To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking of 30.66 x 30.66-degree patches in the vicinity of active regions allowed us to trace the evolution of active regions starting from the pre-emergence state. We have developed a machine learning model to capture the acoustic power flux density variations associated with upcoming magnetic flux emergence. The trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead whether, in a given area of the solar surface, continuum intensity values will decrease. The performed study allows us to investigate the potential of the machine learning approach to predict the emergence of active regions using acoustic power maps as input.

FAST will not perform well where detection of multiple features has to be performed in same region of an image. For this Non-Maximum Suppression is used. In Non-Maximum Suppression a score function is computed, V for all the detected feature points. In a nut shell, FAST is faster than many existing feature detectors but performs poorly in presence of high level of noise. Mainly because the pixel values will be altered because of high-level of noise. Opencv documentation mentions two feature matching methods.

In this work, we explore the impact of inter-agent variation in intensity of effort on the ability of a swarm of artificial agents to achieve a goal. Variation in intensity models biological phenomena such as individual differences in size and strength and increased adeptness for a task due to experience. Focusing on experience, we implement inter-agent variation in intensity, with dynamic values that increase and decrease with an agent's activation or non-activation for a task. Examining intensity variation alone and in combination with activation threshold variation, we find that the desynchronizing effects of variation in thresholds in concert with the increase in agent efficiency due to experience with a task, dramatically improves the swarm's goal achievement.