scholarly journals SEGMENTASI PENYAKIT PADA CITRA DAUN TEBU MENGGUNAKAN FUZZY C MEANS – SUPPORT VECTOR MACHINE DENGAN FITUR WARNA a*

2015 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Mustika Mentari ◽  
Hari Ginardi ◽  
Chastine Fatichah
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Fuzzy C Means

scholarly journals Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

2020 ◽  
Vol 17 (15) ◽  
pp. 5497
Author(s):  
Khaled Assi ◽  
Syed Masiur Rahman ◽  
Umer Mansoor ◽  
Nedal Ratrout
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Injury Severity ◽  
Support Vector ◽  
Severity Level ◽  
Feed Forward ◽  
Fuzzy C Means ◽  
Severity Prediction ◽  
Crash Injury Severity

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means

2021 ◽  
pp. 1-17
Author(s):  
Ming-Ai Li ◽  
Ruo-Tu Wang ◽  
Li-Na Wei
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Joint Optimization ◽  
Cluster Center ◽  
Support Vector ◽  
Classification Problems ◽  
Fuzzy Support Vector Machine ◽  
Local Optima ◽  
Fuzzy C Means ◽  
Initial Cluster

BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima. OBJECTIVE: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM). METHOD: The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs. RESULTS: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively. CONCLUSION: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.

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