Fuzzification Process Research Articles

Fuzzy Logic-Based Quantitative Development Model for Job Satisfaction in College Graduates

College graduates’ job satisfaction is validated via their feedback, experiences and personal developments during their career progression. Validation is accomplished to ensure the students’ job satisfaction and retain them for a prolonged time. The traditional validation models have difficulties in analyzing the individual satisfaction level. The research issue is addressed by introducing the Quantitative Assessment Method (QAM) using Fuzzy Logic to validate the job satisfaction level of different newly placed students. The QAM approach assesses the student experience and personal development across various quarters. The fuzzy optimization uses two factors differentially using partial derivatives. The partial derivatives are extracted using the min–max functions of the fuzzification process such that the derivatives are halted after the maximum factors. The proposed method optimizes the validation using individual satisfaction levels and cumulative experience shared by the students. The available derivatives identify the best-afford job satisfaction level for different progression levels. This best-fit feature is handled using multiple min and max derivatives to extract optimal outputs. This proposed method is valid for improving satisfaction levels and experience analysis.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

Fuzzy convolutional neural network model applied to classification problems

This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.

Evaluation model of students’ English learning ability based on fuzzy logic system

Students’ English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student’s performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student’s diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students’ unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model’s performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student’s various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time.

Fuzzy Logic Speed Regulator for D.C. Motor Tuning

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model. This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.

An applied study of multi-layer decision tree optimization algorithms in machine learning

Abstract This paper investigates the multi-layer decision tree optimization algorithm in machine learning and explores its efficiency and accuracy in applying complex data classification. The focus is on the optimization strategy of the algorithm when dealing with large datasets with multiple types of records. The research adopts methods such as data set Discretization, fuzzification processing and hierarchical fuzzy decision tree construction algorithm. Through experimental verification, our constructed multilevel fuzzy decision tree performs well in two application scenarios: rainfall prediction and fingerprint identification. In the rainfall prediction experiment, using 5200 sample data, the algorithm achieves a classification accuracy of 61%. In the fingerprint recognition experiment, from 3,000 fingerprint images, a 100% recognition rate was performed, and the rejection rate was 25.2%. The results show that the multi-layer decision tree optimization algorithm can effectively handle large data sets of multiple types and significantly improve the accuracy and efficiency of classification. The algorithm offers high adaptability and accuracy when dealing with continuous type attributes. This study provides a new perspective and methodology for the application of multi-layer decision trees in machine learning, which is of great significance for the future development of data processing and classification techniques.

Application of Intelligent Algorithms for Optimal Allocation of Accounting Education Resources in Higher Education Environments

Abstract Inadequate allocation of accounting education resources and uneven spatial distribution are the key issues affecting the high-quality development of accounting education, and the arrival of the era of intelligent technology provides ideas for the rational allocation of higher education resources and transformation and upgrading. This paper constructs a multi-objective planning model for accounting education resource allocation and explores the solution method based on the fuzzy particle swarm algorithm. Population initialization is achieved by combining input-output fuzzification processing and particle swarm optimization search in fuzzy control. Subsequently, the fitness of every particle is assessed. Next, according to the formula of PSO algorithm update iteration, the position and speed are updated, the optimal solution of itself and the optimal solution of the population are updated, and the optimization search is continuously iterated. After the model is put into use the institutions in the western region with low and lower levels of efficiency in the allocation of accounting education resources decreased by 7%, and all the regions with low levels of efficiency have decreased. The number of configuration rounds of this paper’s method is smaller than the number of rounds of configuration conflicts of the PCA method and SVM method, which indicates that this paper’s method has good performance and optimizes the allocation of accounting education resources, making accounting education develop in a balanced way.

Kontrol Kestabilan Kapal Autonomous Submarine Surface Vehicle Dengan Metode Fuzzy Logic

The Autonomous Submarine Surface Vehicle is a type of unmanned underwater vehicle. When the ship performs maneuvers, there are large Pitch and Roll motions. This research aims to control the stability of the Autonomous Submarine Surface Vehicle with Fuzzy. The first process is taking Pitch and Roll data followed by the Fuzzification process to change input data with firm values into Fuzzy values. The next stage is Inference by using the rules (if – then) and the Deffuzification process to change the results of the inference stage into output values. The last is the process of stabilizing the ship with a Thruster dc motor. When the system is activated, it has a time of 0.518 seconds faster to steady state than the deactivated system with a roll tilt of (plus) 11°. On the roll tilt of (plus) 20° the highest PWM rotation is 1600µs with a time of 9,342 seconds to steady state and the roll tilt is (plus) 11° with the highest PWM of 1500µs with a time of 4,335 seconds. Based on this research, the Fuzzy Method can control the stability of the Autonomous Submarine Surface Vehicle ship.

Vocal music teaching method using fuzzy logic approach for musical performance evaluation

Vocal music teaching through E-learning platforms and in-person classrooms requires intense attention and practice. The vocals and strings with/without instrument support are required for improving the voice, pitch presentation, and learning improvement. With digitalization, the assessments are performed using a computer and process-aided technologies for musical performance evaluation. This article introduces a Leveled-Fuzzy Logic Approach (LFLA) for evaluating the musical performance of the vocal teaching method. The evaluation improves the teaching mode by matching the actual learning guidelines. The vocal music teaching guidelines vary for different musical sessions and types. Such different aspects are analyzed using leveled fuzzy; the session flow is analyzed for maximum inference with the actual learning process. The number of learning levels forms the crisp input and the input is analyzed until the fuzzification extracts precise high matching for the guided and real-time teaching. The crisp inputs are induced for combinational inference during the fuzzification process using lagging features. The lagging features such as time, sessions, and evaluation per session are considered for fuzzification. The proposed approach is verified using the metrics assessment rate, lag detection, assessment time, and errors.

Perankingan Dosen Berbasis Aktifitas Forum Moodle Menggunakan Metode Fuzzy Tsukamoto

The online learning process currently utilizes a Learning Management System, one of which is Moodle. Moodle provides a forum menu where the forum is a digital forum for asking questions and answering and sharing between lecturers and students. Lecturer and student activity can be seen from the forum menu. For this reason, research was conducted on the ranking of lecturer activities in the moodle forum. The ranking determination uses the Fuzzy Tsukamoto decision making system. Fuzzy Tsukamoto has step efficiency in determining rules and processing data that has a taboo range and produces crips output. The processing steps carried out are determining the variables, giving weights, creating a fuzzy set of each variable, then the fuzzification process and creating a rule base and finally, defuzzification, namely the calculation of negligence and output. Based on the rule and the predicate alpha value, the appropriate value is obtained to rank the quality of lecturers based on their activities in the moodle forum.
 Keywords — database, forum, fuzzy, moodle, tsukamoto

Gastric Disease Diagnostic Expert System Application Using the Fuzzy Mamdani Method

The level of awareness among Indonesian society regarding the importance of maintaining gastric health is still very low, even though gastric diseases can significantly disrupt daily activities. In a medical examination, a doctor detects diseases in a patient's body based on their symptoms or complaints. The patient's action is to meet the doctor in person, and the doctor asks about the symptoms experienced by the patient. In the manual system, there is a drawback where patients have to visit the doctor for consultation or to have their diseases examined, and they also need to prepare the necessary fees for the examination. Such a manual system can be simplified with an information system where patients don't need to visit the doctor to diagnose their diseases. Therefore, the researcher will develop a gastric disease diagnostic expert system application using the fuzzy Mamdani method. The aim is to make it easier for patients/the public to identify the type of disease based on the symptoms experienced, as well as to provide information on solutions, actions, and medication for the disease. The methodology used in developing the gastric disease diagnostic expert system application involves four stages: fuzzification process, implication function, inference process (rules), and defuzzification. The research flow includes data collection through interviews and data sampling, data analysis, calculation using the fuzzy Mamdani method, implementation, and testing using a black box. The result of this research is a gastric disease diagnostic expert system application using the fuzzy Mamdani method with an accuracy of 65%. This application can help individuals to identify the type of disease based on the symptoms experienced without having to immediately consult a doctor, thus avoiding potential issues

APPLICATION OF THE APRIORI ALGORITHM TO DETERMINE THE COMBINATION OF POVERTY INDICATORS

Poverty is a society that has not been solved until now. The decline in poverty in Laweyan District from 2000 to 2013 was 5.71%, among the five lowest in the reduction in the percentage of poverty in Central Java Province. The problem of poverty is very complex, and the differences in regional characteristics, as well as the techniques used, also influence the indicators of the causes of poverty and the formulation of policies for poverty alleviation. This study uses Principal Component Analysis as part of data preprocessing, followed by applying association rules with the Apriori Algorithm to explore the relationship pattern of poverty indicators. Based on the research that has been conducted on the poverty dataset, which consists of 46 attributes, it is found that the attributes that have passed the preprocessing data are six attributes, namely the Poor Population, ADHB in the Communication Sector, ADHB in the Mining and Excavation Sector, ADHB in the Agriculture and Food Crops Sector, ADHB in the Plantation Sector. and unemployment. These six attributes are transformed into Ascending, Fixed, and Descending categorical data. The fuzzification process for the increase and decrease categories uses the shoulder-type triangle membership function. Applying the Apriori Algorithm to the poverty dataset with a minimum support of 0.4 and a minimum confidence of 0.8 produces 38 rules that show the relationship between indicators and poverty and 134 rules that show the relationship pattern between indicators.

Ensemble feature selection using q-rung orthopair hesitant fuzzy multi criteria decision making extended to VIKOR

ABSTRACT This paper investigates ensemble feature selection using the q-rung orthopair hesitant fuzzy multi-criteria decision-making (MCDM) process. A novel algorithm is proposed for the study of ensemble feature selection and it is called as q-rung orthopair hesitant fuzzy MCDM extended to the Visekriterijumska optimizacija Ikompromisno Resenje (VIKOR) (q-ROHFS VIKOR). This is the first time in the literature an ensemble feature selection problem is modelled as a q-rung orthopair hesitant fuzzy MCDM extended to VIKOR technique. In the proposed method, every feature is ranked based on different available rankers and a preference matrix is obtained. In the sigmoidal transformation, the tuning parameter plays a vital role in the fuzzification process. This tuning parameter reduces the computation run-time in the fuzzification process for different high-dimensional datasets that are considered in this paper. By using q-ROHFS VIKOR method, a score is assigned to each feature based on the values of the preference matrix. At last, an output rank vector is produced for all features from which the user can select the desired number of features. To prove the efficiency and optimality of the proposed method, the comparison with basic filter-based feature selections and ensemble feature selection using feature ranking strategy is obtained. The proposed method in this paper is superior and efficient than the ensemble methods based on the accuracy and F-score levels upto 0.96.

A high-performance and ultra-efficient fully programmable fuzzy membership function generator using FinFET technology for image enhancement

In fuzzy systems, efficient programmable membership function generators (MFGs) are the canonical point for the fuzzification process. This work demonstrates a high-performance programmable MFG using 7 nm FinFET technology. The proposed MFG can generate S-shaped, Z-shaped, Gaussian-shaped, and Generalized Bell-shaped membership functions. The proposed design employs 14 FinFETs to control the produced waveforms’ position, height, width, and slope. According to the simulations, the proposed MFG offers remarkable improvements in transistor count (39%), power-delay product (PDP) (54%), absolute error (45%), and root mean square error (36%) compared to the previous MFGs. The proposed design has been utilized for image enhancement to evaluate the performance of the proposed MFG in realistic environments. The image enhancement simulation results indicate a higher peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) than previous related works. A figure of merit (FoM) is defined considering the image enhancement quality metrics and circuit efficiency to benchmark the entire performance of the proposed MFG. The FoM simulations demonstrate that the proposed design shows an excellent trade-off between the circuit performance and image enhancement quality. Our results confirm that the proposed MFG is suitable for developing high-performance on-chip image processing applications.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problems observed in the fuzzification of an unknown pattern is that importance is given only to the known patterns but not to their features. In contrast, features of the patterns play an essential role when their respective patterns overlap. In this paper, an optimal fuzzy nearest neighbor model has been introduced in which a fuzzification process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has been formed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completely llabelled Telugu vowel data set, and the accuracy is compared with the different models and the fuzzy k nearest neighbor algorithm. The proposed model gives 84.86% accuracy on 50% training data set and 89.35% accuracy on 80% training data set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Data Analytics on Unpredictable Pregnancy Data Records Using Ensemble Neuro-Fuzzy Techniques

The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many different techniques. As a result, we used the Ensembling of Neuro-Fuzzy (E-NF) method to perform a high-level classification of medical diseases. E-NF is a layered computational model with self-learning and self-adaptive capabilities to deal with specific problems, such as the handling of imprecise and ambiguous data that may lead to uncertainty concerns that specifically emerge during the classification stage. Preprocessing data, Training phase, Ensemble phase, and Testing phase make up the complete procedure for the suggested task. Data preprocessing includes feature extraction and dimensionality reduction. Besides such processes, the training phase includes the fuzzification process of medical data. Moreover, training of input data was done using four types of NF techniques: Fuzzy Adaptive Learning Control Network (FALCON), Adaptive Network-based Fuzzy Inference System (ANFIS), Self Constructing Neural Fuzzy Inference Network (SONFIN) and/Evolving Fuzzy Neural Network (EFuNN). Later, in the ensemble phase, all the NF methods’ predicted outcomes are integrated, and finally, the test results are evaluated in the testing phase. The outcomes indicate that the method could predict impaired glucose tolerance, preeclampsia, gestational hypertensive abnormalities, bacteriuria, and iron deficiency anaemia better than the others. In addition, the model exposed the capability to be utilized as an autonomous learning strategy, specifically in the early stages of pregnancy, examinations, and clinical guidelines for disease interventions.

The advanced defuzzification methods of the convex α – cut fuzzy sets

The main features of the fuzzy sets and their corresponding membership functions were presented in terms of the fuzzification process and further by the de-fuzzification operation. The convexity of the α -(alfa) cuts of the fuzzy sets is used in the decomposition of the fuzzy sets. The alfa cuts of the fuzzy sets were defined precisely in terms of the pair of functions and their lowest upper and greatest lower bounds. The convex combination of the intervals of the sub-regions of the fuzzy sets and their membership function were considered as the points of the defuzzified values of the fuzzy sets. The methods of the de-fuzzification to the crisp sets were presented by the formulas to find the defuzzification regions and de-fuzzified values. The compositional concepts of the inference as the expansion of the extension principle were introduced to formalize further the fuzzy reasoning by the set of fuzzy rules based on the approximate reasoning.

Integration of 4253HT Smoother with Intuitionistic Fuzzy Time Series Forecasting Model

Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-i-fuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed.

Fuzzy Interpolation Curve Modelling of Earthquake Magnitude Data

This research discussed on developing the fuzzy interpolation curve model which only used spline and B-spline functions in designing curve interpolation. The development of this model is used fuzzy set theory and more specifically fuzzy number concepts since the modeling problem is focused on modeling data. These data are known as uncertainty data and defined through fuzzy numbers which the properties of these data set belong to fuzzy numbers. There are also several steps to be implemented to obtain the crisp fuzzy model of crisp fuzzy data. These steps include fuzzification and defuzzification. For the fuzzification process which used alpha-cut triangular fuzzy numbers, an enhancement is also applied in process of determining the value of alpha based on the fuzzy data in triangular form. A numerical example is implemented to show the fuzzy interpolation curve modeling in which earthquake magnitude data are selected.