Fuzzy-based System Research Articles

A Neuro-Fuzzy-based Approach to Detect Liver Diseases

The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.

Fuzzy rule based classifier model for evidence based clinical decision support systems

Clinicians benefit from the use of artificial intelligence and machine learning techniques applied to health data within health records, which identify commonalities between them. It enables them to get evidence-based support in recommending shared treatment paths for undiagnosed health records. The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential features, supporting public health experts in their management of population health conditions. This paper presents a novel mapping tool model designed to analyze electronic health records and provide healthcare providers with evidence-based decision support. The work focuses on the analysis of health records from hospital databases, encompassing parameters extracted from routine health checkups. By scrutinizing patterns within examined health records, healthcare providers can seamlessly align with newer health records for diagnosis and treatment recommendations. Core to this approach is the integration of a fuzzy rule-based classifier system within the proposed system. This incorporation facilitates the processing of health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a specially developed distance-measure technique tailored for the proposed fuzzy-based system. Results showcase satisfying performance and robust discriminant capability for accurate recommendations. The alignment of outcomes with expert evaluations underscores the model's efficacy and attainment of benchmarks.

A comparison study of two implemented fuzzy-based models for decision of logical trust

In recent years, the human-to-human and human-to-things relationships are becoming complicated and unreliable, which makes harder decisions in a variety of situations. As a result, trust computing is gaining interest in a number of research fields. The Logical Trust (LT) is one of trust computing concepts. In this paper, we design a Fuzzy-based System for Decision of Logical Trust (FSDLT). We implement two models: FSDLTM1 and FSDLTM2. The FSDLTM1 considers three input parameters: Belief (Be), Experience (Ep), Rationality (Ra) and the output parameter is LT. In FSDLTM2, we consider Reliability (Re) as a new parameter. We evaluated the implemented models by computer simulations. The simulation results show that when Be, Ep, Ra and Re are increasing, the LT is increased. For FSDLTM1, when Ep value is 0.9, all LT values are greater than 0.5. While for FSDLTM2, in case when Be is 0.9, for all values of Ra and Re, when Ep is 0.5 and 0.9, all LT values are higher than 0.5. This shows that the person or device is trustworthy. The FSDLTM2 is more complex than FSALTM1 but it makes a better decision for LT by considering four input parameters.

Fuzzy-Based Control System of Drilling Fluids Density and Apparent Viscosity Simultaneously: An Alternative Strategy to Support Autonomous Drilling Operations

Summary Among all the systems that make up a drilling operation, the production and correction of drilling fluid can be considered the heart of the process. Among the main objectives of the drilling fluid are to cool the drill bit and maintain the pressure gradient inside the drilling well, which is done by controlling its density. Another important function is transporting the cuttings from the bottom to the surface and keeping them in suspension in case of stoppage, which directly depends on the viscosity of the drilling fluid. Density and viscosity must be constantly maintained within an operational window, and failures can lead to serious accidents, even the loss of the well. Currently, this control is done manually: An operator collects samples of the fluid and takes them for analysis in the laboratory and subsequently makes the necessary corrections by manually adding products to the fluid. To reduce process dead time, keep personnel on board, and increase operation safety, a control and monitoring system is necessary. Fuzzy logic was chosen because it can be combined with classical methods, is cheap to develop and implement, and can be customized in terms of natural language, capturing the knowledge acquired by operators from equipment operation, bench tests, etc. This work aimed to develop a novel real-time monitoring and fuzzy-based system for simultaneous control of the apparent viscosity and density of non-Newtonian fluids, dealing with the inevitable interactions between them in a pilot experimental unit. A pilot plant was built to evaluate the fuzzy system approach for modeling and controlling of density and apparent viscosity of drilling fluids. The pilot flow loop comprises a mixing tank, solids vibrating feeders, and a water-dosing pump. The unit was instrumented with online sensors to measure fluid density, temperature, flow rate, differential pressure, and viscosity. The apparent viscosity and density of the non-Newtonian fluid were controlled by manipulating the dosage of carboxymethylcellulose (CMC), barite, and water. The proposed methodology was compared to a classical proportional-integral-derivative (PID) controller in servo and regulatory scenarios for apparent viscosity and density. The results showed that the fuzzy controller dealt adequately with the effect of variable interactions, keeping both variables within their setpoint ranges, demonstrating the ability to control them individually despite their interactions. These results also showed that the fuzzy-based controller could easily be integrated into a diagnostic-predictive monitoring system to control fluid properties, accomplishing setpoint changes and rejecting undesirable disturbances presenting a maximum overshoot of 7.5% for apparent viscosity and 0.3% for density.

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing.

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Functional Electrical Stimulation (FES) cycling, or FES-Cycling, holds great therapeutic potential for individuals with paralysis, such as those with Spinal Cord Injury (SCI), traumatic brain injury, or stroke, aiming to restore mobility. However, the nonlinear nature of the musculoskeletal system poses a significant challenge in controlling FES-Cycling. To address this, an integrated closed-loop phase angle fuzzy-based system was developed. This system offers real-time control by adjusting stimulation intensity (pulse width) within the range of 50 to 200μs while maintaining a constant frequency of 35Hz, thereby ensuring precise pedaling trajectory and cadence patterns. An experimental study involved three healthy individuals (Cases A, B, and C) and one individual with hemiplegia stroke (Case D). Results showed that the proposed system consistently reduced average angle trajectory errors for Cases A, B, and C, with values of 2.6945, 3.2958, and 2.9922 degrees, respectively. Case D, affected by hemiplegia stroke, faced greater challenges and exhibited a higher error of 3.4562 degrees. Fatigue resistance, evaluated through fatigue indices, showed promising results for Cases A, B, and C with values of 0.10778, 0.06866, and 0.04603, respectively. However, Case D experienced higher fatigue (0.2304) due to the unique challenges of hemiplegia stroke. These findings highlight the effectiveness of the proposed control system in optimizing FES-Cycling, particularly for healthy individuals. For individuals with paralysis, like Case D, further research is needed to adapt the system to their specific conditions and cycling patterns. This system holds the potential for enhancing FES-Cycling as a therapeutic strategy and warrants additional investigation and customization for different patient populations.

Empirical Validation of Fuzzy Logic Based Predictive Load Scheduling in Mimic Home Energy Management System

<p>Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and consumers tolerance. Though many research works concentrate on load scheduling, very few works concentrated on real time scenario. On the other hand, research work concentrated on optimal load scheduling through fuzzy logic requires incorporation of fuzzy based system in either simulated or real-time home energy management system. Hence, the proposal aims to schedule the loads in a simulated home energy management system through fuzzy logic controller using MATLAB Simscape with required subsystems. The proposed simulated environment considers four different resistive loads. Intelligent scheduling is aimed to achieve efficient load scheduling. A fuzzy controller has three inputs namely integrated source, state of charge of battery, and power demand whereas probability of scheduling is considered as output. The efficiency of the proposed fuzzy-based load scheduling scheme is evaluated under various load conditions for different sub-systems with varying input power. The results exhibit the efficacy of the proposed scheme.</p> <p> </p>

Observer-based fuzzy integral sliding mode control for bilateral teleoperation systems with time-varying delays

This paper aims to examine the stability and tracking performance of a bilateral teleoperation system. The Takagi–Sugeno fuzzy method is utilized, through which the nonlinear master–slave dynamics is converted as a fuzzy-based system. The state observers are designed for the linearized fuzzy teleoperation systems, and the corresponding estimation errors are formulated. Importantly, a novel observer-based fuzzy integral sliding mode control is developed by deliberately introducing the delay term into the sliding surfaces. As such, advanced delay-product type of Lyapunov–Krasovskii functionals are constructed for the augmented state vectors, in order to acquire the additional delay information. In addition, the Wirtinger-based integral inequality along with an extended reciprocally convex matrix inequality is applied to the Lyapunov derivatives to establish the delay-dependent stability conditions. Numerical results are provided to demonstrate efficacy of the developed control mechanism.

Use of augmented output membership functions to improve control accuracy of second-order fuzzy system

The efficacious application of fuzzy-based systems largely depends on some parameters of subjective decisions, e.g., fuzzy membership functions (MFs). The second-order fuzzy-based system with decoupling technique, which is generally a multi-inputs single-output (MISO) system executing augmented output MFs, is introduced to enhance the fuzzy system’s precision, heftiness, and recital. The best suitable protocol for input and output MFs is presented to design the input and output MFs in the discourse. The results of the uniformed and augmented designed output MFs are compared in identical circumstances. The experimental results and simulation results are in virtuous agreement. By applying the proposed method to different fuzzy systems, the mean absolute error (MAE) was reduced 82%, and the relative error was decreased to an adequate (≤±10%) range. The control precision is enhanced, and the heftiness is improved by reducing the MAE through the proposed second-order fuzzy model. Furthermore, the energy and cost efficacy of the proposed second-order fuzzy model will be highly enhanced by applying the augmented output MFs.

An intelligent fuzzy-based system for handover decision in 5G-IoT networks considering network slicing and SDN technologies

By enabling 5G technology, IoT networks can improve the performance of connected IoT devices. However, handover, ping-pong effect and load balancing are critical issues because of massive and unplaned deployment of small cells. The handover operation in 5G wireless networks is complicated because there are many radio access techniques and technologies. Therefore, the mobility management is required to provide QoS for various applications. In this paper, we present a fuzzy-based system for handover decision in 5G wireless networks considering different network slicing parameters. We implement two Fuzzy-based Handover Decision Models (FHDM): FHDM1 and FHDM2. We evaluate the proposed models by simulations. For FHDM1, Slice Delay (SD), Slice Bandwidth (SB), and Slice Load (SL) are three input parameters and Handover Decision (HD) is the output parameter. For FHDM2, Slice Reliability (SR) is a new considered parameter, so it has four input parameters and the output parameter is HD the same as FHDM1. We found from simulation results that the considered parameters have different effects on HD. For both models, the HD value increases as SD and SL values increase while HD value is decreasing when SB and SR values increase. The results of the simulation indicate that although FHDM2 is more complex than FHDM1, it performs better HD since considers four input parameters. From the simulation results of FHDM2, when we changed SD value form 10% to 90%, we found that the HD value is increased by 50% when SR is 50% and SL is 90%. This indicates that when a user is connecting with the present slice that has bad QoS, it should handover to another slice with better QoS.

Fostering restoration and power delivery efficiency by distributed generators and line switches in electricity distribution network

ABSTRACT The distribution of power is being faced with challenges during and after extreme weather events. This contingent might cause a partial or full power blackout in the distribution territory. Hence, the restoration of the service is one of the key concerns of the utilities. A strategy is adopted to felicitate the power restoration process by the deployment of distributed generators (DGs) and line switches. A fuzzy-based system model is proposed to allocate line switches considering the impact of wind-driven extreme weather and the fault frequency character of the lines. The study further recommends an arrangement of dispatchable DGs in the sections of the distribution grid. A particle swarm optimization-based model is proposed for suitable capacity and location assessment of the DGs to foster the restoration of supply avoiding voltage and frequency disturbances in the developed sections of the grid. The results are further verified with the application of the grasshopper optimization algorithm on the multipurpose composite objective function. The proposed DG allocation approach escalates the network power loss reduction by 95.10%, improvement in average voltage stability level by 6.46%, and loadability margin enhancement by 37.37% when the DGs are operated in grid-connected mode. Moreover, power supply reliability is restored with successive employment of DG units. The proposed approach has been tested on an Indian 28-bus power distribution network. The outcome of the proposed approach is compared with existing DG allocation methods. The experiment confirms the credential of the proposed approach with better value addition in multiple objectives.

Stock Selection System Through Suitability Index and Fuzzy-Based Quantitative Characteristics

With the rapid development of quantitative trading, stock selection is an ongoing task that requires consideration of the characteristics of stocks and investment strategies. Fuzzy set theory is excellent for modeling and describing abstract characteristics to establish the link between stocks and strategies. In this article, we present our idea for a stock selection system called TripleS. It is based on the suitability index (SI) derived from fuzzy-set theory. The TripleS relies on the position size to extract the stock characteristics SI that describes not only the characteristics of each stock but also the extent to which they are suitable for certain strategies. Then, the SI undergoes a transformation process in the hands of the developed fuzzy-set modules for stock selection and investment. There are some other proposed methods for stock selection respectively called TripleS-T1, TripleS-T2, TripleS-T2U, TripleS-EF, and TripleS-EFT. Experimental results show that the proposed TripleS has minor improvement in precision but significant improvement in investment performance. The TripleS-EFT has outstanding profitability, the highest annual return in all strategies, and the highest Sharpe ratio in two of the four strategies, and achieves an annual return of 10.01% and a Sharpe ratio of 1.032, which significantly outperform the benchmark system. The TripleS-EFT also significantly outperforms the state-of-the-art fuzzy-based system in measuring annual return and even has double the annual return in several strategies.

An Accurate and Inter-Operatable Fuzzy-Based System Using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things.

The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics.

Uncertainties-Based Potential Time and Cost Overrun Assessment While Planning a Hydropower Project

Uncertainties are predominant in hydropower development that lead to risks of time and cost overruns. The overruns’ assessment requires quantitative data that is difficult to obtain and sometimes partially or completely unavailable. Accordingly, this study proposes an expert fuzzy-based system for assessing the impact of uncertainties on time and cost overrun. A Bayesian network analysis is used wherein the experts’ opinions are incorporated through an analytic hierarchy process for the uncertainty’s likelihood and impact. A fuzzy inference system is used to incorporate the decision maker’s optimistic and pessimistic approaches. The proposed methodology is illustrated through a 3×42 MW run-of-river Myntdu Leshka Hydroelectric project in India. The project’s initial likely completion time was five years with a cost of ₹3,630 million at the 1999 price level. The results state that the project’s time and cost overruns lie between 158% and 196% and between 235% and 276%, respectively, under optimistic and pessimistic conditions. Such a methodology has not been applied by past studies for hydropower projects, which is the study’s main contribution. The methodology can be easily applied by construction project developers globally in different fields by varying the uncertainties, their likelihood, and impact scales as per different risk perceptions and regional conditions.

Implementation and evaluation of a fuzzy-based system for determining stress feeling level in VANETs: Effect of driving experience and history on driver stress

Drivers are held responsible for the vast majority of traffic accidents. While most of the errors that cause these accidents are involuntary, a significant number of them are caused by irresponsible driving behaviors, which must be utterly preventable. Irresponsible driving, in addition, is often associated with the stress drivers experience while driving. We have previously implemented an intelligent system based on fuzzy logic for determining driver’s stress in Vehicular Ad hoc Networks (VANETs), called Fuzzy-based System for Determining the Stress Feeling Level (FSDSFL), considering the driver’s impatience, the behavior of other drivers, and the traffic condition as input parameters. In this work, we present an Improved FSDSFL (IFSDSFL) system, which considers the driving experience and history as an additional input. We show through simulations the effect that driving experience and history and the other parameters have on the determination of the stress feeling level and demonstrate some actions that can be performed when the stress exceeds certain levels.

Modeling and Simulation of autonomous mobile robot system for collision avoidance in VR environment using Simscape-Multibody

Autonomous mobile robots have recently become a centre of attention in the robotics community due to their ease of design, modeling and implementation, making them very lucrative in logistics, monitoring, mapping and other purposes. Fundamentally, collision avoidance is a major aspect of an autonomous mobile robot which requires assistance from other aspects like sensor fusion, localization, path planning etc., to contribute to achieving proper functionality in an open environment, hence the inherently required integration makes it hard to many researchers, robotics and robot enthusiasts to incorporate multi-dimensional functionalities in their system. Sometimes traditional software for robotics makes it harder to acquire actual insight into their system due to a lack of cross-platform compatibility. Therefore, this research aims to demonstrate a way to integrate important aspects required for obstacle avoidance system for design and analysis using a compatible system which is SolidWorks and MatLab simscape multibody, which provides sufficient scope to properly and accurately simulate autonomous systems on a real-time basis that can be used with actual hardware to final implementation. The findings of the study demonstrate that it is very much feasible to use such a method for robotic and other complex mechanics applications, and further analysis of the sensor fusion algorithm reveals some shortcomings of the sensor fusing algorithm and data processing which can be avoided by introducing further sensor sequencing and implementing adaptive neuro fuzzy-based system.

Mask Covered Face Recognition Using Haar Cascade Classifier and Fuzzy Logic

Right from the beginning of the COVID-19 outbreak, everyone is aware of the havoc caused by the pandemic. To curb its spread, every healthcare agency and civic body around the globe has been advising to wear masks. However, this necessary practice has posed a significant challenge for the modern-day Facial Recognition technology. Face recognition finds significant application in the security domain that demands speed and accuracy both simultaneously. This requires the system to be highly optimized and efficient. Through this paper, we present a novel approach using Haar cascade classifier for face detection with Local Binary Patterns Histograms (LBPH) face recognizer. This work further goes on to address the various problems that occur when the user wears a mask that covers a different area and percentage coverage of the face resulting in inaccuracies as various tests come with false negatives or false positives. This problem is addressed by making use of a fuzzybased system that decides the “threshold confidence score” needed to pass the authentication dynamically. Our proposed model for masked Face recognition achieves an accuracy of 86% when a Haar-feature-based cascade classifier with LBPH face recognizer is used standalone which further improves to around 97% when used in conjunction with a fuzzy system

A fuzzy model for predicting burn patients’ intravenous fluid resuscitation rate

One of the critical areas in the medical field is the burn treatment management that helps to rescue patient’s life by dramatically minimizing the after-burn progression and severity. Initial acute burn treatment involves intravenous fluid resuscitation, which must be administered meticulously to restore fluid loss and maintain essential organ function. A number of factors must be addressed quickly to make a decision for determining the required rate of intravenous fluid. Traditional methods with manual calculation reflects slow response and inaccurate estimation in most of the cases, thus requires random adjustments. Some automated and artificially intelligent systems can be found which still use the traditional formulas showing high fluctuation in the calculation of required rate. Therefore, this research proposes a fuzzy logic based model to predict the adequate intravenous fluid resuscitation rate for a burn patient. Fuzzy systems are treated as intelligent system close to human reasoning capabilities and for this application it ensures smooth transition of required rate without showing any high fluctuations for any adjustment. To attain the objective, a fuzzy logic system has been developed aggregating the clinical burn protocol as the knowledge base of the fuzzy inference engine where the %TBSA and HUO measurements are considered as inputs and intravenous fluid rate is the output. Multiple forms of fuzzy membership functions and different types of defuzzification methods have been applied to the proposed system to compile a comparative study. To explore the best approach, ten different test cases have been simulated employing both the manual calculation and the proposed fuzzy model. Results have presented that the proposed fuzzy based system shows to predict the optimum amount of intravenous fluid rate necessary for burn patient’s resuscitation.

Fuzzy Based Predication Technique for Diabetics Association Analysis for Salem District Farmers

Diabetes is a one of the major issue that all people in the world currently face. Diabetes is caused by excessive amounts of sugar in the blood. Once diabetes is diagnosed, it is not completely curable, but it can be controlled with proper medication, exercise and a balanced diet. Diabetes affects the vital organs of the body such as the heart, kidneys, brain and eyes. The diabetes mellitus and its complications can be determined using a variety of pathological tests, such as patients’ symptoms and blood sugar, urine and lipid profile. The use of fuzzy logic in diagnosis is very common and useful because it combines the knowledge and experience of the physician into ambiguous sets and rules. Most of the researchers proposed methods to diagnosis the diabetes mellitus but still it in their infancy level. This work proposed a fuzzy based system for diagnosing diabetes disease. The usage of pesticides in agriculture by farmers is treated as one of the dependent variable for predication. The empirical zif’s law is used to compute the frequency of farmers using pesticides are predicated as diabetic. The output of the proposed system proved that the fuzzy based prediction model diagnosis the disease accurately.