Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

The integration of deep learning in healthcare has propelled advancements in diagnostics and decision support. However, the inherent opacity of deep neural networks (DNNs) poses challenges to their acceptance and trust in clinical settings. This survey paper delves into the landscape of explainable deep learning techniques within the healthcare domain, offering a thorough examination of deep learning explainability techniques. Recognizing the pressing need for nuanced interpretability, we extend our focus to include the integration of fuzzy logic as a novel and vital category. The survey begins by categorizing and critically analyzing existing intrinsic, visualization, and distillation techniques, shedding light on their strengths and limitations in healthcare applications. Building upon this foundation, we introduce fuzzy logic as a distinct category, emphasizing its capacity to address uncertainties inherent in medical data, thus contributing to the interpretability of DNNs. Fuzzy logic, traditionally applied in decision-making contexts, offers a unique perspective on unraveling the black box of DNNs, providing a structured framework for capturing and explaining complex decision processes. Through a comprehensive exploration of techniques, we showcase the effectiveness of fuzzy logic as an additional layer of interpretability, complementing intrinsic, visualization, and distillation methods. Our survey contributes to a holistic understanding of explainable deep learning in healthcare, facilitating the seamless integration of DNNs into clinical workflows. By combining traditional methods with the novel inclusion of fuzzy logic, we aim to provide a nuanced and comprehensive view of interpretability techniques, advancing the transparency and trustworthiness of deep learning models in the healthcare landscape.

Fuzzy logic is a way of many-valued computing logic that deals with the truth values of the variables between 0 and 1, unlike the conventional Boolean logic. Membership functions are used to depict the fuzzy values of given variable. Though membership functions are determined through expert’s opinion, however, the one estimated through heuristic algorithms is the preferable methods. Membership functions determined through statistical and knowledge engineering methods are usually application dependent and cannot be applied on different datasets. This research focuses on generating the parametric values of the triangular membership function using a novel method. Initially, the Fuzzy C-means algorithm is utilized to generate the parameters values of the Gaussian membership function. Using a set of equations, these values then estimate the parameters of the triangular membership function. The proposed method is applied to the quality of web services data. From the results it is verified that the new approach of generating triangular membership functions can be adopted.

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices. Electricity price forecasting have been a critical input to energy corporations’ strategic decision-making systems over the last 15 years. Many strategies have been utilized for price forecasting in the past, however Artificial Intelligence Techniques (Fuzzy Logic and ANN) have proven to be more efficient than traditional techniques (Regression and Time Series). Fuzzy logic is an approach that uses membership functions (MF) and fuzzy inference model to forecast future electricity prices. Fuzzy c-means (FCM) is one of the popular clustering approach for generating fuzzy membership functions. However, the fuzzy c-means algorithm is limited to producing only one type of MFs, Gaussian MF. The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions. As a result, for the best and most improved results for electricity price forecasting, an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required. Therefore, the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm. The approach is used to compute and improve forecasting accuracy for electricity prices, where Australian Energy Market Operator (AEMO) data is used. The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.