Search

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R 2 , R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R 2 and R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R 2 and R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R 2 and R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model's capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model's superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application., (© 2024. The Author(s).)

Interstate pipelines are the most efficient and feasible mean of transport for crude oil and gas within boarders. Assessing the risks of these pipelines is challenging despite the evolution of computational fuzzy inference systems (FIS). The computational intricacy increases with the dimensions of the system variables especially in the typical Takagi-Sugeno (T-S) fuzzy-model. Typically, the number of rules rises exponentially as the number of system variables increases and hence, it is unfeasible to specify the rules entirely for pipeline risk assessments. This work proposes the significance of indexing pipeline risk assessment approach that is integrated with subtractive clustering fuzzy logic to address the uncertainty of the real-world circumstances. Hypothetical data is used to setup the subtractive clustering fuzzy-model using the fundamental rules and scores of the pipeline risk assessment indexing method. An interstate crude-oil pipeline in Egypt is used as a case study to demonstrate the proposed approach., (© 2022. The Author(s).)

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively., Competing Interests: This study was supported by the National Science Foundation of China (Grant U2033213), the Open Fund of Key Laboratory of Flight Techniques and Flight Safety of the CAAC (Grant FZ2021KF12), the Construction Plan of Scientific Research and Innovation Team for Civil Aviation Flight University of China (Grant JG2022-24), and the Fundamental Research Funds for the Central Universities (Grant PHD2023-030, ZHMH2022-014)., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.

The white potato worm (Premnotrypes Vorax (Hustache)) is one of the pests that causes the greatest damage to the potato crop and the greatest economic losses to the grower; therefore, knowing its life cycle and estimating its intrinsic growth rate is crucial for selecting an appropriate chemical control method, in order to reduce the environmental impact and ensure a profitable production suitable for consumption. In this article, we present a fuzzy Malthusian model describing the evolution of the white potato worm in the crop, considering that the intrinsic growth rate and the reported initial data on the problem are of fuzzy nature. The main contributions and novelty of this paper are summarized in the following two aspects: first, the estimation of the intrinsic growth rate of the white potato worm, in function of the temperature, by using a Takagi-Sugeno-Kang type fuzzy rule-based model; and second, since in practice the initial white potato worm population in a crop is subjective, imprecise and vague, knowing the intrinsic growth rate, we propose and solve a fuzzy initial value problem to determine the evolution in time of the white potato worm population. In conclusion, given a weekly average temperature, it is possible to know the white potato worm population per unit area oscillating in an interval whose length depends on the degree of inaccuracy of the initial population and the intrinsic growth rate. This study can be relevant for grower decision making in terms of the type and frequency of pest control on his crop., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions., Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions., Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts., Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper., Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness., (© 2023. The Author(s).)

In the realm of modern medicine and biology, vast amounts of genetic data with high complexity are available. However, dealing with such high-dimensional data poses challenges due to increased processing complexity and size. Identifying critical genes to reduce data dimensionality is essential. The filter-wrapper hybrid method is a commonly used approach in feature selection. Most of these methods employ filters such as MRMR and ReliefF, but the performance of these simple filters is limited. Rough set methods, on the other hand, are a type of filter method that outperforms traditional filters. Simultaneously, many studies have pointed out the crucial importance of good initialization strategies for the performance of the metaheuristic algorithm (a type of wrapper-based method). Combining these two points, this paper proposes a novel filter-wrapper hybrid method for high-dimensional feature selection. To be specific, we utilize the variant of bWOA (binary Whale Optimization Algorithm) based on Hybrid Fuzzy Rough Set to perform attribute reduction, and the reduced attributes are used as prior knowledge to initialize the population. We then employ metaheuristics for further feature selection based on this initialized population. We conducted experiments using five different algorithms on 14 UCI datasets. The experiment results show that after applying the initialization method proposed in this article, the performance of five enhanced algorithms, has shown significant improvement. Particularly, the improved bMFO using our initialization method: fuzzy_bMFO outperformed six currently advanced algorithms, indicating that our initialization method for metaheuristic algorithms is suitable for high-dimensional feature selection tasks., Competing Interests: Declaration of competing interest This work was supported by the National Key R&D Program of China under Grant Nos. 2021ZD0112501 and 2021ZD0112502; the National Natural Science Foundation of China under Grant Nos. U22A2098, 62172185, 62206105 and 62202200., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Even if assessing binary classifications is a common task in scientific research, no consensus on a single statistic summarizing the confusion matrix has been reached so far. In recent studies, we demonstrated the advantages of the Matthews correlation coefficient (MCC) over other popular rates such as cross-entropy error, F 1 score, accuracy, balanced accuracy, bookmaker informedness, diagnostic odds ratio, Brier score, and Cohen's kappa. In this study, we compared the MCC to other two statistics: prevalence threshold (PT), frequently used in obstetrics and gynecology, and Fowlkes-Mallows index, a metric employed in fuzzy logic and drug discovery. Through the investigation of the mutual relations among three metrics and the study of some relevant use cases, we show that, when positive data elements and negative data elements have the same importance, the Matthews correlation coefficient can be more informative than its two competitors, even this time., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Inertial sensors can serve as inertial references for space missions and require charge management systems to maintain their on-orbit performance. To achieve non-contact charge management through UV discharge, effective control strategies are necessary to improve the optical power output performances of UV light sources while accurately modeling their operating characteristics. This paper proposes a low-power photo-electro-thermal model for widely used AlGaN-based UV LEDs, which comprehensively considers the interaction of optical, electrical, and thermal characteristics of UV LEDs during low-power operations. Based on this model, an optical power control system utilizing a fuzzy adaptive PID controller is constructed, in which a switch is introduced to coordinate the working state of the controller. Thus, the steady-state performance is effectively improved while ensuring dynamic performance. The results show that the proposed model has an average prediction error of 5.8 nW during steady-state operations, and the fuzzy adaptive PID controller with a switch can reduce the fluctuation of light output to 0.67 nW during a single discharge task, meeting the charge management requirements of high-precision inertial sensors.

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The goal of this study is to introduce an effective load frequency control scheme with the integration of tidal turbines in a standalone microgrid (μG) system. As standalone μG experiences lower inertia and lacks primary frequency control, the use of variable tidal turbines in the de-loaded region may be accepted as one of the feasible solutions for managing frequency regulation issues. In this condition, the de-load region alludes to an area where tidal turbines liberate their accumulated kinetic energy in rotational parts pursuing frequency fluctuations. An effectual cascade fractional order fuzzy PID-integral double derivative (CFOFPID-IDD) controller suggested for efficient utilization of tidal turbines, whose design variables are tuned through a recently appeared Jaya algorithm. An investigation is made between the acquired outcomes of the studied CFOFPID-IDD droop controller with fractional order fuzzy PID droop control to analyze the proposed strategy performance in various load conditions, with different physical constraints like time delay, dead zone, and generation rate constraints. Moreover, the sensitivity test reveals that the Jaya-optimized CFOFPID-IDD controller can undergo ± (10-25) % variation in various coefficients without retuning the design variable values. The simulation outcomes validate the effectiveness and adequacy of the proposed regulator., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In this paper, we design a proportional integral observe for a nonlinear synchronous reluctance motor described by a Takagi-Sugeno multi-model. In this design, both states and unknown inputs are estimated simultaneously. First, the mathematical nonlinear model of the synchronous reluctance motor is established, then it transformed into a Takagi-Sugeno exact form by a simple polytopic transformation. In The second step, the proposed observer is designed to the obtained fuzzy system. Convergence conditions are expressed under a linear matrix inequality formulation using the second Lyapunov theorem, which guarantee a bounded error. Observer gains are obtained by solving a sum of constraints. For validation purposes, a hardware-in-the-loop implementation were carried out leading to results that clearly demonstrate the performance and effectiveness of the proposed technique., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

Considering the frequency effect of external disturbances, this paper concerns the finite-frequency control problem for nonlinear semi-Markov jump systems (SMJSs) with piecewise transition probabilities (TPs) via the Takagi-Sugeno (T-S) fuzzy modeling approach. More precisely, the piecewise TPs are assumed to switch stochastically within limits, which implies that the corresponding distributions of sojourn time (ST) also vary randomly. Furthermore, another upper semi-Markov chain is utilized to characterize TPs variation in a finite set. With the aid of Finsler's lemma and Parseval's theorem, sufficient criteria for the controlled SMJS are constructed to meet the desired disturbance attenuation performance in the frequency domain. Then, the mode-dependent controllers are developed to use the TP information more efficiently. In particular, a novel matrix method is proposed to decouple the mode-dependent variables and controller gains by selecting slack matrices. Eventually, a numerical example is transmitted to demonstrate the validity and merits of established results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

Strong disturbances, time delays, and coupling effects complicate the control of evaporator systems. In this paper, to overcome these difficulties a three-stage controller design is given and as a first step a disturbance observer (DOB) is designed. Then, fractional order PI λ D μ controllers (PI λ D μ FOC) with inverted decoupling structure are realized. Finally, PI λ D μ FOC is enhanced via DOB and applied to control of a considered forced circulation evaporator system. Controlling the evaporator system with the DOB-enhanced PI λ D μ FOC is showed more accurate and precise control results when compared with the optimally tuned PID and fuzzy logic controller (FLC). The DOB-enhanced PI λ D μ FOC method has remarkable superiority in disturbance rejection and the proposed control method is improved the tracking ability of the system for reference changes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

At this stage, in the research process of sports public service efficiency evaluation and analysis, due to the use of accurate data analysis methods, there are problems of low evaluation reliability and low analysis speed. Based on this, this paper studies the application of an information strategy based on the intuitionistic fuzzy data matching method in the evaluation and analysis of sports public service efficiency. The efficiency evaluation and analysis model of sports public service based on the intuitive fuzzy information algorithm is established. According to the characteristics of different types of sports public service, different types of data analysis methods are used to evaluate the efficiency, and the optimization of the efficiency analysis model of sports public service is realized according to the service demand and data type of different sports events. Finally, an experiment is designed to quantitatively evaluate the accuracy, stability, and reliability of the sports public service analysis model. The results show that the efficiency intelligent evaluation and analysis model based on the intuitive fuzzy information algorithm can effectively select the optimal sports public service evaluation countermeasures and rules according to the characteristics and data types of different sports events, realize the multi-dimensional accuracy classification of different types of sports events, and effectively improve the reliability of sports public service efficiency evaluation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yu Shao and Rong Bo.)

Fuzzy cognitive maps (FCMs) are a powerful tool for system modeling, which can be used for static and dynamic analysis. However, traditional FCMs are usually learned by gradient-based methods, and the adopted sigmoid nonlinear activation function frequently causes gradient saturation. These two shortcomings set a limit on the modeling accuracy. To overcome those problems, we propose in this paper a new FCM with two improvements. First, the rectified linear unit (ReLu) activation function is adopted to replace the sigmoid function. Second, a newly proposed quasi-oppositional bare bone imperialist competition algorithm (QBBICA) is used to learn the FCM. The improved FCM is used to predict the employment rate of graduates from Liren College, Yanshan University. Experimental results show that the improved FCM is effective in employment rate prediction., Competing Interests: The authors have declared that no competing interests exist.

This paper analyzes the limitations of the current public management performance evaluation system, puts forward a public management performance evaluation model based on fuzzy clustering, and expounds on the theoretical framework and principle of this method. In addition, this paper introduces the concept and method of public management performance evaluation into the evaluation content system of government under the rule of law and realizes the combination of government under the rule of law theory and public management performance evaluation methodology. At the same time, through the analysis of the organizational system, technical system, institutional mechanism, result orientation, public satisfaction, and related index content, a public management performance evaluation index system is designed. Through macro and micro, qualitative and quantitative analysis and evaluation, the performance evaluation index system is improved, which provides an objective basis for forecasting and decision-making, control, and adjustment. The research shows that the highest accuracy of this clustering method can reach about 96%, and its accuracy is about 10% higher than that of the ID3 algorithm. It is expected that this research can make some contributions to the research of the public management performance evaluation index system. At the same time, it highlights the importance of research on performance evaluation of government ruled by law., Competing Interests: The author declares no conflicts of interest., (Copyright © 2022 Rong Zhang.)

In this paper, the T-S fuzzy observer-based adaptive tracking control of the biological system with stage structure is studied. First, a biological model with stage structure is established, and its stability at the equilibrium points is analyzed. Considering the impact of reducing human activities on the biological population, an adaptive controller is applied to the system. Since it is difficult to measure density directly, a fuzzy state observer is designed, which is used to estimate the density of biological population. At the same time, the density of predators can track the desired density through the adjustment of adaptive controller. The stability of the biological system is guaranteed, and the observer error and tracking error are shown to converge to zero. Finally, the effectiveness of the proposed adaptive control method is verified by numerical simulation.

In order to enhance the speed control performance of the brushless DC motor (BLDCM), a novel proportion integration differentiation (PID) is proposed in this paper by using dual fuzzy logic systems (FLSs) with harmony search algorithm (HSA) optimization, which is called DFPID-HSA. Firstly, the FLS1 in DFPID-HSA locks the three coefficients of the PID controller in an extensive range on the basis of the system error and error change rate. Then, the FLS2 is optimized by HSA (HSA-F2) to obtain the precise correction of the three coefficients. To get the optimal global harmony better, the improved dynamic adjustment mode is used for the pitch adjustment rate (PAR) and distance bandwidth (BW) in HSA, and the triple selection method is adopted in the composition harmony section to realize the global search. Finally, DFPID-HSA provides the optimal supply control signal to BLDCM so that it can control the speed effectively. Moreover, the stability of the system is analyzed by the pole, Lyapunov, and Nyquist determination methods. And the sensitivity analysis of DFPID-HSA is carried out under the condition of different motor's mechanical parameters to check its robustness. In addition, the superiority of DFPID-HSA is verified by MATLAB simulation and experiment platform., (© 2022. The Author(s).)

In order to shorten the image registration time and improve the imaging quality, this paper proposes a fuzzy medical computer vision image information recovery algorithm based on the fuzzy sparse representation algorithm. Firstly, by constructing a computer vision image acquisition model, the visual feature quantity of the fuzzy medical computer vision image is extracted, and the feature registration design of the fuzzy medical computer vision image is carried out by using the 3D visual reconstruction technology. Then, by establishing a multidimensional histogram structure model, the wavelet multidimensional scale feature detection method is used to achieve grayscale feature extraction of fuzzy medical computer vision images. Finally, the fuzzy sparse representation algorithm is used to automatically optimize the fuzzy medical computer vision images. The experimental results show that the proposed method has a short image information registration time, less than 10 ms, and has a high peak PSNR. When the number of pixels is 700, its peak PSNR can reach 83.5 dB, which is suitable for computer image restoration., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2022 Yi Tang et al.)

In order to effectively assist industrial designers in the color scheme design of cultural and creative products and output color schemes that meet users' image preferences, an interactive color scheme design method for cultural and creative products based on triangular fuzzy number is proposed. Through the group consistency decision based on triangular fuzzy number, an interactive genetic evolution of the color scheme population of cultural and creative products is performed to generate color schemes that satisfy the group consistency imagery preference and satisfaction, and the final selection scheme is calculated by color beauty. This paper verifies that the method can effectively integrate the imagery preferences of group users and help industrial designers to better design color schemes for cultural and creative products through the results of multiuser decision consistency., Competing Interests: The author declares that there are no conflicts of interest to report regarding the present study., (Copyright © 2022 Taihua Lan.)

The quantity of scientific images associated with patient care has increased markedly in recent years due to the rapid development of hospitals and research facilities. Every hospital generates more medical photographs, resulting in more than 10 GB of data per day being produced by a single image appliance. Software is used extensively to scan and locate diagnostic photographs to identify patient's precise information, which can be valuable for medical science research and advancement. An image recovery system is used to meet this need. This paper suggests an optimized classifier framework focused on a hybrid adaptive neuro-fuzzy approach to accomplish this goal. In the user query, similarity measurement, and the image content, fuzzy sets represent the vagueness that occurs in such data sets. The optimized classifying method 'hybrid adaptive neuro-fuzzy is enhanced with the improved cuckoo search optimization. Score values are determined by utilizing the linear discriminant analysis (LDA) of such classified images. The preliminary findings indicate that the proposed approach can be more reliable and effective at estimation than can existing approaches.

Depression is a common and serious medical disorder that negatively affects the mood and the emotions of people, especially adolescents. In this paper, a novel framework for automatically creating Fuzzy Cognitive Maps (FCMs) is proposed. It is applied for the estimation of the severity of depression among adolescents, based on their electroencephalogram (EEG). The introduced Constructive FCM (CFCM) utilizes features extracted by a Constructive Fuzzy Representation Model (CFRM), which conduces to detect in a more intuitive way the cause-and-effect relationships between the brain activity and depression. CFCM contributes to limiting the participation of experts, and the manual interventions in the traditional construction of FCMs, it provides an embedded mechanism for dimensionality reduction, and it constitutes an inherently interpretable approach to decision making, while being uncertainty-aware and simple to implement. The results of the experiments, using a recent publicly available dataset, demonstrate the effectiveness of the proposed framework and highlight its advantages.

Aiming at the selection research gap of AG600 amphibious aircraft for salvage of life at sea, the selection index system of AG600 has been established, and an interval intuitionistic fuzzy selection decision model based on fuzzy entropy and score function is constructed. An evaluation index system with 8 indexes was determined based on the rules of the applicability, safety, and accessibility by questionnaire and statistical analysis. Different from the traditional method of determining the fuzzy number of each index solely by experts' scoring, this paper establishes a 5-grade fuzzy evaluation grade and index membership function, and the interval intuitionistic fuzzy number of the index is determined by combining subjective and objective methods. Fuzzy entropy and score function are used to calculate the weights of each index and AG600 selection score for different accidents, so as to obtain the matching degree of AG600 selection in marine life accidents more scientifically and reasonably. Finally, the effectiveness of the proposed model is demonstrated by a practical case., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Baoku Du et al.)

The development of a machine's condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.

Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application., Competing Interests: The authors declare that there are no conflicts of interest with any financial organizations regarding the material reported in this manuscript., (Copyright © 2022 Dengfeng Zhang and Yupeng Li.)

In the past, the possibilistic C -means clustering algorithm (PCM) has proven its superiority on various medical datasets by overcoming the unstable clustering effect caused by both the hard division of traditional hard clustering models and the susceptibility of fuzzy C -means clustering algorithm (FCM) to noise. However, with the deep integration and development of the Internet of Things (IoT) as well as big data with the medical field, the width and height of medical datasets are growing bigger and bigger. In the face of high-dimensional and giant complex datasets, it is challenging for the PCM algorithm based on machine learning to extract valuable features from thousands of dimensions, which increases the computational complexity and useless time consumption and makes it difficult to avoid the quality problem of clustering. To this end, this paper proposes a deep possibilistic C -mean clustering algorithm (DPCM) that combines the traditional PCM algorithm with a special deep network called autoencoder. Taking advantage of the fact that the autoencoder can minimize the reconstruction loss and the PCM uses soft affiliation to facilitate gradient descent, DPCM allows deep neural networks and PCM's clustering centers to be optimized at the same time, so that it effectively improves the clustering efficiency and accuracy. Experiments on medical datasets with various dimensions demonstrate that this method has a better effect than traditional clustering methods, besides being able to overcome the interference of noise better., Competing Interests: All the authors do not have any possible conflicts of interest., (Copyright © 2022 Yuxin Gu et al.)

An electrical device that transforms the electricity into the waves of radio and vice versa is termed the antenna. Its main deployment is in the transmitter and receiver of the antenna. While transmission, the transmitter of radio at the extremities of the antenna furnishes the electricity which oscillates at the frequency of radio wave and energy is released as current as em waves. Some of the voltage is formed from the em wave that is invaded at the point of receiving to amplify the receiver. This study focuses on the analysis of the satellite system to aid in mobile antenna tracking. It also examines the techniques for fuzzy control which make up traditional networks that are used. Initially, a basic idea of tracking loops with stabilized antennas was suggested in light of the requirement for the margin of phase and bandwidth. If the gain of the track is reduced due to changes in attributes and throughput, it will be reduced. In addition, fuzzy regulators and PID constituents are used to enhance the loop. The results indicate that the higher and lower antenna tracking gains within the loop were the best fit and the loop's fluctuations are reduced. A controller based on fuzzy logic can be most efficient due to its simplicity and robustness. It is also discovered that fuzzy logic controllers are evaluated by their behavior in relation. This paper presents an evaluation of the controllers in fuzzy logic, which is based on its integration with conventional controllers. There are three gains in PID's regulator PID and every gain can be used to control the variables of inputs and outcomes. The effects of the responses were analyzed and were compared. The commonality was discovered in the results according to the increase in time for II/6 and II/3 based on PID's regulator PID stability, it can be improved by this system, and there is a reduction in the duration of stability. Furthermore, the period of stability may be reduced through the fusion of PID and fuzzy. The effectiveness of the system could be enhanced by the implementation of the neural network. It is also possible to design the two types of control that could be used to control the proposed solid platform., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2022 R. Obulakonda Reddy et al.)

Background: Image segmentation is considered an important step in image processing. Fuzzy c-means clustering is one of the common methods of image segmentation. However, this method suffers from drawbacks, such as sensitivity to initial values, entrapment in local optima, and the inability to distinguish objects with similar color intensity. This paper proposes the hybrid Fuzzy c-means clustering and Gray wolf optimization for image segmentation to overcome the shortcomings of Fuzzy c-means clustering. The Gray wolf optimization has a high exploration capability in finding the best solution to the problem, which prevents the entrapment of the algorithm in local optima. In this study, breast cytology images were used to validate the methods, and the results of the proposed method were compared to those of c-means clustering., Results: FCMGWO has performed better than FCM in separating the nucleus from the other dark objects in the cell. The clustering was validated using Vpc, Vpe, Davies-Bouldin, and Calinski Harabasz criteria. The FCM and FCMGWO methods have a significant difference with respect to the Vpc and Vpe indexes. However, there is no significant difference between the performances of the two clustering methods with respect to the Calinski-Harabasz and Davies-Bouldin indices. The results indicate the better efficacy of the proposed method., Conclusions: The hybrid FCMGWO algorithm distinguishes the cells better in images with less detail than in images with high detail. However, FCM exhibits unacceptable performance in both low- and high-detail images., (© 2022. The Author(s).)

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.

As a powerful technique to merge complementary information of original images, infrared (IR) and visible image fusion approaches are widely used in surveillance, target detecting, tracking, and biological recognition, etc. In this paper, an efficient IR and visible image fusion method is proposed to simultaneously enhance the significant targets/regions in all source images and preserve rich background details in visible images. The multi-scale representation based on the fast global smoother is firstly used to decompose source images into the base and detail layers, aiming to extract the salient structure information and suppress the halos around the edges. Then, a target-enhanced parallel Gaussian fuzzy logic-based fusion rule is proposed to merge the base layers, which can avoid the brightness loss and highlight significant targets/regions. In addition, the visual saliency map-based fusion rule is designed to merge the detail layers with the purpose of obtaining rich details. Finally, the fused image is reconstructed. Extensive experiments are conducted on 21 image pairs and a Nato-camp sequence (32 image pairs) to verify the effectiveness and superiority of the proposed method. Compared with several state-of-the-art methods, experimental results demonstrate that the proposed method can achieve more competitive or superior performances according to both the visual results and objective evaluation.

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c -means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Zhen Yu et al.)

This paper presents the application of adaptive fuzzy sliding mode control (AFSMC) for the respiratory system to assist the patients facing difficulty in breathing. The ventilator system consists of a blower-hose-patient system and patient's lung model with nonlinear lung compliance. The AFSMC is based on two components: singleton control action and a discontinuous term. The singleton control action is based on fuzzy logic with adjustable tuning parameters to approximate the perfect feedback linearization control. The switching control law based on the sliding mode principle aims to minimize the estimation error between approximated single fuzzy control action and perfect feedback linearization control. The proposed control strategy manipulated the airway flow delivered by the ventilator such that the peak pressure will remain under critical values in presence of unknown patient-hose-leak parameters and patient breathing effort. The closed-loop stability of AFSMC will be proven in the sense of Lyapunov. For comparative analysis, classical PID and sliding mode controllers are also designed and implemented for mechanical ventilation problems. For performance analysis, numerical simulations were performed on a mechanical ventilator simulator. Simulation results reveal that the proposed controller demonstrates better tracking of targeted airway pressure compared with its counterparts in terms of faster convergence, less overshoot, and small tracking error. Hence, the proposed controller provides useful insight for its application to real-world scenarios., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Ibrahim M. Mehedi et al.)

This paper aims to present the analysis and development of a complete electronic smart meter that is able to perform four-quadrant measurements, act as a three-phase shunt active power filter (APF), and control three-phase induction motors by stator flux estimation. A transmission control protocol together with Internet protocol (TCP/IP) communication protocol for the remote access of measurement data is embedded into the application to securely transmit reliable information. An artificial neural network trained with particle swarm optimization is used for stator flux estimation, and a fuzzy logic controller is adopted to regulate the power converter DC bus voltage. The present work gathers knowledge from multidisciplinary fields, and all applied techniques have not been proposed altogether before. All control functions are embedded into a field-programmable gate array (FPGA) device, using VHSIC Hardware Description Language (VHDL), to enhance efficiency taking advantage of parallelism and high speed. An FPGA-in-the-loop cosimulation technique was first applied to prove the control functions' functionality, and, later, experimental evaluations are conducted to finally prove equipment operation and reliability.

This paper introduces a kernel based fuzzy clustering approach to deal with the non-linear separable problems by applying kernel Radial Basis Functions (RBF) which maps the input data space non-linearly into a high-dimensional feature space. Discovering clusters in the high-dimensional genomics data is extremely challenging for the bioinformatics researchers for genome analysis. To support the investigations in bioinformatics, explicitly on genomic clustering, we proposed high-dimensional kernelized fuzzy clustering algorithms based on Apache Spark framework for clustering of Single Nucleotide Polymorphism (SNP) sequences. The paper proposes the Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) which inherently uses another proposed Kernelized Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithm. Both the approaches completely adapt the Apache Spark cluster framework by localized sub-clustering Resilient Distributed Dataset (RDD) method. Additionally, we are also proposing a preprocessing approach for generating numeric feature vectors for huge SNP sequences and making it a scalable preprocessing approach by executing it on an Apache Spark cluster, which is applied to real-world SNP datasets taken from open-internet repositories of two different plant species, i.e., soybean and rice. The comparison of the proposed scalable kernelized fuzzy clustering results with similar works shows the significant improvement of the proposed algorithm in terms of time and space complexity, Silhouette index, and Davies-Bouldin index. Exhaustive experiments are performed on various SNP datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with proposed KSLFCM and other scalable clustering algorithms, i.e., SRSIO-FCM, and SLFCM., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background and Objective: The accurate segmentation of pre-treatment and post-treatment organs is always perceived as a challenging task in medical image analysis field. Especially, in those situations where the amount of data set is limited, the researchers are compelled to design unsupervised model for segmentation. In this paper, we propose a novel dynamically learned particle swarm optimization based neighborhood influenced fuzzy c-means (DLPSO-NIFCM) clustering (unsupervised learning model) for solving pre-treatment and post-treatment organs segmentation problems. The proposed segmentation technique has been successfully applied to segment the liver parts from the Computed Tomography (CT) images of abdomen and also the lung parenchyma from the lungs CT images., Methodology: In the proposed method, we formulate a primary convex objective function by considering the membership value of a pixel as well as the membership of its other neighboring pixels. Then we apply a new algebraic transformation on the primary objective function to design a new and more suitable objective function without losing convexity of the primary objective function. This new objective function is compatible for hybridization with any heuristic search technique in true sense. In this work, we propose a dynamically learned PSO to obtain the initial cluster centroids from the final objective function. Finally, we use a graph-based isolation mechanism for refining the segmentation results., Results and Conclusion: This hybrid method, along with the restructured single variable objective function of the distance, leads to accurate clustering results with relatively lesser converging time as compared to the state-of-the-art methods. The segmentation results, obtained through several experiments with real CT images, are encouraging. The numerical values of different performance metrics obtained over the same data set confirm that the proposed algorithm performs better with respect to the state-of-the-art methods. Hence, we may consider the proposed method as a promising tool for clustering and CT image segmentation in a Computer Aided Diagnostic (CAD) system., (Copyright © 2021. Published by Elsevier B.V.)

In this paper, the finite-time stability and control of a kind of singular bio-economic systems with stochastic fluctuations are investigated. When economic profit is no longer a constant but a variable, the system shows distinct dynamic behavior. First, a singular system is proposed to describe the bio-economic system with stochastic fluctuation. Then a singular stochastic T-S fuzzy model is established based on T-S fuzzy system theory. Second, a sufficient condition is proposed to satisfy finite-time stochastic stability of bio-economic system. On this basis, a fuzzy state feedback controller is designed which can make corresponding closed-loop singular stochastic bio-economic system admissible in finite-time, and the states of the system can be driven to a bounded range through the management of the open resource. Finally, the validity of the results is verified through the numerical simulation.

This paper investigates the state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and delay-dependent boundary ranges of the solution, which have less conservatism than the results in existing literatures. The validity of the results is verified by two numerical examples., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Frumen Olivas et al.)

Identifying drug-target interactions (DTIs) is an important part of drug discovery and development. However, identifying DTIs is a complex process that is time consuming, costly, long, and often inefficient, with a low success rate, especially with wet-experimental methods. Computational methods based on drug repositioning and network pharmacology can effectively overcome these defects. In this paper, we develop a new fusion method, called FPSC-DTI, that fuses feature projection fuzzy classification (FP) and super cluster classification (SC) to predict DTI. As the experimental result, the mean percentile ranking (MPR) that was yielded by FPSC-DTI achieved 0.043, 0.084, 0.072, and 0.146 on enzyme, ion channel (IC), G-protein-coupled receptor (GPCR), and nuclear receptor (NR) datasets, respectively. And the AUC values exceeded 0.969 over all four datasets. Compared with other methods, FPSC-DTI obtained better predictive performance and became more robust.

A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains., Competing Interests: The authors declare that they have no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Gen Liang et al.)

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

Background: Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions., Method: With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation., Results: On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks., Conclusions: The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation., Competing Interests: Declaration of Competing Interest No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.compbiomed.2020.103751., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this paper, a new approach is proposed for localization and segmentation of the optic disc in human retina images. This new approach can find the boundary of the optic disc by an initial fuzzy clustering means algorithm. The proposed approach uses active contour model evolution based on a fuzzy clustering algorithm. The robustness of the proposed approach was evaluated with retinal imaging medical databases, such as DRIVE, STARE, DIARETDB1, and DRIONS. These bases contained images affected by different abnormalities, for example, diabetes, retinitis pigmentosa, and age-related macular degeneration AMD. A success detection rate with 100% accuracy was achieved for the DRIVE, DIRATEDB1, and DRIONS-DB databases, and 97.53% for the STARE database. For the optic disc segmentation, the method achieved an average accuracy and overlap in the range of 97.01-99.46% and 78.35-84.56% in these four databases. The result was compared with various methods in the literature, and it was concluded that the proposed method is more accurate than the other existing methods. Graphical abstract.

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Two critical tasks in multi-criteria group decision making (MCGDM) are to describe criterion values and to aggregate the described information to generate a ranking of alternatives. A flexible and superior tool for the first task is q-rung orthopair fuzzy number (qROFN) and an effective tool for the second task is aggregation operator. So far, nearly thirty different aggregation operators of qROFNs have been presented. Each operator has its distinctive characteristics and can work well for specific purpose. However, there is not yet an operator which can provide desirable generality and flexibility in aggregating criterion values, dealing with the heterogeneous interrelationships among criteria, and reducing the influence of extreme criterion values. To provide such an aggregation operator, Muirhead mean operator, power average operator, partitioned average operator, and Archimedean T-norm and T-conorm operations are concurrently introduced into q-rung orthopair fuzzy sets, and an Archimedean power partitioned Muirhead mean operator of qROFNs and its weighted form are presented and a MCGDM method based on the weighted operator is proposed in this paper. The generalised expressions of the two operators are firstly defined. Their properties are explored and proved and their specific expressions are constructed. On the basis of the specific expressions, a method for solving the MCGDM problems based on qROFNs is then designed. Finally, the feasibility and effectiveness of the method is demonstrated via a numerical example, a set of experiments, and qualitative and quantitative comparisons., Competing Interests: The authors have declared that no competing interests exist.

Nonlinear fuzzy classification models have better classification performance than linear fuzzy classifiers. In many nonlinear fuzzy classification problems, piecewise-linear fuzzy discriminant functions can approximate nonlinear fuzzy discriminant functions. In this paper, we first build fuzzy classifier based on data envelopment analysis (DEA) for incremental separable fuzzy training data, which can be widely applied in the healthcare management with fuzzy attributes, then we apply the proposed fuzzy DEA-based classifier in the diagnosis of Coronary with fuzzy symptoms and the classification of breast cancer dataset with fuzzy disturbance. Numerical experiments show the proposed fuzzy DEA-based classifier is accurate and robust.

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images., (Copyright © 2018 Elsevier B.V. All rights reserved.)