Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …

The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …

Meta heuristics is an optimization approach that works as an intelligent technique to solve
optimization problems. Evolutionary algorithms, human-based algorithms, physics-based …

Network function virtualization (NFV) is a new networking paradigm, which replaces the
specific-purpose hardware appliances with software virtualization to perform network …

The Proposed work intends to automate the detection and classification of diabetic
retinopathy from retinal fundus image which is very important in ophthalmology. Most of the …

This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …

In the realm of modern medicine and biology, vast amounts of genetic data with high
complexity are available. However, dealing with such high-dimensional data poses …

Background: The size of genomics data has been growing rapidly over the last decade.
However, the conventional data analysis techniques are incapable of processing this huge …

The rapid development of e-commerce has resulted in optimization of the industrial structure
of Chinese enterprises and has improved the Chinese economy. E-commerce transaction …

Alzheimer's Disease (AD) is a neurological disorder that destroys memory and other
significant mental functions. One of the most accurate methods to identify the disease …