Fuzzy Logic Mechanism Research Articles

The use of fuzzy sets to form the staffing of a medical institution

The timely provision of medical care directly depends on the well-planned time of the medical staff. The use of information systems and decision support systems can improve efficiency and reduce healthcare costs. A difficult problem to solve in medical information systems is the uncertainty of information associated with the failure to meet the conditions for completeness and reliability of incoming information when organizing the work of a medical institution. The use of a fuzzy logic mechanism in decision support systems to assess the staffing of a medical institution will allow taking into account the influence of many factors at the initial decision-making stage, which will contribute to improving the efficiency of the personnel of a medical organization.

Robust Control of Golf Swing Robot Using Backstepping Based on Fuzzy Sliding-Mode and Super-Twisting Backstepping Sliding-Mode Algorithms

This paper focuses on trajectory tracking, robustness and stabilization of a golf swing robot which has been recently developed to simulate the ultra-high-speed swing motions of a golfer. The proposed control strategies are based on the Lyapunov stability theory and include Backstepping and Sliding-Mode Control based techniques. To attenuate the chattering phenomena caused by a discontinuous switching function and improve the dynamic response of the manipulator, a fuzzy system is used in this research; a Backstepping Sliding-Mode Controller (BSMC), a Backstepping Fuzzy Sliding-Mode Controller (BFSMC) and a Super-twisting Backstepping Sliding-Mode Controller (STBSMC) are used to evaluate the proposed hybrid controller’s BFSMC performance. The Lyapunov stability theory is used to guarantee the stability of the proposed closed-loop robot technique. Numerical simulations show the effectiveness of the proposed strategy based on the fuzzy logic mechanism under different disturbances and uncertainties.

Induction Motor Performance Improvement using Super Twisting SMC and Twelve Sector DTC

Induction motor (IM) direct torque control (DTC) is prone to a number of weaknesses, including uncertainty, external disturbances, and non-linear dynamics. Hysteresis controllers are used in the inner loops of this control method, whereas traditional proportional-integral (PI) controllers are used in the outer loop. A high-performance torque and speed system is consequently needed to assure a stable and reliable command that can tolerate such unsettled effects. This paper treats the design of a robust sensorless twelve-sector DTC of a three-phase IM. The speed controller is conceived based on high-order super-twisting sliding mode control with integral action (iSTSMC). The goal is to decrease the flux, torque, the current ripples that constitute the major conventional DTC drawbacks. The phase current ripples have been effectively reduced from 76.92% to 45.30% with a difference of 31.62%. A robust adaptive flux and speed observer-based fuzzy logic mechanism are inserted to get rid of the mechanical sensor. Satisfactory results have been got through simulations in MATLAB/Simulink under load disturbance. In comparison to a conventional six-sector DTC, the suggested technique has a higher performance and lower distortion rate.

Competency Assessment Based on Fuzzy Logic and Artificial Intelligence Mechanism: A Study of Competency Assessment Document for the Algerian SEROR Company

Addressing the issue of how automating the quantitative assessment of competencies through a competency assessment document came to remove the assessment process from the descriptive side and an attempt to propose a new model aligned with modern management requirements. Competency assessment is considered one of the most important indicators for managing competencies in organizations, as it offers valuable insights into the strengths and weaknesses of human resources, which is essential for strategic planning. Organizations are actively seeking a cost-effective and accurate system, aiming to minimize the impact of subjective biases in the evaluation process. Additionally, there is a need for a solution that facilitates swift assessment of a large workforce, ultimately reducing overall costs. To meet these requirements, the current study employs Fuzzy Logic and Artificial Intelligence mechanism to develop a contemporary and precise evaluation model. The study, which analyzed competency assessment data from the Algerian SEROR Company, showcased the possibility of creating a sophisticated quantitative model for competency evaluation using Fuzzy Logic and Artificial Intelligence Mechanism. The results imply that the institution has the potential to embrace a cutting-edge and forward-thinking approach, enhancing objectivity, particularly in dealing with complex systems. Developing a quantitative competency evaluation model requires a comprehensive database that includes relevant factors in the evaluation process, encompassing details about each employee’s position, skills, knowledge and experience. The research emphasizes that creating a quantitative model through Fuzzy Logic and Artificial Intelligence Mechanism is highly achievable, with the applied study indicating an 85% success rate for the model.

Fuzzy-rule-decided small cell offloading for rate-adaptive SVC-DASH video streaming over the vehicle environment

This paper proposed an SVC-DASH video streaming method over the vehicular environment based on the Multi-access Edge Computing (MEC) architecture. For the streaming concern, a control scheme using the Segment-Set-based buffer-aware bitrate adaptation with the backward quality's increment control was proposed; for the mobility concern, a Small Cell Video Streaming Offloading (SC–VSO) control scheme adopting the multiple attribute fuzzy-based decision for deciding whether it can offload the video streaming traffic from a macro cell to a small cell or not was proposed. To have the suitable bitrate adaptation and stable quality variation, three factors that the proposed streaming control scheme considers are (i) estimated bandwidth, (ii) buffer occupancy and (iii) quality variation. Since the vehicle is moving across many types of Base Stations (BSs), the proposed mobility control scheme can decide whether to have SC-VSO or not before vehicle X entering into the ahead small cell's signal coverage based on (i) the network situations of the corresponding macro and small cell and (ii) the reported context from vehicle X using the proposed fuzzy logic mechanism. The performance evaluation results shown that the proposed method has the higher video quality and better quality stability for video streaming over the vehicle environment.

Hierarchical learning particle swarm optimization using fuzzy logic

Particle swarm optimization (PSO) has been used extensively in numerical and engineering optimization problems in the last decades. However, due to drawbacks such as a single learning sample, PSO has the problems of loss of population diversity and easily falls into local optimum. To enhance optimization capability, a PSO based on fuzzy logic and hierarchical learning mechanism (FHPSO) is proposed. In FHPSO, the parameters are dynamically adjusted through fuzzy logic. The purpose of the fuzzy system is to generate appropriate parameters based on the performance metrics at each iteration, which better balances exploration and exploitation capability. Then particles are classified into different layers in terms of their fitness, and the particles in different layers perform different learning mechanisms. Each layer divides the particles into high-energy particles and low-energy particles. The high-energy particles in each layer are qualified to learn from the particles in the upper layer and the low-energy particles learn from the high-energy particles in their layer. This learning mechanism avoids all individuals to learn the global optimal individual at each iteration which will effectively reduce the speed and possibility of premature convergence and maintain population diversity. The FHPSO was compared with 8 well-known algorithms and 6 state-of-the-art PSO variants in the CEC 2022 and CEC 2021 test suites, respectively. The experimental results show significant performance of FHPSO. Simulation results for 5 complex engineering optimization problems and 3D path planning problem also show that the FHPSO can provides more competitive optimization results.

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato

Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm-940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere.

Improvement of the Standard Perturb & Observe MPPT control strategy by the proposed Fuzzy Logic Mechanism for a Cascade Regulation of a PMSM-based PV pumping system

Improvement of the Standard Perturb & Observe MPPT control strategy by the proposed Fuzzy Logic Mechanism for a Cascade Regulation of a PMSM-based PV pumping system

Fido: A String-Based Fuzzy Logic Mechanism for Content Extraction from UAV Data Lakes

In this article, we propose Fido, a fuzzy-based string comparison method for extracting content from data lakes in a camera-based Internet of Drones (IoD) environment. Data lakes support data dumping in their native format, which makes them suitable for real-time deployments such as IoD. However, parsing through these unstructured databases has its concerns, particularly data extraction. Existing works on image-based content extraction focus on complex geometric and matrix mathematical operations, which are expensive in terms of both computation and time. A straightforward and fast solution is necessary for overcoming such challenges. Toward this, we limit our operations to string comparisons between the user's request and tags in the data lake. In particular, we adopt a fuzzy-based approach as it is capable of efficiently handling spelling variations, out-of-order words, and partial matchings, compared to conventional string comparison methods. Through lab-scale experiments, we demonstrate the efficacy of Fido with an almost 80 percent similarity ratio between the request and response images and processing time of 80 µs. Further, we observe minuscule deviations between the coordinates of the request and response images (0.005 units).

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Wireless sensor networks (WSN) is considered as one of the exploring technology for its deployment of the massive number of dedicated sensor nodes which sense the environment and collect the data. The collected data are sent to the sink node through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the Distributed Denial of Services (DDoS) attack. This paper aims to identify the DDoS (Flooding) attack quickly and to recover the data of sensor nodes using the fuzzy logic mechanism. Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) uses type 1 fuzzy logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy- type 2 is used for the recovery of data from the DDoS attack. Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the data under attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is also compared with other related existing schemes. The proposed method saves energy usage by up to 20% compared with the related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

Retraction Note to: Diagnosing breast cancer with an improved artificial immune recognition system

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student’s t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

WITHDRAWN: Modelling and analysis of fuzzy logic mechanism to predict the risk level of glaucoma assessment from retinal fundus

Medical field requires more automation tools to predict diseases caused in visual system as it is a predominant one. Unless manual identification of the disease might be a lengthy diagnosis also error sensitive. When it comes to eye section, retinal image is a primary way of diagnosing the disease. Segmentation of this image for further accurate results may help the medical field to gain knowledge over the disease. The disease that causes irreversible blindness called Glaucoma is focussed in this study and an automation technique for its accurate prediction at early stage is provoked. The symptoms of this disease like severe head pain, vomiting and nausea is the prior warning to individuals. People who undergoes this kind of symptoms are treated as patients for prediction of the disease. Wide range of patient data are collected and it is required to be analysed immediately. This process further requires the diagnosis outcome in a short time and it must be an accurate one. Specifically, fundus retinal image must be screened from the patients and given to the system for segmentation and analysis. The segmented image used to predict the area to be examined. Examining different objects of the retina namely cup, optic disc and vessels of optic nerve helps to give an accurate result. Hence, deciding whether the patient is affected also with its risky level may help the medical experts for further treatment. This can be done by the proposed system model along with the help of fuzzy logic predicts the presence of the disease also its risk level accurately and efficiently.

An efficient mobility aware stable and secure clustering protocol for mobile ADHOC networks

Energy efficiency and security are considered as two crucial issues in the design of MANET. In this paper, an energy efficient stable as well as secure clustering (EESSC) technique is presented for MANET. The presented EESSC technique makes use of fuzzy logic mechanism to select the cluster heads (CHs) properly by the use of five variables namely remaining energy level, node degree, distance, trust level and node mobility. In addition, a standby CH (SBCH) is also introduced to find useful in case of accidental cases like CH dies, CH moves out of the cluster or CH is comprises. In such case, SBCH is invoked to act as CH and again another SBCH is chosen. This process helps to achieve network availability with no disturbances along with the additional level of security. The simulation of the EESSC takes place under diverse aspects and the outcome verified the betterment of the presented model over the compared ones. From the simulation outcome, under the simulation time of 250 s, the CH changes takes place by 19 times whereas a maximum of 28 and 30 CHs were changed by the existing EWCA and EESSC methods respectively.

Adaptive hybrid climate control in the manufacture of microelectronics

The article discusses the procedure for hybrid control of microclimate parameters in a clean room intended for the production of electronics, using a combination of technologies for recurrent estimation and fuzzy regulation. On the basis of the developed procedure, a smooth assessment of the current values of the controlled parameters and compensation control adequate to disturbing influences are provided. The developed technology is designed to provide an increase in the quality indicators of overcoming transient processes, and, consequently, to improve the efficiency of controlling the climate dynamic system during the implementation of the production process for manufacturing electronics with increased requirements for its operational characteristics. Modern information systems are characterized by the rapid development of the capabilities of the used hardware. At the same time, in order to achieve maximum results, the use of artificial intelligence technologies is inevitable, the most effective implementation of which requires adequate consideration of all available information about the controlled production process. In the formulation under consideration, the complexity of the problem is determined by the need to meet mutually conflicting requirements while ensuring specified values of climatic parameters. The specificity of the control problem is determined by the fact that a change (restoration) of the values of one controlled parameter should not lead to a loss of the set values of other climatic parameters. To achieve the described target effect, it is proposed to supplement the procedure of recurrent climate monitoring with a fuzzy logic mechanism when changing values of climatic parameters.

Data-Driven Adaptive Optimal Control for Linear Systems With Structured Time-Varying Uncertainty

In this paper, a data-driven adaptive optimal control strategy is proposed for a class of linear systems with structured time-varying uncertainty, minimizing the upper bound of a pre-defined cost function while maintaining the closed-loop stability. An off-policy data-driven reinforcement learning algorithm is presented, which uses repeatedly the online state signal on some fixed time intervals without knowing system information, yielding a guaranteed cost control (GCC) law with quadratic stability for the system. This law is further optimized through a particle swarm optimization (PSO) method, the parameters of which are adaptively adjusted by a fuzzy logic mechanism, and an optimal GCC law with the minimum upper bound of the cost function is finally obtained. The effectiveness of this strategy is verified on the dynamic model of a two-degree-of-freedom helicopter, showing that both stability and convergence of the closed-loop system are guaranteed and that the cost is minimized with much less iteration than the conventional PSO method with constant parameters.

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.

New CSMA/CA prioritisation based on fuzzy control mechanism

Wireless sensor networks (WSNs) consist of several small nodes that use different types of sensors to gather different types of data and to communicate with each other through several protocols. The IEEE 802.15.4 presents a soft medium access control (MAC) protocol that provides good efficiency for data transmission by adapting its parameters according to characteristics of different applications. The main idea of this paper is to apply a dynamic allocation of priority level for nodes that compete for the access to the medium through tuning parameters in the carrier sense multiple access/collision avoidance (CSMA/CA) algorithm. A priority scheduling is achieved using a fuzzy logic mechanism exploiting queue level and traffic rate of each node in order to ensure adequate dynamic allocation. This paper demonstrates various advantages of priority-based CSMA/CA for quality of service (QoS). Results show significant improvements achieved by our approach among the IEEE 802.15.4 MAC standards.

New CSMA/CA prioritisation based on fuzzy control mechanism

Wireless sensor networks (WSNs) consist of several small nodes that use different types of sensors to gather different types of data and to communicate with each other through several protocols. The IEEE 802.15.4 presents a soft medium access control (MAC) protocol that provides good efficiency for data transmission by adapting its parameters according to characteristics of different applications. The main idea of this paper is to apply a dynamic allocation of priority level for nodes that compete for the access to the medium through tuning parameters in the carrier sense multiple access/collision avoidance (CSMA/CA) algorithm. A priority scheduling is achieved using a fuzzy logic mechanism exploiting queue level and traffic rate of each node in order to ensure adequate dynamic allocation. This paper demonstrates various advantages of priority-based CSMA/CA for quality of service (QoS). Results show significant improvements achieved by our approach among the IEEE 802.15.4 MAC standards.

Evaluation of the presentation skills of the pre-service teachers via fuzzy logic

The main purpose of this study is the presentation skills evaluation of pre-service teachers via fuzzy logic. There are two different groups in this study. The first one consists of 14 instructors serving in the Computer Education and Instructional Technology (CEIT) Department of Firat University (FU). This group has supplied expert view in ranking the importance of the matters in the Presentation Evaluation Scale (PES) and in forming the rule base that sets ground for the operation of fuzzy logic mechanism. The second group consists of 41 juniors having the Operating Systems and Applications Course in the CEIT Department of FU. The students in this group make a presentation associated with the units of this course, PES are applied on them and the study is conducted by the data obtained from this scale. Frequencies and percentages and Wilcoxon test is used to analyze the data sets. It is found that there is a significant difference between the PES obtained student scores and those scores that passes through fuzzy logic mechanism. The scores of some students have decreased, those of some have increased while there is no significant change in some of them.

Optimization of Trusses With Self-Adaptive Approach In Genetic Algorithms

Abstract This paper presents a genetic algorithm method for the optimization of the weight of steel truss structures. In the method of genetic algorithm integer encoding of a discrete set of design variables and novel self-adaptive method based on fuzzy logic mechanism are applied for improving the quality and speed of optimization. Self-adaptive method is applied simultaneously in the selection of chromosomes and to control basic parameters of genetic algorithm. The algorithm proposed in the work was tested on the examples of optimization of steel trusses. Obtained results proved the effectiveness of genetic algorithm in relation to classical genetic algorithm.