Fuzzy-based Classification Research Articles

The Comprehensive Analysis of the Effect of Chinese Word Segmentation on Fuzzy-Based Classification Algorithms for Agricultural Questions

The Comprehensive Analysis of the Effect of Chinese Word Segmentation on Fuzzy-Based Classification Algorithms for Agricultural Questions

A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine.

The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers.

Optimal Unification of Static and Dynamic Features for Smartphone Security Analysis

Android Smartphones are proliferating extensively in the digital world due to their widespread applications in a myriad of fields. The increased popularity of the android platform entices malware developers to design malicious apps to achieve their malevolent intents. Also, static analysis approaches fail to detect run-time behaviors of malicious apps. To address these issues, an optimal unification of static and dynamic features for smartphone security analysis is proposed. The proposed solution exploits both static and dynamic features for generating a highly distinct unified feature vector using graph based cross-diffusion strategy. Further, a unified feature is subjected to the fuzzy-based classification model to distinguish benign and malicious applications. The suggested framework is extensively experimentally validated through both qualitative and quantitative analysis and results are compared with the existing solutions. Performance evaluation over benchmarked datasets from Google Play Store, Drebin, Androzoo, AMD, and CICMalDroid2020 revealed that the suggested solution outperforms state-of-the-art methods. We achieve average detection accuracy of 98.62% and F1 Score of 0.9916.

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

In this work, a novel bearing fault identification scheme making use of deep learning has been proposed. Initially, the raw vibration signal is passed through a time-varying filter based empirical mode decomposition (TVF-EMD) to obtain different modes. Filter parameters of TVF-EMD are optimized by a newly developed optimization algorithm i.e. ameliorated African vulture optimization algorithm The Kernel estimate for mutual information has been considered as the fitness index for the developed optimization algorithm. The mode having the least value of fitness index is known as a prominent mode from which sensitive features representing different bearing conditions are extracted. These extracted features help in preparing the data matrix which is further utilised to build fuzzy-based classification models. The results obtained revealed that the linguistic hedge neuro-fuzzy classifier obtained maximum performance with the least computational time. The comparison of the developed method has also been done with other classification models viz., KNN, SVM, ELM and random forest that revealed the superiority of the developed method.

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world’s economy. The variability in climate change, rapid urbanization and fast-growing socio-economic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment. Keywords : Machine Learning, Pluvial Flood, Risk, Fuzzy Rule-Based, Prediction DOI: 10.7176/CEIS/12-1-07 Publication date: January 31 st 2021

Fuzzy-based Classification of Vaporous Compound using Electronic Nose

The Fuzzy logic is a promising method in Artificial Intelligence. This is extensively employed to manage factors in the surroundings. The influence of fuzzy logic is demonstrated by evidence of making of proposed intelligent systems based on fuzzy logic methods e.g. pressure cooker, refrigerator, washing machine and so on. The intention of proposed work is to design an e-nose capable of detecting the distinct smell generated by various chemical vapours. These sensors of gas were used in this scheme. These readings from sensors passed to a fuzzy logic controller. The fuzzification process is taken by Mamdani style. The outcome of the proposed scheme is displayed at LCD display and matched to the outcome of simulation done using Matlab.

Hazardous Gas Detection using Gas Sensors Arrays and Fuzzy-Based Classification

This paper presents hazardous gas detection using gas sensors arrays and fuzzy-based classification. This research is an automation of hazardous gas detection using electronic nose. Gases surround us could either hazard or benefit our health. Gas detection is an important issue, as humans should not breathe in hazardous gases in order to maintain their health. Hence, there must be an indicator to show the hazardous level of certain gases so that people can avoid and minimize the impact on their health. In this paper, hazardous gas detection is implemented by using gas sensor arrays and fuzzy-based classification. A classification for the electronic nose (e-nose) is developed in order to classify gases and determine the level of hazard of gases. The results found that e-nose system is able to differentiate hazardous level of chosen gases which are LP gas and CO gas.

Object Detection and Fuzzy-Based Classification Using UAV Data

Object Detection and Fuzzy-Based Classification Using UAV Data

Object Detection and Fuzzy-Based Classification Using UAV Data

Object Detection and Fuzzy-Based Classification Using UAV Data

A combination of fuzzy positioned dental X-ray analysis model to presume the peculiar images

ABSTRACTDental X-ray segmentation is one of the most important and necessary steps to analyze the dental images for better medical diagnosis. The aim of this work is to develop an effective method for the prediction of normal and abnormal images using classification process. The hybrid fuzzy-based classification model (HFCM) includes the hybridization of Bayesian hidden morkov model and fuzzy classifier. Parameters such as accuracy, specificity, sensitivity, precision, and computational time are calculated to assess the robustness of the proposed approach.

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

A fuzzy-based classification strategy (FBCS) based on brain–computer interface

Brain–computer interface systems help paralyzed people to control devices such as a computer cursor, robotic limbs, wheelchairs, or spellers by only using their thoughts. Nowadays, electroencephalogram (EEG) signals are mostly used to detect activity of various actions within the brain as they provide rich information about brain’s electrical activity. However, EEG signal generates large amount of data which is usually difficult to interpret and classify. This paper introduces a new classification strategy based on EEG signals, which is called fuzzy-based classification strategy (FBCS). FBCS minimizes the classification time by perfectly extracting the effective features of the produced EEG signals based on a set of elected electrodes using semantic analysis, then taking the classification decision accordingly. FBCS uses feature reduction and electrode selection techniques to reduce the dimensionality of data to be classified, which also improves the classification accuracy. Experimental results have shown that FBCS outperforms recent classification strategies in terms of accuracy and classification time.

UPDATING LIDAR DSM USING HIGH RESOLUTION STEREO-BASED DSM FROM WORLDVIEW-2

Abstract. In recent years, the acquisition and processing techniques of high resolution Digital Surface Models (DSM) have been rapidly improved. Airborne LiDAR production as a well-known and high quality DSM is still unbeatable in elevation accuracy and highly produced dense point clouds. In this paper, the objective is to update an old but high quality DSM produced by LiDAR data using a DSM generated from high resolution stereo satellite images. A classification-base algorithm is proposed to extract building changes between DSMs in two epochs. For image classification procedure, the DSM and Worldview-2 orthorectified images have been used as input data for a fuzzy-based classification method. Then, extracted buildings are classified into unchanged, destroyed, new, and changed classes. In this study a dataset related to Munich city, has been utilized to test the experimental investigation. The implemented qualitative and quantitative assessments demonstrate high quality as well as high feasibility of the proposed approach.

Fuzzy-Based Classification of Breast Lesions Using Ultrasound Echography and Elastography

Common breast lesions have different elasticity properties. Segmentation of contours of breast lesions from elastography and B mode images by incorporating variational level set method is involved in the proposed work. After segmentation, strain and shape features, such as differences in area, perimeter, and contour and width to height difference and solidity, as well as texture features like contrast, entropy, standard deviation, dissimilarity, homogeneity and energy, are estimated. A nonlinear fuzzy inference system is applied for classifying the breast lesions as benign cyst, benign solid mass, or malignant solid mass. Detection of malignant solid masses is our primary objective. A classification accuracy of 83% is obtained. One hundred percent sensitivity is reported. It can be concluded that the proposed fuzzy-based classification technique can be used as an aid for the automated detection of breast lesions.

Intention Reading from a Fuzzy-Based Human Engagement Model and Behavioural Features

This paper presents a novel approach for a quantitative appraisal model to identify human intent so as to interact with a robot and determine an engagement level. To efficiently select an attention target for communication in multi-person interactions, we propose a fuzzy-based classification algorithm which is developed by an incremental learning procedure and which facilitates a multi-dimensional pattern analysis for ambiguous human behaviours. From acquired participants' non-verbal behaviour patterns, we extract the dominant feature data, analyse the generality of the model and verify the effectiveness for proper and prompt gaze behaviour. The proposed model works successfully in multiple people interactions.

Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1

In this study, the development of an adaptive neuro-fuzzy classifier (ANFC) is proposed by using linguistic hedges (LHs). The LHs that are constituted by the power of fuzzy sets introduce the importance of the fuzzy sets for fuzzy rules. They can also change the primary meaning of fuzzy membership functions to secondary meaning. To improve the meaning of fuzzy rules and classification accuracy, a layer, which defines the adaptive linguistic hedges, is added into the proposed classifier network. The LHs are trained with other network parameters by scaled conjugate gradient (SCG) training algorithm. The tuned LH values of fuzzy sets improve the flexibility of fuzzy sets, this property of LH can improve the distinguishability rates of overlapped classes. The new classifier is compared with the other classifiers for different classification problems. The empirical results indicate that the recognition rates of the new classifier are better than the other fuzzy-based classification methods with less fuzzy rules.