Depression is a common and recurring illness. It is a serious medical condition which might lead to self-harm. Various factors like unawareness about depression, shortage of medical health professionals and social stigmas make proper treatment for depression inaccessible. With the growing importance of technology in every sector of life, healthcare systems have also started using technology to provide better treatment. Various studies have shown that the widespread use of smartphones can be useful in predicting as well as treating depression by recommending activities. Smartphones can be very useful in the continuous monitoring of a patient which in turn helps to keep track of the activities of the patient. Social media data can also be used to find out the mental state of a patient. We propose EmoCure, a smartphone application that uses social media data, wearable sensor data, patient history, and smartphone usage patterns to predict, monitor, and treat depression using emotion regulating activities. We use machine learning models for finding out the sentiments in the social media posts. To predict depression, we use ensemble learning. We then recommend personalized emotion regulating activities whichever the user prefers.

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This paper discusses the possibility of extraction of Hydrogen from urine and using it as a fuel to power various machinery and automobiles which could run on hydrogen fuel. The paper also attempts to show a practical layout of various processes and stages which could facilitate the conversion of urea into hydrogen molecules which can be then stored in compressed chambers and subsequently distributed as required. The design is compact, user-friendly, sustainable and is mostly based on domestic use.