure can be exploited to build a new inference process. This will set some ground work for future developments, such as the design of a new defuzzification process for Constrained Type-2 fuzzy systems

Fuzzy logic as-a-service for Ambient Intelligence Environments

Fuzzy Logic Systems (FLSs) are normally associated with dedicated hardware/software systems. However, the distributed and pervasive architecture of many modern hardware/software systems is driving increasing interest in pervasive, distributed FLSs. Achieving this vision will require the design of FLS implementations which support client-server models and more specifically, cloud-computing and service-oriented solutions. Here, FLSs become a globally accessible service that enables openness, device independence, load balancing, resource sharing and ultimately cost effectiveness. In this paper, the recently standardised fuzzy mark-up language (IEEE-1855) and proposed extensions are used for designing Web Services for FLS computations. The novelty of this approach is in integrating different FLS components (input collection, processing and output) into a single web service platform which uses a well specified language for communication over the Web via HTTP request/responses. The utility of this approach is shown in the context of implementing FLSs in Ambient Intelligent Environments

Exploring differences in interpretation of words essential in medical expert-patient communication

In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patient-completed questionnaires which employ words to capture aspects of patients’ well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients’ perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals

fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets

This paper presents a toolkit for automatic generation and analysis of fuzzy sets (FS) from data. Toolkits are vital for the wider dissemination, accessibility and implementation of theoretic work and applications on FSs. There are currently several toolkits in the literature that focus on knowledge representation and fuzzy inference, but there are few that focus on the automatic generation and comparison of FSs. As there are several methods of constructing FSs from data, it is important to have the tools to use these methods. This paper presents an open-source, python-based toolkit, named fuzzycreator, that facilitates the creation of both conventional and non-conventional (non-normal and non-convex) type-1, interval type-2 and general type-2 FSs from data. These FSs may then be analysed and compared through a series of tools and measures (included in the toolkit), such as evaluating their similarity and distance. An overview of the key features of the toolkit are given and demonstrations which provide rapid access to cutting-edge methodologies in FSs to both expert and non-expert users

Exploring differences in interpretation of words essential in medical expert-patient communication

In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patient-completed questionnaires which employ words to capture aspects of patients’ well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients’ perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals

Fuzzy Systems-as-a-Service in Cloud Computing

Fuzzy systems have become widely accepted and applied in a host of domains such as control, electronics or mechanics. The software for construction of these systems has traditionally been exploited from tools, platforms and languages run on-premise computing infrastructure. On the other hand, rise and ubiquity of the cloud computing model has brought a revolutionary way for computing services deployment. The boost of cloud services is leading towards increasingly specific service offering just as data mining and machine learning service. Unfortunately, so far, no definition for fuzzy system as service is available. This paper identifies this opportunity and focus on developing a proposal for fuzzy system-as-a-service definition. To achieve this, the proposal pursues three objectives: the complete description of cloud services for fuzzy systems using semantic technology, the composition of services and the exploitation of the model in cloud platforms for integration with other services. As an illustrative case, a real-world problem is addressed with the proposed specification.This work was supported by the Research Projects P12-TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Competitiveness - Government of Spain)

Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings