Towards designing and measuring interpretable hierarchical fuzzy systems

This thesis presents a detailed study of the interpretability of hierarchical fuzzy systems (HFSs). It focuses on the development of a design guidelines framework for interpretable HFSs. This thesis aims to fill some of the gaps in the body of knowledge. Several research questions are raised, including: “How can the interpretability of HFSs be measured with indices?”, “How can complexity be comprehensively measured in HFSs?”, “How can user perception on the interpretability and complexity of HFSs be captured?” and “How can interpretable HFSs be designed?” Thus, to study the interpretability of HFSs, this thesis includes the following methodology discussed in different chapters. First, measures of interpretability are investigated, and an index measuring interpretability specifically in HFSs is introduced. The best way to know about interpretability is by learning how to measure it. Although many researchers have suggested indices to measure interpretability, none of them try to measure the interpretability of HFSs. Indeed, all of them only focus on measuring the interpretability of fuzzy logic systems (FLSs). This is due to the HFSs’ architecture, i.e., multiple subsystems, layers, and topologies, and this presents a significant challenge to measure the interpretability of HFSs. Based on this investigation, this study successfully introduces an initial index for measuring the interpretability of HFSs. The initial index is built based on the challenges arising from the structure of HFSs mentioned. Due to the subjective nature of the interpretability, the best way to validate the proposed measurements of interpretability of HFSs is by asking the users. However, it is not an easy task to get the user perception, particularly on the interpretability, and there is a lack of research on this issue. Therefore, the second focus of this thesis presents research on a new method of capturing user perceptions of the interpretability and also complexity of HFSs. This is the first time that user study has been used to obtain and assess both qualities in HFSs. Based on this, a new analysis of the relationship between interpretability and complexity of HFSs is presented, and this provides insights into the process of developing measures of interpretability of HFSs. However, rather than just using the user study to evaluate the measurements directly, this study also uses input from the user study to ‘guide’ the measurement of interpretability of HFSs in what is known as a participatory design approach. The participatory design approach enables the subjective views of a range of users to be taken into account in shaping the measurement of interpretability of HFSs. Thus, the use of the participatory design approach to configure the resulting measurement of interpretability of HFSs is also evaluated. Complexity is seen as an essential component in determining interpretability. In FLSs, complexity is expressed by the number of rules, variables, and fuzzy terms, called rule-based complexity. Several studies have used indicators (for example, the number of rules) to measure the complexity of FLSs. However, none of the studies considered the structure of HFSs, i.e., multiple subsystems, layers and varied topologies, which may also affect the complexity of HFSs. In addition, the user study revealed different perceptions of complexity in HFSs. Therefore, research is then presented on improving the complexity measurement in HFSs. The measurement is based on combining rule-based complexity with the structural complexity. Designing an interpretable HFS is a challenging task because of the need to define the interpretability of the architecture of the HFS (the subsystems, the input variables of each subsystem, and the interactions between subsystem), as well as the rules of each subsystem. To assist with this, a design guidelines framework for interpretable HFSs is produced. The framework is based on the measurement index of interpretability and complexity that is presented earlier. The framework consists of five guidelines for building interpretable HFSs. Finally, to demonstrate a design guidelines framework for interpretable HFSs on the realworld example, a design for an interpretable HFS for a neonatal intensive care unit (NICU) is produced. It is aimed to provide understandable decision support model to clinicians. In the medical context, it is essential for people to understand the importance of the system features. The HFSs is then practically illustrated by using real physiological data at NICU and compared with a flat FLS system. The results show that the design guidelines framework can offer the ability to design an interpretable HFS in practice efficiently

An Implementation of Internet-connected Indoor Dust Bin Waste Level Monitoring System for Office Use

The paper discusses an implementation of an internet-connected monitoring system to optimize waste collection within office space environment. The implementation consists of four parts – the physical dustbin module, network, middleware and user interface. The implementation uses NodeMCU board with embedded microcontroller to sense and transmit waste level data to a middleware over 802.11 wireless network, while the middleware aggregate data from all registered dustbins within the office area. The aggregated data is then stored in a database server for further analysis. The web-based user interface is used to monitor the dustbins waste level as well as historical data report. This information is vital for waste level management planning and prediction. Additionally, a Technology Acceptance Model survey result has shown that the monitoring system is perceived to be easy to use and useful for office management in monitoring and managing their waste

Hierarchical Fuzzy Systems: Interpretability and Complexity

Hierarchical fuzzy systems (HFSs) have been regarded as a useful solution for overcoming the major issues in fuzzy logic systems (FLSs), i.e., rule explosion due to the increase in the number of input variables. In HFS, the standard FLS are reformed into a low-dimensional FLS subsystem network. Moreover, the rules in HFS usually have antecedents with fewer variables than the rules in standard FLS with equivalent functions, because the number of input variables in each subsystem is less. Consequently, HFSs manage to decrease rule explosion, which minimises complexity and improves model interpretability. Nevertheless, the issues related to the question of “Does the complexity reduction of HFSs that have multiple subsystems, layers and different topologies really improve their interpretability?” are not clear and persist. In this paper, a comparison focusing on interpretability and complexity is made between two HFS’ topologies: parallel and serial. A detailed measurement of the interpretability and complexity with different configurations for both topologies is provided. This comparative study aims to examine the correlation between interpretability and complexity in HFS

Interpretability and complexity of design in the creation of fuzzy logic systems: a user study

In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducing the number of rules tends to reduce the complexity of FLSs, thus improving their interpretability. However, none of these studies have considered interpretability and complexity from human perspectives. Since interpretability is of a subjective nature, it is essential to see how people perceive interpretability and complexity particularly in relation to creating FLSs. Therefore, in this paper we have investigated this issue using an initial user study. This is the first time that a user study has been used to assess the interpretability and complexity of designs in relation to creating FLSs. The user study involved a range of expert practitioners in FLSs and received a diverse set of answers. We are interested to see whether, from the perspectives of people, FLSs are necessarily more interpretable when they are less complex in terms of their design. Although the initial user study is based on small samples (i.e., 25 participants), nevertheless this research provides initial insight into this issue that motivates our future research

An E-Quiz System: A Pathway to Improve English Learning for Preschoolers in Rural Areas

E-Quiz Systems is a system that replaces manual techniques, which are paperless methods often utilised in rural preschools. This paper presents an interactive web-based solution to assist students and teachers in rural regions with English language learning and instruction. This study attempts to motivate preschoolers in rural locations to learn English while providing instructors with interactive web-based methods to measure their students’ knowledge levels. A web-based application system is created to display e-quiz questions using the interactive multimedia principles of multiple media types and navigation with user input. Using interactive multimedia concepts, an E-Quiz System is designed to serve as an interactive quiz for English learning. ADDIE Model was the approach employed in this project. In addition, this project underwent testing for functionality and usability. As a contribution, it has been established that this study will give a pathway that piques children’s interest in learning English and makes it simpler for instructors to evaluate children’s growth using the E-Quiz System score

Interpretability and complexity of design in the creation of fuzzy logic systems: a user study

In recent years, researchers have become increasingly more interested in designing an interpretable Fuzzy Logic System (FLS). Many studies have claimed that reducing the complexity of FLSs can lead to improved model interpretability. That is, reducing the number of rules tends to reduce the complexity of FLSs, thus improving their interpretability. However, none of these studies have considered interpretability and complexity from human perspectives. Since interpretability is of a subjective nature, it is essential to see how people perceive interpretability and complexity particularly in relation to creating FLSs. Therefore, in this paper we have investigated this issue using an initial user study. This is the first time that a user study has been used to assess the interpretability and complexity of designs in relation to creating FLSs. The user study involved a range of expert practitioners in FLSs and received a diverse set of answers. We are interested to see whether, from the perspectives of people, FLSs are necessarily more interpretable when they are less complex in terms of their design. Although the initial user study is based on small samples (i.e., 25 participants), nevertheless this research provides initial insight into this issue that motivates our future research