Hybrid Fuzzy Research Articles

Pneumonia Detection through Deep Learning: A Comparative Exploration of Classification and Segmentation Strategies

The Convolution Neural Network (CNN) algorithm is one of the most widely used methods for identifying and categorizing lung cancer. This paper covers the most suitable architecture and CNN algorithms for lung cancer and pneumonia deduction and classification. The main contributions to the diagnosis and classification of lung cancer with four steps are Nonlinear transfer learning framework (NLTF), Hierarchical Feature Mapping (HFM), Lifelong Partial Dissection (LPD), and Deep Lifelong Convolutional Neural Network (DLCNN). The application of non-local total fuzzy (NLTF) filtering removes various categories of noise after lung CT imageries and enhances cancer areas. The application of Hybrid Fuzzy Morphology (HFM) constructed segmentation to minimize the region of interest (ROI) for cancer using morphology opening and closing processes. Extraction of traits unique to each disease employing Lung Parenchyma Division (LPD) and extraction of deep seismic features using the Geometric Optimal Algorithm (GOA). Training and testing the proposed Deep Learning Convolutional Neural Network (DLCNN) model using the extracted features to classify benign, malignant lung cancers and Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia.

Assessment of the severity of associated ischemia in conditions of atherosclerosis of the carotid arteries and arteries of the lower extremities

The purpose of the research is to develop a method for assessing the severity of ischemic processes in multifocal atherosclerosis against the background of chronic cerebrovascular insufficiency and chronic ischemia of the lower extremities.Methods. The main methods used in this study are the methodology for the synthesis of hybrid fuzzy decision rules, exploratory analysis, and the Delphi expert assessment method; calculations were based on the G. Rush model and the E. Shortliff iterative model.Results. In the course of the research, particular decisive rules were synthesized to assess violations of the regulatory functions of the brain, ischemic disorders of the brain and lower extremities, disorders of the motor functions of the lower extremities, on the basis of which, using the iterative model of E. Shortliff and aggregating decisive rules, intermediate decisive rules were formed, which, in turn, entered the final decisive the rule "severity of ischemic interaction". Further, based on the final decisive rule, an algorithm was developed to assess the severity of ischemic interactions for comorbid patients with disintegration of affective-effector mechanisms of interaction between peripheral organs and regulatory functions of the central nervous system, allowing to assess the severity of the ischemic process and the risk of fatal complications in patients with the pathology in question.Conclusion. In the course of the conducted research, high results were demonstrated in the application of synthesized partial decision rules within the framework of the task set in this study, and the expediency of using the obtained models and methods in the practice of a neurologist, vascular surgeon, angioedrologist, neurosurgeon was also shown.

The method and fuzzy models for evaluating the functional states of RAM

The purpose of the research is to improve the quality of assessment of functional states of working memory by developing a method for synthesizing fuzzy decision rules.Methods. To monitor the state of various RAM blocks, a set of techniques was selected: reaction time assessment; searching for a signal in noise; "identification"; full reproduction; identification of missing digits; Memory. To synthesize decision rules, additional methods were used to assess such properties of attention as switchability, concentration and stability, for the implementation of which a device for monitoring the properties of the attention and memory function was used. To select an adequate mathematical research apparatus, an exploratory analysis of the structure of the processed data was carried out, according to which it was established that the selected classes of RAM states are of a fuzzy nature with uncertain boundaries of their intersections. Taking this into account, the methodology for the synthesis of hybrid fuzzy decision rules was chosen as a mathematical research tool, which was modified by developing a new method for fuzzy assessment of the functional state of RAM based on the characteristics of its properties.Results. In the course of the research, models were synthesized to assess such characteristics of the functional state as levels of fatigue, psycho-emotional stress and the functional state of RAM using the full reproduction method. The resulting models can be used to synthesize decision rules for forecasting, early and differential diagnostics of the functional state, assessing the quality of work of the operator of human-machine systems and the health status of RAM.Conclusion. The paper proposes a method for synthesizing fuzzy decision rules for assessing the functional state of RAM based on the characteristics of its properties using techniques obtained from the results of microstructural analysis. Fuzzy decision rules were obtained for assessing the level of fatigue, psycho-emotional stress and the functional state of RAM using the full reproduction method. During expert assessment and mathematical modeling, it was shown that confidence in the correct assessment of the level of functional state exceeds 0,95.

An Enhanced Fuzzy Hybrid of Fireworks and Grey Wolf Metaheuristic Algorithms

This research work envisages addressing fuzzy adjustment of parameters into a hybrid optimization algorithm for solving mathematical benchmark function problems. The problem of benchmark mathematical functions consists of finding the minimal values. In this study, we considered function optimization. We are presenting an enhanced Fuzzy Hybrid Algorithm, which is called Enhanced Fuzzy Hybrid Fireworks and Grey Wolf Metaheuristic Algorithm, and denoted as EF-FWA-GWO. The fuzzy adjustment of parameters is achieved using Fuzzy Inference Systems. For this work, we implemented two variants of the Fuzzy Systems. The first variant utilizes Triangular membership functions, and the second variant employs Gaussian membership functions. Both variants are of a Mamdani Fuzzy Inference Type. The proposed method was applied to 22 mathematical benchmark functions, divided into two parts: the first part consists of 13 functions that can be classified as unimodal and multimodal, and the second part consists of the 9 fixed-dimension multimodal benchmark functions. The proposed method presents better performance with 60 and 90 dimensions, averaging 51% and 58% improvement in the benchmark functions, respectively. And then, a statistical comparison between the conventional hybrid algorithm and the Fuzzy Enhanced Hybrid Algorithm is presented to complement the conclusions of this research. Finally, we also applied the Fuzzy Hybrid Algorithm in a control problem to test its performance in designing a Fuzzy controller for a mobile robot.

Position Control for Automatic Assembly Equipment Using a New Hybrid Fuzzy Controller

Position Control for Automatic Assembly Equipment Using a New Hybrid Fuzzy Controller

Risk assessment of emergency operations of floating storage and regasification unit

As the receiving terminal of liquefied natural gas (LNG), the efficient emergency response of the floating storage and regasification unit (FSRU) is crucial to ensure the safety of LNG transportation at sea. However, few existing literature study the risk issues of FSRUs during emergency operations. In order to improve the emergency response capability of FSRU, this study proposes an innovative assessment method to identify hazards, quantify and rank the risks associated with emergency response and disposal operations of FSRU accidents. Firstly, a comprehensive index hierarchy system applicable to human, equipment, environment, and management aspects of emergency response and disposal operations of FSRU accident is established through an extensive literature review, analysis of accident reports, and expert judgments. Secondly, based on the concept of Intuitionistic Fuzzy Numbers, the Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) operator is used to enhance the conventional FMEA approach. This method considers the varying levels of expert confidence and integrates subjective and objective weights of risk influential factors (RIFs), and the efficacy is validated through sensitivity analysis. Finally, a comprehensive evaluation model employing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation algorithms is used to aggregate the risk values of RIFs. The findings of this study offer decision-makers insights into risks during emergency operation, provide valuable guiding strategies for FSRU accident management, and improve the capability for emergencies at sea.

Hybrid fuzzy optimistic and pessimistic data envelopment analysis models for evaluating decision-making units in the presence of vague data

Hybrid fuzzy optimistic and pessimistic data envelopment analysis models for evaluating decision-making units in the presence of vague data

A Hybrid Fuzzy Extension and Its Application in Multi-Attribute Decision Making

The hypersoft set theory is an extension of soft set theory. The complex non-linear diophantine fuzzy set is a hybrid fuzzy extension that serves as a generalization of the q-rung linear diophantine fuzzy set and the complex linear diophantine fuzzy set. In this paper, to tackle multi-sub-attributed real-world situations under complex non-linear diophantine fuzzy ambiance, the concept of complex q-rung linear diophantine fuzzy hypersoft set is proposed along with its score and accuracy function. Also, the idea of lattice ordered complex q-rung linear diophantine fuzzy hypersoft set is proposed in this paper, along with some of its basic algebraic operations. Furthermore, a highly effective algorithm using lattice ordered complex q-rung linear diophantine fuzzy hypersoft set is provided for handling multi-attributed decision-making issues exquisitely, along with an illustrative example in the field of vertical farming. Then, a comparative analysis between the proposed and current notions is provided to demonstrate the superiority and benefits of the suggested concepts over the current ones.

Method of thromboembolism prediction in advanced atherosclerosis

Aim. To develop a method for thromboembolism prediction in patients with ad­vanced atherosclerosis.Material and methods. The study involved 170 patients with advanced atheroscle­rosis and 110 patients with local atherosclerotic lesions. At the first stage, speciali­zed experts generated signs characterizing the severity of prothrombotic conditions. To determine whether patients belonged to the first or second group, the multivariate statistics method was used. Decision rules (DRs) was developed based on soft computing (SC), as well as the methodology for the synthesis of hybrid fuzzy DRs.Results. According to the discriminant function coefficient (DFC), the division into classes was made with the formation of an intersection area, which affected the diagnostic sensitivity of this method (=86%). Due to difficulties of separating more heterogeneous groups with a small sample, RP was synthesized according to the atherosclerosis severity classification using SC technology. Atherosclerosis severity classification was adopted according to fuzzy DRs: initiation stage (up to 20%); reverse stage (21-40%); progressive stage (41-65%); critical stage (more than 65%). As­ses­sment of thromboembolism risk in advanced atherosclerosis in the examined pati­ents showed the following trends: progressive stage — 55% of patients; critical stage — 21% of patients; reverse stage — 16% of patients; initiation stage — 7% of patients.Conclusion. DFC for DRs makes it possible to identify groups with high and low thromboembolism risk. The resulting final fuzzy DRs make it possible to differentiate the atherothrombotic condition in advanced atherosclerosis by severity, which can help to timely determine preventive and therapeutic measures.

Research on Critical Factors Influencing Organizational Resilience of Major Transportation Infrastructure Projects: A Hybrid Fuzzy DEMATEL-ISM-MICMAC Approach

To strengthen major transportation infrastructure projects’ (MTIPs’) organizational resilience and fortify their capacity for crisis management and project risk prevention. In this paper, based on the resilience theory development process, the connotation of organizational resilience of MTIPs is defined, and 20 influencing factors of organizational resilience of MTIPs are extracted from four categories of stability, redundancy, adaptability, and rapidity according to the literature analysis and case study method. The significance, causality, and multilevel recursive order structure of the influencing factors were investigated by the fuzzy DEMATEL-ISM approach, and their driving and dependent characteristics were analyzed through MICMAC. The results indicate that risk warning and prediction, human resources management, inter-organizational synergies, resource reserve situations, organizational leadership, and organizational learning are the crucial factors of organizational resilience in MTIPs. There are three levels and five ranks in the multilevel recursive rank structure of the factors affecting MTIPs’ organizational resilience. Among them, risk warning and prediction, equipment condition and performance, human resources management, and organizational leadership have the deepest impact on organizational resilience in MTIPs. The findings can clarify ideas for subsequent research on organizational resilience in this area and inform project decision-makers in developing strategies for optimizing organizational resilience.

A New Approach for Wireless Sensor Networks based on Tree-based Routing using Hybrid Fuzzy C-Means with Genetic Algorithm

A New Approach for Wireless Sensor Networks based on Tree-based Routing using Hybrid Fuzzy C-Means with Genetic Algorithm

Supplier selection in green supply chain management using correlation-based TOPSIS in a q-rung orthopair fuzzy soft environment

Fuzzy hybrid models are efficient mathematical tools for managing unclear and vague data in real-world scenarios. This research explores the q-rung orthopair fuzzy soft set (q-ROFSS), which presents incomplete and ambiguous details in decision-making problems. The main intention of this study is to describe and evaluate the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS. Also, the technique for order preference should be enhanced by similarity to the ideal solution (TOPSIS) with extended measures in q-ROFSS settings. Furthermore, we integrated mathematical formulations of correlation obstructions to confirm the consistency of the planned technique. It helps handle difficulties involving multi-attribute group decision-making (MAGDM). Moreover, a numerical illustration is presented to clarify how the advocated decision-making methodology can be implemented in evaluating suppliers in green supply chain management (GSCM). As a result, each alternative is assessed using multiple criteria, such as quality and reliability, capacity and scalability, compliance and certifications, and sustainability practices. The technique proposed in this study retains the selected research's specific structure more effectively than current techniques. A comparative analysis further substantiates the feasibility and effectiveness of the proposed approach over other decision-making techniques.

Green supply chain finance model in logistics and transportation industry: improving from policies and regulation and financial digitalisation practices

ABSTRACT This study contributes to the green supply chain finance model to facilitate the alignment between financial prosperity and the green transition in the logistics and transportation industry in Vietnam. Green supply chain finance has received increasing attention of stricter environmental regulations and increases consumer demand in industries involved. This complex issue requires ongoing attention and resolution in the industry. This study aims to construct a green supply chain finance model and identify the attributes. This study employs a hybrid fuzzy Delphi and fuzzy decision-making trial and evaluation laboratory. The model is shown to be robust according to sensitivity analysis. The results show that the validated model is built by 13 criteria within five aspects. The aspects are policies, regulations, financial digitalisation approaches, and collaborative financing decision-making. The practical causal criteria include (1) shared profit maximisation, (2) government rewards, (3) sustainability agreements and to provide guidelines for the practices.

Green Supplier Selection Using Fuzzy AHP, Fuzzy TOSIS, and Fuzzy WASPAS: A Case Study of the Moroccan Automotive Industry

The green supplier selection presents numerous challenges, from initial assessment to final selection, which specialists in this field of supplier management often encounter. Among the techniques that aim to meet these challenges that are continually progressing is the creation and development of high-quality decision-making tools. In this study, the assessment of suppliers hinges on both traditional and environmental factors. A framework of multi-criteria decision-making (MCDM) is announced in order to appraise green supplier selection. This framework integrates Fuzzy Analytical Hierarchy Process (AHP) combined with two additional methods: WASPAS (“Weighted Aggregated Sum-Product Assessment”) and TOPSIS (“Technique for Order Preference by Similarity to Ideal Solution”). At the outset, there were five environmental criteria for green supplier selection: “Health and Safety”, “Sustainable Product Design”, “ISO 14001 Certification”, “Investment Recovery”, and “Green Packaging”, along with three conventional criteria including quality, price, and delivery, were pinpointed through a review of literature and expert input to facilitate the MCDM approach. As part of demonstrating the pertinency of the recommended framework, a practical case study of the automotive industry in Morocco is discussed. The results demonstrate that the utilized Fuzzy hybrid methods yield consistent rankings for green suppliers. Additionally, among the four green suppliers, number three obtained the best ranking, which indicates the robustness and performance of the chosen models. Furthermore, this study offers a unified platform for selecting green suppliers under a Fuzzy environment. Therefore, the chosen strategy and its analysis provide relevant data and information to decision-makers for the choice and selection of suppliers. It can also provide and help procurement departments and decision-makers to choose and select the efficient ecological supplier in the global market supply chain.

Enhanced Hybrid Robust Fuzzy-PID Controller for Precise Trajectory Tracking Electro-Hydraulic Actuator System

The Electro-Hydraulic Actuator (EHA) system integrates electrical and hydraulic elements, enabling it to generate a rapid reaction, a high power-to-weight ratio, and significant stiffness. Nevertheless, EHA systems demonstrate non-linear characteristics and modeling uncertainties, such as friction and parametric uncertainty. Designing a controller for accurate trajectory tracking is greatly challenging due to these limitations. This paper introduces a hybrid robust fuzzy proportional-integral-derivative (HFPID) and (HF+PID) controller. The controller is designed to effectively control a third-order model of an EHA system for trajectory tracking. It is a significant contribution to the development of an intelligent robust controller that can perform well in different environments. Initially, a mathematical model for the EHA system was created using a first-principle approach. Subsequently, the Ziegler-Nichols method was employed to fine-tune the PID controller, while a conventional Fuzzy Logic Controller (FLC) was constructed in MATLAB Simulink utilizing linguistic variables and rule-based control. Without further tuning, the FL and PID controller are combined as a hybrid controller with different structures: Hybrid Fuzzy-PID (HFPID) and Hybrid Fuzzy+PID (HF+PID) controller. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) are utilized as indices to assess the tracking accuracy and robustness of the four controllers. A greater value of MSE and RMSE indicates poorer performance of the controller. The results demonstrate that the HF+PID controller surpasses the other controllers by reaching the lowest MSE and RMSE values. It showcases the efficacy and accuracy in monitoring sinusoidal, multi-sinusoidal, and point-to-point trajectory tracking. Future work should focus on implementing the designed controller on hardware for real-time performance and experimenting with various types of FLC or Hybrid controllers, such as self-tuning fuzzy-PID, to further explore their potential.

A Novel Hybrid Fuzzy Multiple-Criteria Decision-Making Model for the Selection of the Most Suitable Land Reclamation Variant at Open-Pit Coal Mines

The expansion of the open-pit exploitation of mineral raw materials, and especially the energy resources of fossil fuels, makes open-pit coal mines spatially dominant objects of large mining basins. Exploitation activities are accompanied by negative ecological impacts on the environment, which requires the integral planning, revitalization, reclamation, and rehabilitation of the disturbed area for human use in the post-exploitation period. The post-exploitation remediation and rehabilitation of open-pit mining areas and disposal sites, i.e., space disturbed by mining activities and accompanying facilities, are complex synthetic multidisciplinary multiphase engineering project tasks. In this paper, a hybrid fuzzy MCDM model (Multiple-Criteria Decision-Making) was developed for the selection of a reclamation solution for the Tamnava-West Field open-pit mine. IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define the weights of 12 criteria of different structures used in the evaluation of reclamation solutions. The Fuzzy ROV (Range of Value) method was applied to select the reclamation solution from a total of 11 solutions previously obtained using a process approach. The results of the hybrid IMF SWARA—Fuzzy ROV model show that forestry is the best solution for the Tamnava-West Field open-pit mine. After the results had been obtained, verification analyses of the proposed model were performed and the best stable proposed reclamation solution was determined.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images