Fuzzy Logic Approach Research Articles

Integrating multispectral remote sensing and geological investigation for gold prospecting in the Borongo-Mborguene gold field, eastern Cameroon

Multispectral remote sensing data has great promise for mineral prospecting via regional-scale mapping of alteration zones and geological structures, once the method is calibrated using in-situ geological observations. The Borongo-Mborguene area, located in eastern Cameroon at the transition from sub-tropical to arid climates, hosts several alluvial gold deposits. In this investigation, maps developed by processing of Landsat-8 OLI images, ASTER satellite images, and SRTM DEM data were compared to results of a comprehensive field survey to locate zones of hydrothermal alteration and regional structural lineaments, and to verify the association of these features with gold mineralization. Specific Landsat-8 and ASTER band ratios, as well as principal component analysis of the multispectral images, were selected to isolate spectral signals that characterize particular groups of alteration minerals, such as clays, hydrated carbonates, hydrated sulphates, iron oxides, and iron hydroxides. Lineaments and their preferred orientations were extracted by filtering of the digital elevation data. A fuzzy logic approach was subsequently applied to the ensemble of resulting maps to highlight five areas of concentrated hydrothermal alteration and structural complexity as potential gold prospects (Mborguene, Boutila, Nandoungue, Borongo and Ouaden villages). Petrographic study of samples collected from the remotely-identified alteration zones revealed a hydrothermal ore assemblage of gold, galena, pyrite and hematite in mineralized quartz veins. Structural control of the alteration zones in the Borongo-Mborguene gold field was confirmed by field mapping along a shear zone. Hence, our petrographic and field studies indicate that the multispectral remote sensing data and data processing methods developed herein are appropriate for mineral exploration in remote sub-tropical areas of Africa. More generally, the computationally efficient and simple procedure developed here should yield accurate preliminary maps of the spatial distribution of hydrothermal halos and related mineral deposits in challenging mixed-type vegetated terrain around the globe.

Type-3 fuzzy logic and Lyapunov approach for dynamic modeling and analysis of financial markets

Chaos theory offers a new way to investigate variations in financial markets data that cannot be obtained with traditional methods. The primary approach for diagnosing chaos is the existence of positive small Lyapunov views. The positive Lyapunov index indicates the average instability and the system's chaotic nature. The negativity indicates the average rate of non-chaoticness. In this paper, a new approach on basis of type-3 fuzzy logic systems is introduced for modeling the chaotic dynamics of financial data. Also, the attracting dimension tests and the Lyapunov views in the reconstructed dynamics are used for examinations. The simulations on case-study currency market show the applicability and good accuracy of the suggested approach.

Modeling performance evaluation in badminton sports: a fuzzy logic approach

Spectators and many young students have flocked to badminton matches in recent years. Badminton practice has received a lot of media coverage. The current state of badminton evaluation methods is lacking in reliability. This article's overarching goal is to examine the many applications of fuzzy logic in badminton performance evaluation and improvement. Data on the badminton technique's flexion and extension phases are mapped into the suggested model using a fuzzy inference system (FIS). This study suggests a fuzzy logic-based badminton-specific objective fuzzy inference system (Bmt-FIS) to evaluate team sports. Despite the gravity of the situation, decisions involving performance reviews often use subjective data. These common decision-making problems may be realistically addressed by fuzzy logic models. Fuzzy logic has the potential to be an effective tool in situations where both quantitative and qualitative data interpretation are allowed. To do this, it accounts for the inherent variability in athletic performance by taking into consideration the 'hazy' or 'uncertain' limitations of data. By taking limitations into account, a rule-based approach makes performance evaluation more precise. Here, a fuzzy inference system (FIS) uses the input variables to evaluate the student's performance. While data mining approaches have been studied, the adaptive neural fuzzy method outperforms others because of its exceptional accuracy.This method eloquently and clearly conveys the many levels of integrity and ambiguity. Also, fuzzy logic may be a great tool for evaluating badminton skills. This foundational study connects the dynamic realm of sports with static measures.

Towards Refined Autism Screening: A Fuzzy Logic Approach with a Focus on Subtle Diagnostic Challenges

This study explores the creation and testing of a Fuzzy Inferencing System for automating preliminary referrals for autism diagnosis, utilizing membership functions aligned with the Autism Quotient 10-item questionnaire. Validated across three distinct datasets, the system demonstrated perfect accuracy in deterministic settings and an overall accuracy of 92.91% in a broad fuzzy dataset. The use of Fuzzy Logic reflects the complex and variable nature of autism diagnosis, suggesting its potential applicability in this field. While the system effectively categorized clear referral and non-referral scenarios, it faced challenges in accurately identifying cases requiring a second opinion. These results indicate the need for further refinement to enhance the efficiency and accuracy of preliminary autism screenings, pointing to future avenues for improving the system’s performance. The motivation behind this study is to address the diagnostic gap for high-functioning adults whose symptoms present in a more neurotypical manner. Many current deep learning approaches for diagnosing autism focus on quantitative datasets like fMRI and facial expressions, often overlooking behavioral traits. However, autism diagnosis still heavily relies on long histories and multi-stakeholder information from parents, teachers, doctors and behavioral experts. This research addresses the challenge of creating an automated system that can handle the nuances and variability inherent in ASD symptoms. The theoretical innovation lies in the novel application of Fuzzy Logic to interpret these subtle diagnostic indicators, providing a more systematic approach compared to traditional methods. By bridging the gap between subjective clinical evaluations and objective computational techniques, this study aims to enhance the preliminary screening process for ASD.

A New Hybrid COA-OOA Based Task Scheduling and Fuzzy Logic Approach to Increase Fault Tolerance in Cloud Computing

INTRODUCTION: Technology is made available to customers worldwide through a distributed computing architecture called cloud computing. In the cloud paradigm, there is a risk of single-point failures, in order to prevent errors and gain confidence from consumers in their cloud services, one problem facing cloud providers is efficiently scheduling tasks.OBJECTIVES: High availability and fault tolerance must be offered to clients by these services. Fuzzy logic and hybrid COA-OOA are used in this study proposed fault-tolerant work scheduling algorithm. Jobs given by users and virtual machines are considered as input for this proposed approach. METHODS: The given tasks are initially scheduled utilizing the FIFO order. Then, it is rescheduled utilizing the Hybrid Coati Optimization Algorithm (COA) - Osprey Optimization Algorithm (OOA) for scheduling the task based on priority.RESULTS: This scheduled job is assigned to the VM for further execution. If the jobs are not executed successfully, then fault tolerant mechanism is carried out. Faults are recognized by employing fuzzy logic in this proposed approach. CONCLUSION: This proposed approach attains 62 sec response time, 61 sec of makespan and 98% success rate. Thus, this proposed approach is the best choice for efficient task scheduling with fault tolerant mechanism.

APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.

Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation

A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint’s understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences.

A comparative study based on AHP and fuzzy logic approaches for landslide susceptibility zoning using a GIS-based multi-criteria decision analysis

A comparative study based on AHP and fuzzy logic approaches for landslide susceptibility zoning using a GIS-based multi-criteria decision analysis

Distribution of fish larvae and juveniles on salinity in an estuary predicted from remote sensing and fuzzy logic approach

Distribution of fish larvae and juveniles on salinity in an estuary predicted from remote sensing and fuzzy logic approach

A Fuzzy Logic Approach for Efficient Control of BLDC Motors for Low-Cost Electric Two-Wheelers

A Fuzzy Logic Approach for Efficient Control of BLDC Motors for Low-Cost Electric Two-Wheelers

Chicken Egg Hatching Optimization with Automatic Control Using Fuzzy Logic<b></b>

Egg hatching is an important process in the livestock industry to produce quality poultry seedlings. In the hatching process environmental conditions such as temperature and humidity must be carefully regulated to ensure optimal hatching. In an effort to improve the efficiency and accuracy of hatching, this research introduces an egg hatching machine tool that adopts a fuzzy logic approach. The developed machine tool is equipped with temperature and humidity sensors that measure the environmental conditions inside the egg incubator in real-time. The data obtained from the sensors are used as inputs for a fuzzy logic-based control system that regulates the temperature and humidity of the incubator. The hatching machine is built keeping in mind the ideal conditions between 35° - 40° with 70% - 80% humidity. Fuzzy logic allows the system to handle uncertainty and ambiguity in temperature and humidity settings. Based on the fuzzy rules set, the system can adaptively adjust the environmental conditions to achieve optimal conditions for hatching eggs.

Theoretical and experimental study on (E)-N'-(2-hydroxy-5-(thiophen-3-yl)benzylidene)benzohydrazide compound: Its optimization with DFT and Fuzzy logic approach (FLA), structural and spectroscopic investigation, HOMO-LUMO, MEP, atomic charge, and NBO analysis

The spectral properties, including 1H–13C NMR, FTIR, and UV–Vis, of the newly synthesized compound, (E)-N'-(2-hydroxy-5-(thiophen-3-yl)benzylidene)benzohydrazide (TBH), were calculated using DFT/B3LYP/6–311G(d,p) and compared with experimental spectra. Experimental FTIR, UV–Vis, NMR and DFT calculations showed that the TBH was in the form of keto-amine/phenol-imine tautomer stabilized by intramolecular hydrogen bonding in solid state and solution. Theoretical calculations were also accomplished for the HOMO-LUMO energies, MEP surface, net atomic charges, and NBO analysis. The experimental and theoretical band gap (ΔE) values examined in different organic solvents for TBH were found to be in the range of 3.87–3.90 eV and 3.84–3.93 eV, respectively. The theoretical results revealed the molecule's high intramolecular charge transfer, realistic molecular stability, and high chemical reactivity. A good agreement was observed between the experimental data and the DFT-based calculations for the TBH molecule. Additionally, the DFT calculations were integrated with the fuzzy logic approach (FLA), and the Mamdani fuzzy logic system was used to pattern the potential energy of the molecule based on its two torsion angles.

Integration of Fuzzy Logic and Soft Computing Approaches for the Detection and Classification of Liver Cancer

Integration of Fuzzy Logic and Soft Computing Approaches for the Detection and Classification of Liver Cancer

Dynamic Thermal Rating in Diverse Climatic Conditions: Uncertainty Analysis and Fuzzy Logic Approach

Abstract: In recent times, there has been a significant rise in the need for electricity, a trend expected to continue. This surge in demand has resulted in transmission lines operating at much higher capacities, which in turn exposes them to increased thermal and mechanical stress, ultimately impacting the reliability of the transmission network. In this paper presents a comprehensive analysis of the static thermal rating (STR) and dynamic thermal rating (DTR) of power lines across varied climatic conditions. The study encompasses a thorough investigation into the thermal behavior of power lines during both summer and winter seasons over a 24-hour period. Through rigorous computational simulations and empirical data collection, the STR and DTR profiles are determined to assess the real-time capacity of power lines under changing environmental conditions. Furthermore, the research introduces a novel approach utilizing fuzzy logic to model the dynamic thermal rating (FDTR) based on the IEEE 738 standard, in regions characterized by diverse climatic patterns. By integrating fuzzy logic with meteorological data, the FDTR model offers enhanced accuracy in predicting the thermal performance of power lines, considering factors such as ambient temperature, wind speed, and solar radiation. The proposed methodology aims to provide utilities and grid operators with a reliable tool for optimizing power transmission capacity, mitigating the risk of overheating, and ensuring grid reliability in regions with complex and fluctuating weather conditions. This study contributes to the advancement of smart grid technology and facilitates efficient energy management in diverse climate zones

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.

Performance evaluation of PID and Fuzzy Logic controllers for residential ORC-based cogeneration systems

Cogeneration systems have been identified as a way to reduce primary energy consumption, especially the ones using fossil fuels. Although this technology has already reached maturity on a medium-to-large scale, it is not very well explored on a micro-scale, especially at a residential level where the market is currently filled with carbon-fueled boilers and combi-boilers. For implementation at a residential level, the organic Rankine cycle has been mentioned as a suitable technology because of its simplicity and thermal/ electrical efficiencies. Despite its performance, one of the most important aspects of these applications is the response time, which is mainly connected to the system’s control. Due to its influence, this study aims to provide insights for developing flexible control strategies and analyze two different control methodologies, a standard PID and a Fuzzy Logic controller, considering the operating conditions of an experimental ORC-based micro-cogeneration test rig.Before being implemented in the test rig, both control methodologies were optimized and tested in a virtual environment in order to avoid the exposure of the test rig to unsafe operating conditions. While both controllers show a reasonable response in operating conditions similar to the ones used in the virtual environment, the Fuzzy Logic approach proved to be more adaptable to changes in the system’s dynamics, which are caused by intrinsic (e.g., unpredicted evaporative phenomena) or external disturbances (e.g., a pressure ratio variation), obtaining an overall better performance. Additionally, the limitations of static PID controllers for complex nonlinear processes, like the ORC, were highlighted by their incapacity to stabilize the system when the organic fluid is diverted from the bypass line to the expansion valve. Conversely, it was concluded that the ability to incorporate expert knowledge and heuristics makes the Fuzzy Logic controller well-suited for ORC systems with uncertain, unstable, and fluctuating behavior.

Most quantifiers have many meanings.

In this paper, we investigate, by means of a computational model, how individuals map quantifiers onto numbers and how they order quantifiers on a mental line. We selected five English quantifiers (few, fewer than half, many, more than half, and most) which differ in truth conditions and vagueness. We collected binary truth value judgment data in an online quantifier verification experiment. Using a Bayesian three-parameter logistic regression model, we separated three sources of individual differences: truth condition, vagueness, and response error. Clustering on one of the model's parameter that corresponds to truth conditions revealed four subgroups of participants with different quantifier-to-number mappings and different ranges of the mental line of quantifiers. Our findings suggest multiple sources of individual differences in semantic representations of quantifiers and support a conceptual distinction between different types of imprecision in quantifier meanings. We discuss the consequence of our findings for the main theoretical approaches to quantifiers: the bivalent truth-conditional approach and the fuzzy logic approach. We argue that the former approach neither can explain inter-individual differences nor intra-individual differences in truth conditions of vague quantifiers. The latter approach requires further specification to fully account for individual differences demonstrated in this study.

Fuzzy inference logic assisted rendezvous points based effective routing strategy in wireless sensor networks

SummaryIn this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection.

IT-2 Fuzzy Control and Behavioral Approach Navigation System for Holonomic 4WD/4WS Agricultural Robot

In contemporary agriculture, researchers are focusing on precision farming and environmentally conscious techniques, leading to the advancement of autonomous agricultural mobile robots. Consequently, A navigation system using TSK Interval Type-2 fuzzy logic has been developed and, for the first time, applied to a holonomic mobile agricultural robot equipped with four-wheel drive and steer. This system employs a behavioral approach to accomplish the robot’s primary objective in unfamiliar environments. It comprises four controllers: one exclusively dedicated to goal-seeking behavior, with the remaining controllers responsible for obstacle avoidance. Supervision of these controllers is conducted through the Mandani fuzzy logic approach, which combines the outputs of all four controllers, robot wheel velocities and steering angles, using a proposed equations. Various case studies were examined and simulated utilizing MATLAB and VREP software platform to assess the navigator system’s performance. The simulation outcomes showcase the efficiency of the proposed equations and the system’s navigation capabilities. The stability of the entire system was thoroughly examined and confirmed, employing the Lyapunov function, in environments with and without obstacles.

A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants

Wind energy power plants (WEPPs) are renewable energy generation facilities that are increasingly used today. The performance evaluations of these energy plants are generally based on technical performance. Although there are studies in the literature focusing on determining the technical performance of WEPPs, there is a lack of research on evaluating their sustainable performance. The primary motivation of this study is to develop a decision model for identifying the sustainable performance of WEPPs. The model incorporates fuzzy logic and multi-criteria decision-making (MCDM) approaches, incorporating expert opinions and both qualitative and quantitative criteria. To determine the influence levels of experts, spherical fuzzy (SF) sets are utilized. The weighting of criteria is achieved using the SF-defining interrelationships between ranked criteria II (DIBR II) method, which is a novel extension of the DIBR II method based on SF sets. Additionally, an SF-alternative ranking order method accounting for two-step normalization (AROMAN) method is developed for ranking the sustainable performance of WEPPs, building upon the AROMAN method, and adapting it to SF sets. This hybrid model is named the SF-DIBR II-AROMAN hybrid model. The step-by-step procedures of this model are presented in the research, and an algorithm for these procedures is developed. To demonstrate its practicality, a case study is conducted, focusing on WEPPs with a capacity ranging from 25 to 150 megawatts in Çanakkale, Turkey. According to the research results, among the fifteen WEPPs in the Çanakkale region, “Saros WEPP” is identified as having the best one. The robustness of the obtained results is ensured through sensitivity analyses. Based on the results of two sensitivity analysis scenarios, “Saros WEPP” is found to have the highest sustainable performance. The SF-DIBR II-AROMAN hybrid model results are compared with different alternative ranking method results, highlighting its superior aspects. Overall, SF-DIBR II-AROMAN is consistent and robust. The research also provides insights and managerial implications, as well as explains the benefits of the proposed model for evaluating WEPPs’ sustainable performance.