Rapid advancements in deep learning-based technologies have developed several
synthetic video and audio generation methods producing incredibly hyper-realistic …

Solar activity has significant impacts on human activities and health. One most commonly
used measure of solar activity is the sunspot number. This paper compares three important …

Regresi linier berganda digunakan untuk mengidentifikasi hubungan antara variabel
respons dengan minimal dua variabel prediktor. Variabel respons merupakan variabel yang …

A multi-layer, deep-learning (DL) architecture consisting of stacked Convolutional Long
Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number …

Effective predicting sunspot numbers (SSN) is the complex task of studying space weather,
solar activity, satellite communication, and Earth's climate. Developing a reliable SSN …

Predicting global solar activity is crucial for smart cities, especially for space activities,
communication industries, and climate change monitoring. The recently developed models …

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms.
In this work, we propose a novel combined model for sunspot prediction using improved …

Background: Ocean surface currents need to be monitored to minimize accidents at ship
crossings. One way to predict ocean currents—and estimate the danger level of the sea—is …

Sunspot number forecasting is a significant task for human beings in order to observe solar
activity, and it is a classical chaotic time series. To improve the forecasting accuracy, we …

Ekspor migas merupakan komoditas yang berperan penting dalam perekonomian negara
dan pengelolaannya harus dimaksimalkan demi kemakmuran dan kesejahteraan rakyat …