Data clustering is an unsupervised learning task that has been extensively studied, given its
wide applicability in various domains. Traditional algorithms often struggle to achieve a …

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known
properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog …

The present examination introduces a promising approach for efficiently predicting power
consumption using empirical data from a well-established related study. Utilizing support …

Communication infrastructure is damaged by disasters and it is difficult to support
communication services in affected areas. UAVs play an important role in the emergency …

Feature selection is important in many machine learning applications. Our results
demonstrate that it is not necessary to use all features of a dataset to perform classification …

This study investigates the interruption identification problem for organization safe havens;
the main goal is to classify network behavior as normal or abnormal while minimizing …

In this analysis, Optimization of Hybrid Grey Wolf technique for Feature Selection analysis is
implemented. In many areas standard performance is given by Grey Wolf Optimizer's (GWO) …

Diabetic Retinopathy has been found to be the leading cause of sight impairment in most
parts of the globe, particularly in diabetic patients. An early detection of DR in retinal images …

Present work involves the use of deep learning models, in particular, Convolutional Neural
Networks, to detect critical diseases of the lungs such as pneumonia, tuberculosis, and lung …

The mainly prevailing and deadly illness, brain tumors contain an extremely low maximum
endurance rate. Thus, one of the most important steps in raising patients' standard of …