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We present our preliminary findings as part of a coronal condensations and bright spots, are common new data mining application aiming at the automatic detection around the time of solar maximum (the sun goes through of images with coronal loops from one of NASA's solar image a cycle of activity every 11 years), while larger faint databases, known as EIT. Coronal loops are immense arches of hot gas on the surface of the Sun, thought to be jets of hot onest last d solr weeks a ty pical o e plasma flowing along in the alleys between the strong coronal quiet corona, when solar activity is low. The two ends magnetic fields. We use various data mining techniques including of a loop, known as footprints, lie in regions of the combining crisp and fuzzy classifiers for automated detection of photosphere of opposite magnetic polarity to each other. blocks extracted from EIT solar images. Our data mining and Coronal loops have been linked to complex phenomena retrieval system helps provide relevant data to astrophysicists who need such data to study the solar corona, and whose work onates sn, such asvolent corona mas rejtions, solar is traditionally hindered by the need to manually sift through flares, and solar storms, that affect the rest of the solar thousands of images in order to locate the very few that are useful system, by shaking the Earth's magnetic field and power for further analysis. Our data-driven approach is distinct from grids, and possibly harming satellites and astronomers related image processing based approaches that cannot scale to in space. Measurements of the temperature distribution large image databases because they rely mostly on semi-automated along the loop length can be used to support or eliminate detection and on heavy and computationally intensive local shape analysis.'234 various classes of coronal temperature models [7]. In order to make progress, scientific analysis requires data observed by instruments such as EIT, TRACE, and I. INTRODUCTION SXT [7]. The combination of EIT, TRACE, and SXT The Coronal Heating Problem [8,9] is one of the information provides a powerful data set that will yield longest standing unsolved mysteries in astrophysics, and unprecedented detail on the plasma parameters of a is essentially concerned with understanding and model-variety of coronal loop structures. The biggest obstacle ing the exact properties of temperature distribution along to completing studies of the solar loop properties has coronal loops. The corona is the uppermost level of the been putting the data set together, so that the subseSun's (or another star's) atmosphere, lying immediately quent scientific analysis is performed. The search for above its visible surface. Coronal loops (see Figure 1) interesting images (the ones with coronal loops) is by far are immense arches of hot gas on the surface of the the most time consuming aspect of this scientific task, Sun, thought to be jets of hot plasma flowing along in amounting to a "search for a needle in a haystack". In the alleys between the st...

The prominent rise of social networks within the past decade have become a gold mine for data mining operations seeking to model the real world through these virtual worlds. One of the most important applications that has been proposed is utilizing information generated from social networks as a supplemental health surveillance system to monitor disease epidemics. At the time this research was conducted in 2020, the COVID-19 virus had evolved into a global pandemic, forcing many countries to implement preventative measures to halt its expanse. Health surveillance has been a powerful tool in placing further preventative measures, however it is not a perfect system, and slowly collected, misidentified information can prove detrimental to these efforts. This research proposes a new potential surveillance avenue through unsupervised machine learning using dynamic, evolutionary variants of clustering algorithms DBSCAN and the Louvain method to allow for community detection in temporal networks. This technique is paired with geographical data collected directly from the social media Twitter, to create an effective and accurate health surveillance system that grows as time passes. The experimental results show that the proposed system is promising and has the potential to be an advancement on current machine learning health surveillance techniques.

Fiber Reinforced Polymer (FRP) usage to wrap reinforced concrete (RC) structures has become a popular technology. Most studies about RC columns wrapped with FRP in literature ignored the internal steel reinforcement. This paper aims to develop a model for the axial compressive strength and axial strain for FRP confined concrete columns with internal steel reinforcement. The impact of FRP, Transverse, and longitudinal reinforcement is studied. Two non-destructive analysis methods are explored: Artificial Neural Networks (ANNs) and Regression Analysis (RA). The database used in the analysis contains the experimental results of sixty-four concrete columns under the compressive concentric load available in the literature. The results show that both models can predict the column's compressive stress and strain reasonably with low error and high accuracy. FRP has the highest effect on the confined compressive stress and strain compared to other materials. While the longitudinal steel actively contributes to the compressive strength, and the transverse steel actively contributes to the compressive strain.