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The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

This study applies a discourse analysis framework to explore the portrayal of women in Maysloon Hadi’s novel (The Black Eyes) (2011), using Critical Discourse Analysis (CDA) and Norman Fairclough’s tri-dimensional model (1989) as the analytical foundation. It investigates the roles and challenges women face in the novel. While there is growing interest in the portrayal of women in literature, Iraqi literature—especially from the perspective of Iraqi women writers remains underexplored. Hadi’s *The Black Eyes* provides a unique case to examine this intersection. Despite the novel’s rich narrative, which offers insight into Iraqi women’s lives, there is a lack of comprehensive CDA to understand how its language constructs

Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le

Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

Signature verification involves vague situations in which a signature could resemble many reference samples or might differ because of handwriting variances. By presenting the features and similarity score of signatures from the matching algorithm as fuzzy sets and capturing the degrees of membership, non-membership, and indeterminacy, a neutrosophic engine can significantly contribute to signature verification by addressing the inherent uncertainties and ambiguities present in signatures. But type-1 neutrosophic logic gives these membership functions fixed values, which could not adequately capture the various degrees of uncertainty in the characteristics of signatures. Type-1 neutrosophic representation is also unable to adjust to various

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall

One of the crucial public health problems worldwide is the urinary tract infections (UTIs) that are derived from uropathogenic bacteria (UPBs). Slime layer is known to have the ability to permit bacteria to achieve smooth surfaces attachment like catheters and prosthetic implants which in turn, facilitate biofilm formation and may cause lethal infections. On the other hand, Extended-spectrum beta-lactamase (ESBL) production is considered a growing concern among UPBs due to the limiting of the treatment options and contributes to resistance toward antibiotics. The principal study's point is the finding out the slime layer and ESBL production in Escherichia coli and Klebsiella pneumoniae of uropathogenic origin. Ten ready isolates (five isola