Fuzzy Logic Techniques Research Articles

Enhanced Powder Mixed Electrical Discharge Machining Performance toward Improved Machining Characteristics for Inconel 625 Super Alloy by Response Surface Methodology and Fuzzy Logic Techniques

Enhanced Powder Mixed Electrical Discharge Machining Performance toward Improved Machining Characteristics for Inconel 625 Super Alloy by Response Surface Methodology and Fuzzy Logic Techniques

Fuzzy-logic technique for gold mineralization prospecting using Landsat 9 OLI processing and fieldwork data in the Bibemi goldfield, north Cameroon

Identifying potential hydrothermal alteration areas is indeed an essential method for mineral exploration. In this research, we developed an algorithm for the delineation of alteration mineral deposits related to gold mineralization in the Bibemi region using set of criteria derived from Landsat 9 OLI data using false colour composites, band ratios, principal component analysis, spectral angle mapper, and fuzzy-logic overlay methods. The methods used showed iron-oxides, ferrous, and hydroxyl-bearing and carbonate mineral properties related to gold mineralization. The fuzzy overlay map identified regions depending on their mineralization prospective, serving as foundation for prospective mineral deposit evaluation investigation, which was produced by the merging of band ratios and PC's alteration markers labelled very good, and excellent and encompasses 0.8–0.9, 0.9–1.0 respectively. The identified regions fit gold mineralization zones based on their potential as proven by prior and field research. In addition, lineaments analysis showed the presence of three main structural direction impacting the Bibemi region (N–S, NNE-SSW, and ESE-WNW to SSE-NNW), when merged with identified rock formations permits the possible deposition of mineral deposits. The innovative aspect of this research is the integration and processing of Landsat 9 OLI and fieldwork data, which allows for the identification of potentially mineralized rock formations and defining exploration targets.

Simulation model of a patient with type 1 diabetes using fuzzification

Type 1 diabetes is one of the most common diseases. The disease is caused by a lack of insulin secretion from the beta cells of the pancreas, which leads to improper regulation of blood glucose levels. The article presents a simulation model for determining changes in glucose-insulin levels using fuzzy logic techniques. The work concerns a quite simple deterministic simulation model of a digital twin of a type 1 diabetes patient, and fuzzification can significantly improve the efficiency of this model. A series of numerical experiments showed that enriching a simple deterministic patient model with a fuzzy approach gives much more accurate results than the simple deterministic model. The use of fuzzy sets opens up a number of possibilities and is a completely natural approach, resulting from, among others, the specificity of the simulated phenomenon - vital parameters of people with type 1 diabetes.

Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review

Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.

Solar Panel Efficiency Improvement through Dual-Axis Solar Tracking with Fuzzy Logic and Water Treatment Techniques

Indonesia's heavy reliance on non-renewable energy sources, such as fossil fuels and other resources obtained from mining, poses sustainability challenges. Solar panels, which are environmentally friendly and renewable energy alternatives, are designed to convert solar energy into electricity, and they have shown room for improvement in their efficiency. One method to enhance its efficiency is the utilization of dual-axis solar tracking, employing linear actuators for control over both horizontal and vertical panel movements. In addition, solar panels frequently experience efficiency losses as a result of high working temperatures when exposed to sunlight. The use of water treatment techniques may help address this problem. In this research, the two-axis solar tracking approach with water treatment methods were combined to achieve greater efficiency and boost energy production. A notable increase in solar panel efficiency was seen subsequent to the design, implementation, and testing of the proposed system, resulting in a notable rise in power output. Combining the two-axis solar tracking approach with water treatment methods produced solar panels with a 7.46% efficiency and a 17.77% power increment. Dual-axis solar tracking and combined with water treatment could significantly increase solar panel efficiency, which will ultimately lead to environtmentally clean renewable energy production increment.

Penerapan Artificial Intelligence Menggunakan Fuzzy Logic dalam Dunia Pendidikan

This research aims to apply artificial intelligence or what is commonly known as Artificial Intelligence (AI) using fuzzy logic in the world of education. The topic of artificial intelligence has become a hot and important topic of conversation today. Fuzzy logic allows processing unclear information and is used to overcome uncertainty that exists in the world of education. This article discusses the use of fuzzy logic techniques to improve the quality of education. One application of fuzzy logic is in the development of an automatic assessment system used by lecturers in providing assessments to students in the learning process. Fuzzy logic can carry out assessments objectively and enable more appropriate curriculum adjustments based on students' interpersonal skills which can ultimately increase the efficiency of the learning process. The use of fuzzy logic can provide recommendations for the learning process that can help students find material that suits their interests and needs. Fuzzy logic can be used to measure student development in social and emotional cognitive aspects. The application of AI with fuzzy logic aims to improve the quality of education and student learning experiences in the world of education.

Designing accurate lightweight intrusion detection systems for IoT networks using fine-tuned linear SVM and feature selectors

Intrusion detection systems (IDSs) play a crucial role in ensuring the security and integrity of Internet of Things (IoT) networks by blocking unwanted packets and facilitating secure traffic flow. However, traditional IDSs based on data mining, fuzzy logic, heuristics, rough sets, or conventional machine learning (ML) techniques often lack accuracy and are not energy efficient, primarily due to inappropriate feature selection or the use of all features in datasets. To address these challenges, this study proposes a lightweight, accurate, and high-performance IDSs for IoT networks using fine-tuned Linear Support Vector Machines (LSVMs) and feature selection methods. Four feature selectors, including Importance Coefficient-, Forward- and Backward-Sequential-, and Correlation Coefficient-based approaches, were applied to identify the most important and efficient features from three datasets: KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021. The fine-tuned LSVMs algorithm was then trained on subsets of the selected and full features of the datasets to detect various IoT botnet attacks. Evaluation results show that the IDS models trained with subsets of relevant features outperform those trained with the full feature sets of the datasets in terms of training and test performance and accuracy. The study concludes that it is possible to develop lightweight IDSs by training them with a reduced number of features (6) instead of using the full features (40, 15, 115) in KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021, respectively. The findings highlight a potential for significantly improving the efficiency and accuracy of IDSs on IoT networks using the fine-tuned feature selectors and LSVMs.

ANFIS based method for faults detection in the photovoltaic system

<span>As the case with all electrical and electronic systems, a photovoltaic (PV) system can be exposed to several failures causing it to malfunction; several studies have found that the reliability of the PV systems is highly dependent on the material used for the construction of the PV panels, temperature, humidity and solar radiation. A PV system can have several faults, whether construction type faults, or material and electrical faults caused by climatic conditions. This requires identification, the main objective of which is to provide a tool that can detect and locate these faults in order to guarantee optimal performance of the system, and thus reduce maintenance costs and above all increase productivity by increasing the availability rate facilities in order to have a better performance. The objective of this article is to propose a technique for detecting and locating faults in a PV system, the proposed algorithm is based on adaptive neuro fuzzy inference system (ANFIS). This algorithm is based on the technique of artificial neural networks and fuzzy logic. Nine faults will be examined with simulation results under MATLAB-Simulink in PV system (array, converter DC-DC and battery).</span>

Toward Fuzzy Activation Function Activated Zeroing Neural Network for Currents Computing

In order to improve the convergence and noise resistance ability of the ZNN models, a fuzzy activation function (FAF) is designed. Based on the FAF, a fuzzy activation function activated zeroing neural network (FAFZNN) for online fast computing circuit currents is proposed. By introducing the fuzzy logic technique, the convergence and noise resistance ability of the proposed FAFZNN model are further promoted, and it realizes prescribed-time stable, which is irrelevant to its system initial states even in noisy environment. Moreover, the prescribed-time convergence and strong robustness to noises of the proposed FAFZNN model are verified by strict mathematical analysis. The comparable simulation results for static direct currents (DC) and dynamic alternating currents (AC) computing in noiseless and noisy environment further validates its superior effectiveness and robustness for practical applications.

Intelligent Model for Improving Risk Assessment in Sterilization Units Using Revised FMEA, Fuzzy Inference, k-Nearest Neighbors and Support Vector Machine.

The complex environment of the hospital and the critical operations practiced in the medical departments, such as the sterili zation unit, require implementing risk assessment plans as fundamental support for effective management. The Failure Modes Analysis and Effects (FMEA) method is one of the popular methods used to perform the risk assessment process. Fuzzy logic and machine learning techniques provide robust devices that improve the efficiency of several risk assessment methods, such as FMEA. Hence, this study aims to enhance the efficiency of risk assessment in hospital sterilization units using an intelligent model based on revised FMEA, an improved FMEA adaptable to the studied system, fuzzy inference system, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) techniques. An interesting application of the model in the central sterilization unit of the largest university hospital is presented. The performance of the proposed model is evaluated at the end to prove its efficiency.

Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array

Introduction. Presently, signal analysis of stator current of induction motor has become a popular technique to assess the health state of asynchronous motor in order to avoid failures. The classical implementations of failure detection algorithms for rotating machines, based on microprogrammed sequential systems such as microprocessors and digital signal processing have shown their limitations in terms of speed and real time constraints, which requires the use of new technologies providing more efficient diagnostics such as application specific integrated circuit or field programmable gate array (FPGA). The purpose of this work is to study the contribution of the implementation of fuzzy logic on FPGA programmable logic circuits in the diagnosis of asynchronous machine failures for a phase unbalance and a missing phase faults cases. Methodology. In this work, we propose hardware architecture on FPGA of a failure detection algorithm for asynchronous machine based on fuzzy logic and motor current signal analysis by taking the RMS signal of stator current as a fault indicator signal. Results. The validation of the proposed architecture was carried out by a co-simulation hardware process between the ML402 boards equipped with a Virtex-4 FPGA circuit of the Xilinx type and Xilinx system generator under MATLAB/Simulink. Originality. The present work combined the performance of fuzzy logic techniques, the simplicity of stator current signal analysis algorithms and the execution power of ML402 FPGA board, for the fault diagnosis of induction machine achieving the best ratios speed/performance and simplicity/performance. Practical value. The emergence of this method has improved the performance of fault detection for asynchronous machine, especially in terms of hardware resource consumption, real-time online detection and speed of detection.

Fuzzy-based optimization of AODV routing for efficient route in wireless mesh networks

The performance of any communication system heavily relies on the efficient routing of interventions. This article addresses the significant issue of routing protocol selection for optimal path determination in networks. Particularly, when wireless communication occurs among mobile nodes with limited resources, such as batteries, the routing problem becomes even more challenging. This article proposes the Fuzzy Control Energy Efficient (FCEE) routing protocol to overcome these challenges. The FCEE protocol combines the Ad-Hoc On-Demand Distance Vector (AODV) protocol with fuzzy logic techniques to enhance network lifetime and performance. The proposed approach introduces a memory-based channel integrated with fuzzy logic methodologies, which effectively restricts the forwarding of unnecessary broadcast packets based on the energy availability of the operating node. Through extensive simulations, demonstrate the promising capabilities of FCEE as a routing protocol for wireless mesh networks. To further assess the effectiveness of the FCEE protocol, the proposed article compares it with two existing routing protocols: AODV and Intelligent Routing AODV (IRAODV). The simulation results shows that the FCEE routing protocol significantly enhances the reliability of the conventional AODV, providing improved link connectivity and longer route lifetimes. Additionally, our proposed protocol enhances the quality of service (QoS) for mesh routing, with an average throughput of 351.374 (Kbps) compared to 90 (Kbps) for IRAODV and 39 (Kbps) for AODV. Moreover, FCEE exhibits superior energy efficiency with an average energy consumption of 14, while IRAODV and AODV consume 40 and 90 joules, respectively. In conclusion, the FCEE routing protocol demonstrates its potential to address the challenges of efficient routing in wireless mesh networks. By leveraging fuzzy logic and integrating it with AODV, FCEE enhances network lifetime, performance, and energy efficiency, making it a promising solution for future wireless communication systems.

FUZZY LOGIC SYSTEMS WITH DATA CLASSIFICATION - A COOPERATIVE APPROACH FOR INTELLIGENT DECISION SUPPORT

In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets.

The Use of GIS for Identifying Optimum Landfill Sites in Osogbo, Southwestern, Nigeria

The amount of waste disposed off globally has significantly increased due to increase in population and change in consumer habits. Million tons of organic waste are disposed of globally through incineration, land applications, landfill, and other methods. Selecting appropriate places for waste landfills is a challenging task for planners, especially in states with dense populations such as Osogbo due to land scarcity, land prices, and increased solid waste generation. Therefore, this study aimed to identify the optimal suitable areas for the solid waste landfill for proper waste management in the study area. In this work, a multi-criteria decision-making approach combining GIS and the Fuzzy Logic technique for landfill suitability site evaluation was used. Six factors are involved in the selection process namely soil, drainage, land use/land cover, built-up areas, road network, and slope. Using the weighted overlay analysis tool in the GIS environment, the results of the MCDM assigned to each criterion show that five potential sites in this study were suitable for the construction of a potential landfill, of which two are located in the most suitable zone and three are located in the unsuitable zone. It was concluded that the results presented on the final aggregation maps demonstrate the significance of selecting the appropriate criteria weights for the environmental and economic set of standards.

Spectrum Prediction Indicating Bearing State in Induction Motor by Forced Vibration Analysis and Fuzzy Logic Technique

Spectrum Prediction Indicating Bearing State in Induction Motor by Forced Vibration Analysis and Fuzzy Logic Technique

Perovskite lattice constant prediction framework using optimized artificial neural network and fuzzy logic models by metaheuristic algorithms

Perovskites have gained significant attention in recent years due to their unique and versatile material properties. The lattice parameters of the perovskite compounds play a crucial role in the engineering of layers and substrates for heteroepitaxial thin films. As an essential parameter in the cubic perovskite structure, the lattice constant, plays a significant role in the development of materials for specific technological applications and serves as a distinctive identifier of the crystal structure of the material. In the field of materials science, advanced Computational Intelligence (CI)-based techniques have become increasingly important for simulating the relationship between the physicochemical parameters of chemical elements and the physical properties of materials and compounds. Hence, this paper presents efficient techniques based on artificial neural network (ANN) and fuzzy logic to predict the lattice constants of pseudo-cubic and cubic perovskites. The identification of optimized parameters for the ANN and fuzzy logic models is accomplished using innovative metaheuristic algorithms such as, Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO) and Imperialist Competitive Algorithm (ICA). In the first part, the study assessed, the effectiveness of various metaheuristic algorithms (PSO-IWO-ICA) in tuning the parameters of the ANN prediction structure in order to get the optimal parameter of the ANN. Whereas in the second part, once we extracted the best optimization algorithm, we combined it with the fuzzy logic technique and then we compared the effectiveness of the two techniques, ANN and Fuzzy logic. On the basis of root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2), the proposed PSO-ANN and PSO-Fuzzy based models are compared with existing and recent models such as Ubic, Sidey, and Owolabi. The proposed PSO-Fuzzy model performs better than our PSO-ANN model, the Ubic, Sidey, and Owolabi models, with performance improvement of 70.90%, 90.36%, 89.74% 84.46%, respectively on the basis of RMSE. Similarly, it attains performance improvement of 71.26%, 90.31%, 89.58%, and 85.02% on the basis of MAE. Furthermore, the developed PSO-ANN based model outperforms the Ubic, Sidey and Owolabi models with performance improvement of 66.86%, 64.74% and 46.60% respectively, on the basis of RMSE and percentage enhancement of 66.27%, 63.75%, and 47.90% when compared on the basis of MAE. Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.

Improvement of the Power System's Transient Stability Using the Unified Power Flow Controller with Fuzzy Logic Technique

By combining the functions of many FACTS (flexible alternative current transmission systems) devices into a single unit, the UPFC (unified power flow controller) is the most efficient FACTS device available. Two voltage converters work together to form the Unified Power Flow Controller, a flexible tool for enhancing voltage regulation, power flow management, and power system stability. Both converters rely heavily on their control methods. This article discusses the power flow mechanism in a transmission line, which is necessary for understanding the functioning of UPFC in a transmission line and using a standard PI controller with voltage references in the control circuit of both converters. This study aims to enhance the power system's dependability and stability by developing a smart support-based UPFC device. Fuzzy logic is a useful tool for controlling UPFC equipment in a variety of failure scenarios. The fuzzy logic controller contrasts power grid characteristics such as a rotor speed deviation to a reference value to create operational pulses for the voltage source converter in the UPFC system. The suggested model will be tested using MATLAB simulation. The success of the proposed model is shown by thoroughly comparing simulation results to those of previously established models. This research paper's original contribution is the creation of a smart support-based UPFC device with fuzzy logic control. By comparing power system parameters such as rotor speed deviation to a reference value and generating operational pulses for the voltage source converter in the UPFC system, this technique increases the electricity grid's dependability and stability. The effectiveness of the suggested model is proved by a comparison of simulation results obtained in a MATLAB environment with those obtained from previously developed models.

A Novel Method of Fraud Detection of Credit Cards by Fuzzy, LSTM, and PSO Optimization

Since online shopping has become so popular, credit card theft has skyrocketed. This makes it hard to spot fake charges on accounts. In this research, credit card fraud detection is performed using a fuzzy database. It has been considered a data mining challenge to reliably identify whether or not a transaction is legitimate. This paper discusses the LSTM method and fuzzy logic. The learning process was sped up and made more precise by using a technique called particle swarm optimization (PSO). Performance values have been compared with those of the LSTM and fuzzy logic techniques, and a PSO-based neural network has been intensively trained by increasing the number of iterations and the population, or number of swarms. It has been shown that the PSO-based algorithm yields the best result for detecting fraudulent transactions. The goal of this method is to lower the mean square error (MSE) rate of the system. PSO is a popular optimization technique that can be used to locate the optimal set of features for the credit card fraud detection system. The proposed method PSO shows less mean squared error compared with Fuzzy and LSTM techniques.

The application of fuzzy logic techniques to improve decision making in apparel size

Traditional set theory, or crisp set theory, is built on the concept of crisp sets. These are sets for which the membership of an element within a set is defined as either true or false; in or out; 1 or 0. This construction is extremely useful, as mathematics has shown, but it struggles to model concepts of our world that possess vagueness or uncertainty. Therefore, we explore an expansion of set theory to allow an element to be partially within a set, thus constituting what is known as a fuzzy set. This paper introduces the basic concept of fuzzy sets, which includes fuzzy sets and crisp sets, as well as the operations of a fuzzy set and fuzzy classification systems. Fuzzy logic has been utilized to solve numerous textile-related difficulties, one of which was determining the proper clothing size. In this study, we examined fuzzy logic applications in textiles, such as the construction of fuzzy expert systems and fuzzy logic for predicting clothing size. This research demonstrates that when determining the correct size of clothing, the outcome is heavily reliant on fuzzy logic.

Efficient hybrid strategy based on FFT and fuzzy logic techniques applied to fault diagnosis in power transmission line

Efficient hybrid strategy based on FFT and fuzzy logic techniques applied to fault diagnosis in power transmission line