Advanced music classification using a combination of capsule neural network by upgraded ideal gas molecular movement algorithm.

Music genres classification has long been a challenging task in the field of Music Information Retrieval (MIR) due to the intricate and diverse nature of musical content. Traditional methods have struggled to accurately capture the complex patterns that differentiate one genre from another. However, recent advancements in deep learning have presented new opportunities to tackle this challenge.

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems.

Hybrid approach for accurate water demand prediction using socio-economic and climatic factors with ELM optimization.

This study proposes a hybrid approach for accurately predicting water demand by integrating socio-economic variables, such as population and GDP (per capita), with climatic variables, including temperature and precipitation. The prediction model utilizes an Extreme Learning Machine (ELM), effectively capturing the dynamic relationships between the input variables and water demand. The Improved Ant Nesting Algorithm is employed to fine-tune the weights and biases to optimize the network's performance.