Sk Shahnawaz

Samayan Bhattacharya, Sk Shahnawaz

Predicting disaster events from seismic data is of paramount importance and can save thousands of lives, especially in earthquake-prone areas and habitations around volcanic craters. The drastic rise in the number of seismic monitoring stations in recent years has allowed the collection of a huge quantity of data, outpacing the capacity of seismologists. Due to the complex nature of the seismological data, it is often difficult for seismologists to detect subtle patterns with major implications. Machine learning algorithms have been demonstrated to be effective in classification and prediction tasks for seismic data. It has been widely known that some animals can sense disasters like earthquakes from seismic signals well before the disaster strikes. Mel spectrogram has been widely used for speech recognition as it scales the actual frequencies according to human hearing. In this paper, we propose a variant of the Mel spectrogram to scale the raw frequencies of seismic data to the hearing of such animals that can sense disasters from seismic signals. We are using a Computer vision algorithm along with clustering that allows for the classification of unlabelled seismic data.

Samayan Bhattacharya, Sk Shahnawaz

Atrial Fibrillation (AF) is the most common type of arrhythmia (Greek a-, loss + rhythmos, rhythm = loss of rhythm) leading to hospitalization in the United States. Though sometimes AF is asymptomatic, it increases the risk of stroke and heart failure in patients, in addition to lowering the health-related quality of life (HRQOL). AF-related care costs the healthcare system between $6.0 to $26 billion each year. Early detection of AF and clinical attention can help improve symptoms and HRQOL of the patient, as well as bring down the cost of care. However, the prevalent paradigm of AF detection depends on electrocardiogram (ECG) recorded at a single point in time and does not shed light on the relation of the symptoms with heart rhythm or AF. In the recent decade, due to the democratization of health monitors and the advent of high-performing computers, Machine Learning algorithms have been proven effective in identifying AF, from the ECG of patients. This paper provides an overview of the symptoms of AF, its diagnosis, and future prospects for research in the field.