The circumstances and factors which determine the volcanic explosive ejection are unknown, and currently, there is no effective way to determine the end of a volcanic explosive ejection. At present, the end of an eruption is determined by either generalized standards or the measurement which is unique to the volcano. We investigate the use of controlled machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Gaussian Process Classifiers (GPC), and create a decisiveness index D to assess the uniformity of the groups provided by these machine learning models. We analyzed the measured end-date obtained by seismic information categorization is two to four months later than the end-dates determined by the earliest instance of visible eruption for both volcanic systems. Likewise, the measurement systems, measurement technology becomes key elements in the seismic data analysis. The findings are consistent across models and correspond to previous, broad definitions of ejection. Obtained classifications demonstrate a more significant relationship between eruptive movement and visual activity than information base records of ejection start and completion timings. Our research has presented a new measurement-based categorization technique for studying volcanic eruptions, which provides a reliable tool for determining whether or not an emission has stopped without the need for visual confirmation.

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<p class="Abstract">The world has come to a standstill with the Coronavirus taking over. In these dire times, there are fewer doctors and more patients and hence, treatment is becoming more and more difficult and expensive. In recent times, Computer Science, Machine Intelligence, measurement technology has made a lot of progress in the field of Medical Science hence aiding the automation of a lot of medical activities. One area of progress in this regard is the automation of the process of detection of respiratory diseases (such as COVID-19). There have been many Convolutional Neural Network (CNN) architectures and approaches that have been proposed for Chest X-Ray Classification. But a big problem still remains and that is the minimal availability of Medical X-Ray Images due to improper measurements. Due to this minimal availability of Chest X-Ray data, most CNN classifiers do not get trained to an optimal level and the required standards for automating the process are not met. In order to overcome this problem, we propose a new deep learning based approach for accurate measurements of physiological data.</p>