International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Parallel clustering serves as a platform for handling big data. The literature displays a number of clustering algorithms using a map-reduce framework, but they did not assure the effective clusters such that knowledge extraction becomes tough. With the aim to render a better and effective data clustering method to analyze the big data arriving from distributed systems, this paper uses a new clustering method. The proposed method named as BatDolphin-based Sparse Fuzzy C-Means (BatDol-Sparse FCM) clustering algorithm is proposed that paves way for the optimal selection of the cluster centroids. The distributed big data is managed using the Map-Reduce framework that is inbuilt with the BatDolphin-based Sparse Fuzzy C-Means algorithm such that the local and global clustering is executed. The implementation of the proposed BatDol-Sparse FCM algorithm is done by using data from the E-khool Learning Management System (LMS) and three medical datasets from the UCI repository. The analysis is performed using the metrics, such as clustering accuracy (CA) and dice coefficient (DC). From the simulation results, it is evident that the proposed parallel clustering scheme provided better results than the existing algorithms with the values of 0.96 and 0.9667 for CA and DC respectively.

In this paper, for sparse multi-label data, based on inter-instance relations and inter-label correlation, a Sparse Multi-Label Kernel Gaussian Neural Network (SMLKGNN) framework is proposed. Double insurance for the sparse multi-label datasets is constructed with bidirectional relations such as inter-instance and inter-label. When instance features or label sets are too sparse to be extracted effectively, we argument that the inter-instance relations and inter-label correlation can supplement and deduce the relevant information. Meanwhile, to enhance the explainable of neural network, Gaussian process is adopted to simulate the real underlying distribution of multi-label dataset. Besides, this paper also considers that contributions of different features have different effects on the experimental results, thus self-attention is leveraged to balance various features. Finally, the applicability of the algorithm is verified in three sparse datasets, and the generalization performance is also validated in three groups of benchmark datasets.

In this paper we study some geometrical properties of the polytope of 3-tolerant fuzzy measures. To achieve this task, we profit that this polytope is an order polytope and hence many properties can be extracted from the subjacent poset. The main result in the paper is a straightforward procedure for obtaining a random 3-tolerant fuzzy measure. We also compute the volume and obtain some other properties of this polytope. These results can be also applied by duality to the polytope of 3-intolerant measures and they can also be easily extended to other subfamilies of fuzzy measures.

Recently, some authors studied the distributive equations for continuous t-norms and some families of usual classes of uninorms over overlap functions in Refs. 25 and 32, but lacked a complete characterization of the distributivity on idempotent uninorms and overlap or grouping functions widely used in image processing. As a supplement to the previous results, in this article we fully characterize the distributivity equations of idempotent uninorms over these two functions by virtue of the associated functions of idempotent uninorms. Moreover, we also discuss the distributivity conditions of above two special functions over idempotent uninorms, yet find in this case that the associated functions of idempotent uninorms must satisfy particular conditions and those two functions usually are a class of special aggregation functions with a constant domain whose value equals to the neutral element of the idempotent uninorm.

In this paper, we study uni-nullnorms and null-uninorms on bounded lattices, which are generalizations of uninorms, nullnorms, t-norms and t-conorms. We construct two uni-nullnorms and two null-uninorms on a bounded lattice $L$. We determine the smallest-greatest uni-nullnorms with 2-neutral element ${\{e,1\}}_a$ and the smallest-greatest null-uninorms with 2-neutral element ${\{0,e\}}_a$ by using these construction methods.

Clustering, as one of the main tasks of machine learning, is also the core work of granular computing, namely granulation. Most of the recent granular computing based clustering algorithms only utilize the plain granule features without taking the granule structure into account, especially in information area with widespread application of algebraic structure. This paper aims at proposing an algebraic structure based clustering method from granular computing prospective. Specifically, the algebraic structure based granularity is firstly formulated based on the granule structure of an algebraic binary operator. An algebraic structure based clustering method is then proposed by incorporating congruence partitioning granules and homomorphically projecting granule structure. Finally, proof of the lattice at multiple hierarchical levels and comparative analysis of experimental cases validate the effectiveness of the proposed clustering method. The algebraic structure based clustering method can provide a general framework to perform granularity clustering using the algebraic granule structure information. It meanwhile advances the granular computing methods by combing the granular computing theory and the clustering theory.

Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief Network (BSDHO-based DBN) algorithm based on spark architecture that follows the master and slave nodes. The proposed BSDHO is obtained by combining Deer Hunting Optimization algorithm and Bird Swarm Algorithm. The developed model poses two nodes, namely slave and master node. The training data is initially given to the master node in the spark architecture to perform transformation of data. Here, the transformation of data is done with an exponential log kernel, and then selection of feature is done with sequential forward selecting for choosing suitable features for enhanced processing. Consequently, oversampling process is performed with Fuzzy K-Nearest Neighbor (Fuzzy KNN) in the slave node using selected features to manage imbalance data. Then, in master node, classification is done with Deep belief Network, and trained using developed Bird swarm Deer Hunting Optimization (BSDHO) algorithm. On the other hand, the test data is taken as input, and is fed to the slave node to perform data transformation. Then, the transformed data is given to the master node for classification based on the proposed BSDHO. At last, the training data and testing data output produced the classified output. The proposed BSDHO-based DBN provided enhanced outcomes with highest specificity of 97.92%, accuracy of 96.92%, and sensitivity of 96.9%.

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach’s effectiveness.