In this study, a novel hybrid model for forecasting crude oil futures price time series is
proposed. The combination of Bidirectional long short-term memory network (BiLSTM) …

Econometricians deal with a tremendous amount of data to derive the relationships between
economic entities. When statistical techniques are applied to the economic data to …

The success of the Multi-Layer Perceptron Neural Network (MLP) relies on carefully
configuring its weights and biases to promising values. The gradient descent technique is …

Workload prediction is a fundamental task in edge data centers, which aims to accurately
estimate the workload to achieve an in-situ resource provisioning for workload execution. In …

Following the weight of beef cattle is of great importance to the producer. The activities of
nutrition, management, genetics, health and environment can benefit from the weight control …

Cardiovascular diseases (CVDs) are the primary cause of death. Every year, many people
die due to heart attacks. The electrocardiogram (ECG) signal plays a vital role in diagnosing …

Pose-dependent modal properties of the robotic milling system determine the milling
stability, which significantly affects the machining accuracy and surface quality. Experimental …

This study aims to forecast metal futures in commodity markets, including gold, silver,
copper, platinum, palladium, and aluminium, using different machine and deep learning …

Gold and silver are two of the most precious metals in great demand due to their industrial,
electrical, and decorative uses. They have gained high popularity in terms of investments …

The preparation of sintered NdFeB magnets is complex, time-consuming, and costly. Data-
driven machine learning methods can enhance the efficiency of material synthesis and …