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 development of information technology has led to a significant increase in the share of
multimedia traffic in data networks. This has necessitated to solve the following information …

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the
enhanced variants of velocity-free particle swarm optimizer with proven defects and …

Introduction: In pattern recognition and data mining, feature selection is one of the most
crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify …

The paper describes the usage of self-learning Hierarchical LSTM technique for classifying
hatred and trolling contents in social media code-mixed data. The Hierarchical LSTM-based …

With recent advancements in data collection tools and the widespread use of intelligent
information systems, a huge amount of data streams with lots of redundant, irrelevant, and …

Grey wolf optimizer (GWO) is a new nature-inspired algorithm that simulates the predatory
behaviors of grey wolves in nature. The GWO mainly divides the whole hunting process into …

To observe the earth surface and its atmospheric interaction, various advanced optical and
radar sensors are utilized. This observation returns a huge amount of optical …

Feature selection is a real-world problem that finds a minimal feature subset from an original
feature set. A good feature selection method, in addition to selecting the most relevant …

The psychological health of a person plays an important role in their daily life activities. The
paper addresses depression issues with the machine learning model using facial …