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The objective of this research was to develop a new and highly accurate approach based on a fuzzy inference system (FIS) for the evaluation of usability based on ISO 9241-210:2019. In this study, a fully automated method of usability evaluation is used for interactive systems with a special look at interactive social robots.

Fuzzy logic uses as an intelligent computing technique to deal with uncertainty and incomplete data. Here this system is implemented using MATLAB fuzzy toolbox. This system attempted to quantify four criteria that correlate highly with ISO 9241-210:2019 criteria for the evaluation of interactive systems with maximum usability. Also, the system was evaluated with standard cases of computer interactive systems usability evaluation. The system did not need to train various data and to check the rules. Just small data were used to fine-tune the fuzzy sets. The results were compared against experimental usability evaluation with the statistical analysis.

It is found that there was a high strong linear relation between the FIS usability assessment and System Usability Scale (SUS) based usability assessment, and authors’ new method provides reliable results in the estimation of the usability.

In human-robot systems, human performance plays an important role in the performance of social interactive systems. In the present study, the proposed system has considered all the necessary criteria for designing an interactive system with a high level of user because it is based on ISO 9241-210:2019.

For future research, the system could be expanded with the training of historical data and the production of rules through integrating FIS and neural networks.

This system considered all essential criteria for designing an interactive system with a high level of usability because it is based on ISO 9241-210:2019. For future research, the system could be expanded with the training of historical data and the production of rules through integrating FIS and neural networks.

The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability.

This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements.

This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality.

Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs.

As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.

In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.

The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.

The purpose of this paper is to establish correlations among the input variables of production within themselves and input variables of consumption within themselves and to forecast the production and consumption of the rice.

The production and consumption of rice crop is governed by diverse variables. In the present study five key input variables for production of rice based on literature review and the authenticated data available from agricultural sources have been selected. These variables are area sown, agricultural workers (AW), area irrigated, growth rate and yield per hectare. On similar basis four key input variables responsible for consumption of rice are considered, namely, price of rice, population, poverty ratio and per capita net national product (NNP).

Correlation analysis showed that priority wise production of rice depends upon yield per hectare, percentage irrigation, AW and area sown. The growth rate is found to be having insignificant correlation with other variables of production and hence was omitted from subsequent study. Correlation analysis also showed that priority wise consumption depends upon whole sale price per ton, population and the per capita NNP. The poverty ratio is found to be having insignificant correlation with other variables of consumption and hence was omitted from subsequent study. The outcomes of the correlation analysis are utilized for designing rule base for fuzzy inference system (FIS) to forecast the production and consumption of the rice. Subsequently Bayesian technique is used to forecast production and consumption and its results are compared with the results of fuzzy inference analysis.

There are many techniques used for forecasting purpose but FIS and Bayesian technique outperform others. In the present study, the authors therefore focussed on these two techniques. Bayesian technique takes into account the expert opinion at the current conditions whereas FIS uses previously designed rule base. Besides discussing the appropriateness of these two techniques for forecasting production and consumption of rice, their forecasting outcomes will help in logistical and operational planning of the resources at national level, farmers’ level and traders’ level.

As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.

On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.

On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.

The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company.

The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based).

The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods.

Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

The motion control of unmanned ground vehicles (UGV) is a challenge in the industry of automation. The purpose of this paper is to propose a fuzzy inference system (FIS) based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.

The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the FIS, which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.

Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS. The reported simulation results are obtained using MATLAB software package. The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that is populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.

The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions. The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles. However, the methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.

This paper presents the design of a FIS and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. This facilitates an improved functionality of the operation of UGV.

The decline of the motoric and cognitive functions of the elderly and the high risk of changes in their vital signs lead to some disabilities that inconvenience them. This paper aims to assist the elderly in their daily lives through personalized and seamless technologies.

The authors developed a personalized adaptive system for elderly care in a smart home using a fuzzy inference system (FIS), which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system. Reflexive sensing is obtained from a body sensor and environmental sensor networks. Three methods comprising the FIS generation algorithm – fuzzy subtractive clustering (FSC), grid partitioning and fuzzy c-means clustering (FCM) – were compared to obtain the best prediction accuracy.

The results of the experiment showed that FSC produced the best F₁-score (96 per cent positioning accuracy, 94 per cent reflexive alert accuracy, 96 per cent air conditioning accuracy and 95 per cent lighting conditioning accuracy), whereas others failed to predict some classes and had lower validation accuracy results. Therefore, it is concluded that FSC is the best FIS generation method for our proposed system.

Personalized and seamless technologies for elderly implies life-share awareness, stakeholder awareness and community awareness.

This paper presents a model of personalized adaptive system based on their preferences and medical reference, which consists of a predictive positioning system, reflexive alert system and adaptive conditioning system.