Stock market predictions are a challenging problem due to the dynamic and complex nature
of financial data. This study proposes an approach that integrates the domain knowledge of …

The objective of this study is to develop a computationally efficient methodology for the
prediction of oil rate, water rate, and injection bottomhole pressure (BHP), and history …

This article presents a novel algorithm that accurately predicts market trends and identifies
trading entry points for US 30‐year Treasury bonds. The proposed method employs a hybrid …

Wind power forecasting involves data preprocessing and modeling. In pursuit of better
forecasting performance, most previous studies focused on creating various wind power …

To address the complex maneuvering characteristics of hypersonic targets in adjacent
space, this paper proposes an LSTM trajectory estimation method combined with the …

There's a pressing need to democratise DL algorithms while leveraging their performance.
This chapter proposes a customisable and efficient Automated Machine Learning (AutoML) …

In the dynamic realm of time series analysis and forecasting, the pursuit of more precise and
efficient models persists as a fundamental objective. This research contributes by presenting …

We are delighted to present the proceedings of the 1st International Conference on
Computation of Artificial Intelligence & Machine Learning (ICCAIML 2024), held on 18th and …

Extreme weather events have a tremendous influence on global and national economy,
affecting food harvests, human lives, and ecosystems. The current research uses a novel …

A recurrent neural network model, a database designed for neural network training, and a
software tool for interacting with a bot have all been created. A large dataset (50 thousand …