scholarly journals Study on Sentiment Classification Strategies Based on the Fuzzy Logic with Crow Search Algorithm

2022 ◽  
Author(s):  
Mazen Mohammed ◽  
Lasheng Yu ◽  
Ali Aldhubri ◽  
Gamil R. S.Qaid
Keyword(s):  
Fuzzy Logic ◽  
Search Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Data Sets ◽  
Fuzzy Logic Algorithm ◽  
Rule Based System ◽  
Proposed Model ◽  
Media Applications

Abstract In recent times, sentiment analysis research has gained wide popularity. That situation is caused by the nature of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the Fuzzy rule-based system (FRBS) with crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. We compared the performance of our proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. We tested our model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrated the effectiveness of the proposed model and achieved competitive performance in terms of accuracy, recall, precision, and the F–score.

scholarly journals A Fuzzy Rule-Based System for Classification of Diabetes

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8095
Author(s):  
Khalid Mahmood Aamir ◽  
Laiba Sarfraz ◽  
Muhammad Ramzan ◽  
Muhammad Bilal ◽  
Jana Shafi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Early Diagnosis ◽  
Early Stage ◽  
Fuzzy Rule ◽  
Healthcare Sector ◽  
Diagnostic Process ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Early Diagnosis Of Diabetes

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

2022 ◽  
Author(s):  
Zakaria Shams Siam ◽  
Rubyat Tasnuva Hasan ◽  
Hossain Ahamed ◽  
Samiya Kabir Youme ◽  
Soumik Sarker Anik ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Compartmental Model ◽  
Fuzzy Rule ◽  
Transmission Dynamics ◽  
Intervention Measures ◽  
Proposed Model ◽  
Total Death ◽  
Linguistic Intervention ◽  
Fuzzy Linguistic ◽  
Death Cases

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

scholarly journals A Fuzzy Based Model for Standardized Sustainability Assessment of Photovoltaic Cells

2018 ◽  
Vol 10 (12) ◽  
pp. 4787 ◽  
Author(s):  
Muath Bani Salim ◽  
Dervis Emre Demirocak ◽  
Nael Barakat
Keyword(s):  
Fuzzy Logic ◽  
Environmental Sustainability ◽  
Life Cycle Analysis ◽  
Sustainability Assessment ◽  
Fuzzy Logic Algorithm ◽  
Multiple Factors ◽  
Proposed Model ◽  
Pv Cell ◽  
Relevant Factors ◽  
New Discovery

In this paper, a new environmental sustainability indicator (ESI) is proposed to evaluate photovoltaic (PV) cells utilizing Life Cycle Analysis (LCA) principles. The proposed indicator is based on a model that employs a fuzzy logic algorithm to combine multiple factors, usually used in multiple LCAs, and produce results allowing a comprehensive interpretation of LCA phase sub-results leading to standardized comparisons of various PV cells. Such comparisons would be essential for policymakers and PV cell manufacturers and users, as they allow for fair assessment of the environmental sustainability of a particular type of PV with multiple factors. The output of the proposed model was tested and verified against published information on LCAs related to PV cells. A distinct feature of this fuzzy logic model is its expandability, allowing more factors to be included in the future, as desired by the users, or dictated by a new discovery. It also provides a platform that can be used to evaluate other families of products. Moreover, standardizing the comparison process helps in improving the sustainability of PV cells through targeting individual relevant factors for changes while tracking the combined final impact of these changes on the overall environmental sustainability of the PV cell.

scholarly journals Interpretability and Accuracy Analysis of Fuzzy Rule Based System Designed for Abalone

2020 ◽  
Vol 8 (5) ◽  
pp. 1335-1340
Keyword(s):  
Fuzzy Rule ◽  
Vital Role ◽  
Rule Based ◽  
Rule Based System ◽  
Proposed Model ◽  
Human Decision ◽  
Fuzzy Partitions ◽  
Accuracy Assessments ◽  
Improved Accuracy ◽  
Rule Based Systems

Fuzzy Rule Based Systems are playing vital role in the implementation of human decision making. The development of interpretable Fuzzy Rule Based Systems with improved accuracy is a crucial research aspect in fuzzy based systems. Mamdani type fuzzy rule based systems are used to implement the proposed model. In this manuscript a FRBS is implemented with Guaje Open-Access Java based software. The interpretability and accuracy assessments are recorded on the different experiments with various rule generation methods, like Fuzzy decision tree and Wang Mendel method. The results are found satisfactory and a trade-off is handled between interpretability and accuracy. The major concern of the experimentation is number and type of fuzzy partitions. K-means and Hierarchical Fuzzy Partitions are used in the experiments with three and five number of fuzzy partitions.

Objectification of the evaluation of a pharmacological test with alpha-adrenergic blocking agents during uroflowmetry

2021 ◽  
pp. 40-54
Author(s):  
Vadim Valerievich Danilov ◽  
Vitaliy Vadimovich Danilov ◽  
Danilov Valeriy Vadimovich Danilov Valeriy Vadimovich
Keyword(s):  
Fuzzy Logic ◽  
Urination Disorders ◽  
Infravesical Obstruction ◽  
Fuzzy Logic Algorithm ◽  
Non Invasive ◽  
Urethral Strictures ◽  
Logic Block ◽  
Proposed Model ◽  
Pharmacological Test ◽  
Adrenergic Blocking

The tactics of treating dysuric disorders are largely determined by the pathophysiological and morpho-clinical basis: infravesical obstruction, impaired bladder contractility, complex neurogenic urination disorders, etc. Among the diseases that most often cause infravesical obstruction in men, the most common pathologies are benign prostatic hyperplasia, prostate cancer, prostate sclerosis, obstructive processes of the bladder neck (contractures, fibrosis), urethral strictures of various etiologies. The use of a comprehensive urodynamic study makes it possible to differentiate the causes of urinary disorders. One of the most common and non-invasive methods used in the urologist’s clinical practice is uroflowmetry. The use of the fuzzy logic algorithm described in the article for making a decision on the presence of obstructive urination allows one to assess the urodynamic situation using the home uroflow monitoring technique. Analytical urodynamics in conjunction with the fuzzy logic block increases the accuracy of describing the examination results, and the introduction of the proposed model into the software simplifies the work with diagnostic urological equipment and increases the efficiency of the examination.

An Intelligent Operator for Genetic Fuzzy Rule Based System

2012 ◽  
pp. 173-185
Author(s):  
C. Rani ◽  
S. N. Deepa
Keyword(s):  
Fuzzy Rule ◽  
Bench Mark ◽  
Data Sets ◽  
Local Optima ◽  
Rule Based ◽  
Rule Based System ◽  
Usual Procedure ◽  
Best Value ◽  
Information Sharing Mechanism ◽  
Intelligent Information

This paper proposes a modified form of operator based on Particle Swarm Optimization (PSO) for designing Genetic Fuzzy Rule Based System (GFRBS). The usual procedure of velocity updating in PSO is modified by calculating the velocity using chromosome’s individual best value and global best value based on an updating probability without considering the inertia weight, old velocity and constriction factors. This kind of calculation brings intelligent information sharing mechanism and memory capability to Genetic Algorithm (GA) and can be easily implemented along with other genetic operators. The performance of the proposed operator is evaluated using ten publicly available bench mark data sets. Simulation results show that the proposed operator introduces new material into the population, thereby allows faster and more accurate convergence without struck into a local optima. Statistical analysis of the experimental results shows that the proposed operator produces a classifier model with minimum number of rules and higher classification accuracy.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

Predicting Job Performance with a Fuzzy Rule-Based System

2003 ◽  
Vol 02 (03) ◽  
pp. 425-444 ◽  
Author(s):  
J. Philip Craiger ◽  
Michael D. Coovert ◽  
Mark S. Teachout
Keyword(s):  
Job Performance ◽  
Fuzzy Rule ◽  
Data Sets ◽  
Behavioral Models ◽  
Classification Problems ◽  
Rule Based ◽  
Job Experience ◽  
Rule Based System ◽  
Us Air Force ◽  
And Performance

Classification problems affect all organizations. Important decisions affecting an organization's effectiveness include predicting the success of job applicants and the matching and assignment of individuals from a pool of applicants to available positions. In these situations, linear mathematical models are employed to optimize the allocation of an organization's human resources.Use of linear techniques may be problematic, however, when relationships between predictor and criterion are nonlinear. As an alternative, we developed a fuzzy associative memory (FAM: a rule-based system based on fuzzy sets and logic) and used it to derive predictive (classification) equations composed of measures of job experience and job performance. The data consisted of two job experience factors used to predict measures of job performance for four US Air Force job families. The results indicated a nonlinear relationship between experience and performance for three of the four data sets. The overall classification accuracy was similar for the two systems, although the FAM provided better classification for two of the jobs. We discuss the apparent nonlinear relationships between experience and performance, and the advantages and implications of using these systems to develop and describe behavioral models.

scholarly journals Log Message Anomaly Detection with Oversampling

2020 ◽  
Author(s):  
Amir Farzad ◽  
T. Aaron Gulliver
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Learning Algorithms ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Data Sets ◽  
Significant Challenge ◽  
Proposed Model

Imbalanced data is a significant challenge in classification with machine learning algorithms. This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced. In this paper, a model to generate text log messages is proposed which employs a SeqGAN network. An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network. The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird. Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.

scholarly journals Formulizing the Fuzzy Rule for Takagi-Sugeno Model in Network Traffic Control

2018 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Alhihi ◽  
Mohammad Reza Khosravi
Keyword(s):  
Fuzzy Logic ◽  
Communication Networks ◽  
Traffic Control ◽  
Engineering Application ◽  
Fuzzy Rule ◽  
Proposed Model ◽  
Simulation Based ◽  
Fuzzy Neural ◽  
Fuzzy Logic Theory ◽  
Takagi Sugeno

Background:Nowadays, fuzzy logic theory is a popular approach to control network variables in engineering problems such as computer and communication networking. In this research, we formulize a new fuzzy logic-based rule for an important engineering application,i.e., traffic control of communication networks.Method:In this regard, we propose a new formulization based on a well-known model of traffic control in the networks entitled Takagi-Sugeno. Towards this modeling, we use a typical Fuzzy Neural Network (FNN) with an optimizer based on Genetic Algorithm (GA).Conclusion:The simulation results of our new model clearly prove that the proposed model and its formulation are approximately according to a theoretically consumed model for the problem. In details, we suppose two arbitrary examples for the problem which have two different assumed solutions, and then, we try to resolve the problem for both conditions based on the model in which the simulations show relatively similar results for both simulation-based and theoretical results in both examples.

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