In the era of space exploration, coronal holes on the sun play a significant role due to their impact on satellites and aircraft through their open magnetic fields and increased solar wind emissions. This study employs computer vision techniques to detect coronal hole regions and estimate their sizes using imagery from the Solar Dynamics Observatory (SDO). Additionally, we utilize deep learning methods, specifically Long Short-Term Memory (LSTM) networks, to analyze trends in the area of coronal holes and predict their areas across various solar regions over a span of seven days. By examining time series data, we aim to identify patterns in coronal hole behavior and understand their potential effects on space weather. This research enhances our ability to anticipate and prepare for space weather events that could affect Earth's technological systems.

The detection and analysis of the solar coronal holes (CHs) is an important field of study in the domain of solar physics. Mainly, it is required for the proper prediction of the geomagnetic storms which directly or indirectly affect various space and ground-based systems. For the detection of CHs till date, the solar scientist depends on manual hand-drawn approaches. However, with the advancement of image processing technologies, some automated image segmentation methods have been used for the detection of CHs. In-spite of this, fast and accurate detection of CHs are till a major issues. Here in this work, a novel quantum computing-based fast fuzzy c-mean technique has been developed for fast detection of the CHs region. The task has been carried out in two stages, in first stage the solar image has been segmented using a quantum computing based fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted out from the segmented image based on image morphological operation. In the work, quantum computing has been used to optimize the cost function of the fast fuzzy c-mean (FFCM) algorithm, where quantum approximate optimization algorithm (QAOA) has been used to optimize the quadratic part of the cost function. The proposed method has been tested for 193 \AA{} SDO/AIA full-disk solar image datasets and has been compared with the existing techniques. The outcome shows the comparable performance of the proposed method with the existing one within a very lesser time.

In the era of space exploration, the implications of space weather have become increasingly evident. Central to this is the phenomenon of coronal holes, which can significantly influence the functioning of satellites and aircraft. These coronal holes, present on the sun, are distinguished by their open magnetic field lines and comparatively cooler temperatures, leading to the emission of solar winds at heightened rates. To anticipate the effects of these coronal holes on Earth, our study harnesses computer vision to pinpoint the coronal hole regions and estimate their dimensions using imagery from the Solar Dynamics Observatory (SDO). Further, we deploy deep learning methodologies, specifically the Long Short-Term Memory (LSTM) approach, to analyze the trends in the data related to the area of the coronal holes and predict their dimensions across various solar regions over a span of seven days. By evaluating the time series data concerning the area of the coronal holes, our research seeks to uncover patterns in the behavior of coronal holes and comprehend their potential influence on space weather occurrences. This investigation marks a pivotal stride towards bolstering our capacity to anticipate and brace for space weather events that could have ramifications for Earth and its technological apparatuses.

Artificial intelligence can be used to detect coronal holes in the Sun's upper atmosphere, an international research team has shown. Robert Jarolim at the University of Graz in Austria, Tatiana Podladchikova at Skoltech in Russia and colleagues have demonstrated a strong agreement between the holes identified by their convolutional neural network, and those picked up manually by astronomers. The system could lead to more reliable forecasting of disruptive space weather and an improved understanding of the Sun's complex evolution. When observed at extreme ultraviolet (EUV) wavelengths, holes can appear in the Sun's corona – its upper atmosphere. These holes are cooler and less dense than surrounding material in the corona and comprise many smaller-scale magnetic funnels.

Artificial intelligence (AI) now has the capabilities to predict space weather that is caused by our Sun accurately. Researchers from the University of Graz have created a new neural network that allows for artificial intelligence to reliably predict changes in the Sun's coronal holes from space-based observations. As you already know, the light emitted from the Sun plays a vital role in our existence here on Earth. Additionally, the light from the Sun interacting with Earth's magnetic field can influence our electronics, and in extreme cases, when the Sun blasts Earth with too many charged particles, our electricity grids can be temporarily knocked offline by geomagnetic storms. Now, the researchers have developed a new neural network that examines some of the dark regions on the Sun called coronal holes.

Autonomous technology is touted to be the future of driving. Experts in the field claim it could be safer and more efficient than having humans behind the wheel – but, it might prove no match for the forces of nature. This week's solar storm has served as a reminder that driverless cars have their limitations, too. Scientists say driverless cars, if designed in a way that's too reliant on GPS, may suffer complications during powerful space weather events, making it difficult to carry out their functions as intended. Experts this week warned that a minor storm generated by holes (white arrows in this August 2017 Nasa image) in the sun's outermost magnetic layer could cause'weak power grid fluctuations' and have a small'impact on satellite operations'.