A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data

The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibilistic Fuzzy C-Means (ImPFCM) by combining the PFCM algorithm with a very powerful statistical method. The properties of this new ImPFCM algorithm show that it is not only applicable on clusters of spherical shapes, but also on clusters of different sizes and densities. The results of the comparative study with very recent algorithms indicate the performance and the superiority of the proposed approach to easily group the datasets in a large-dimensional space and to use not only the Euclidean distance but more sophisticated standards norms, capable to deal with much more complicated problems. On the other hand, we have demonstrated that the ImPFCM algorithm is also capable of detecting the cluster center with high accuracy and performing satisfactorily in multiple environments with noisy data and outliers.

AUTOMATED SEGMENTATION OF COVID-19 REGIONS FROM LUNG CT IMAGES USING WATERSHED ALGORITHM AND CLASSIFICATION USING MACHINE LEARNING CLASSIFIERS

The COVID-19 pandemic originated in Wuhan, China in December 2019 and has since affected over 200 countries worldwide. The highly contagious Coronavirus primarily affects the respiratory system, causing pulmonary inflammation that can be visualized through medical imaging such as CT and X-rays. Conventional testing methods include PCR and antibody tests. Shortage of test kits in hospitals as well as time taken for results to be received can be compensated through medical imaging. Therefore, there is a need for an automated system, which is accurate and robust in detection of Covid-19 from medical radiographs for clinical practice. The objectives of our study are as follows: (i) To segment the lung CT images using a hybrid watershed and fuzzy c-means algorithm. (2) To extract various textural features using the GLCM algorithm. (iii) To implement machine learning classifiers for classification of COVID and non-COVID image classes. Our dataset consisting of 60 chest CT images of COVID-19 and non-COVID-19 patients was pre-processed and segmented using a hybrid watershed and fuzzy c-means algorithm. Then, textural features were extracted from the segmented ROI using the GLCM algorithm. Finally, the images were classified into COVID and non-COVID classes using three machine learning classifiers namely Naïve Bayes, SVM and K-star. Naïve Bayes classifier achieved the highest accuracy of 95%, while SVM achieved 93% accuracy. The ROC curves were also obtained, with AUC of 0.98. Thus, our proposed system has shown promising results in the classification of lung CT images into the two classes namely COVID and non-COVID.

A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

MRI Brain Abnormality Detection Using Conventional Neural Network (CNN)

Brain tumours has huge heterogeneity and there is always a familiarity between normal and abnormal tissues and hence the extraction of tumour portions from normal images becomes persistent. In this paper, MRI brain tumor detection is performed from a brain images using Fuzzy C-means(FCM) algorithm and sebsequently Convolutional Neural Network(CNN) algorithm is employed. Here, firstly preprocessing step is performed by Skull Stripping algorithm followed by Segmentation process. Fuzzy C-means algorithm is used to segment the Cerebrospinal Fluid(CSF), Grey matter(GM) and White Matter(WM) from the database. The third part is to extract features to find whether the tumor is present or not, here eleven features are extracted like mean, entropy, S.D(Standard Deviation). The final part is the classification process done by Convolutional Neural Network(CNN) in which it is able to differentiate whether the input image is normal image or an abnormal image. Compared to other methods, here the values of the features extracted are higher for normal images than for abnormal Images and it is shown from the graphs drawn from the extracted features.

Analisa Data Penerimaan Siswa Pada Perguruan Tinggi melalui Jalur SNMPTN menggunakan Algoritma Fuzzy C-Means (Studi Kasus : SMAN 5 Kota Bengkulu)

SMA Negeri 5 Bengkulu City is one of State Senior High Schools in Bengkulu City that has been accredited A with a total of 58 educators and 23 administrative staff and employees. In addition, SMA Negeri 5 Bengkulu City has also won many achievements both in academics (graduates of SMA N 5 Bengkulu City are accepted at the best universities in Indonesia through test and non-test pathways) as well as in the fields of science (Olympics), Sports, IMTAQ and the arts for the provincial and national levels. Application of student admissions data in higher education through SNMPTN using Fuzzy C-Means Algorithm at SMA N 5 Bengkulu City is an application that can help analyze data grouping based on student admission data into 3 groups. Data Analysis of Student Admissions in higher education through SNMPTN was made using the Visual Basic.Net programming language and SQL Server 2008 database by applying the Fuzzy C-Means algorithm. This application is able to provide information on the results of the analysis of student admissions at Higher Education through SNMPTN. The more data on student admissions in Higher Education, the more accurate the grouping results. Based on the results of the tests that have been carried out, the Application of Student Admission Data in Higher Education through SNMPTN can provide information on the results of data grouping divided into 3 groups, namely high, medium and low