EARLYBUDDY is a smart alarm clock app for Android devices. It primarily uses traffic data to help the user wake up on time when there is an estimated delay. Users can choose a timeframe within which they must awaken rather than a single wake-up time. The app will then select a time inside that window based on the projected delay based on frequent traffic data updates. If it detects that the user will be late to their destination, it will change the existing alarm to wake them up. Users can also specify their morning habits, which are factored into the total delay estimate. These routines are optional, but they are encouraged because they help the user to organize their morning and provide more data for the application to use when calculating delays. All humans are not made equal when it comes to sleep. EARLYBUDDY features a provision that prevents users from turning off the phone to silence the alarm if they find it difficult to get up from bed in the morning. Users must tap the stop alarm or snooze button, which only appears in the app itself at the center of the screen, to turn off the alarm. If you press anyplace else on the screen or push the snooze button incorrectly, the alarm will ring again. The basic goal here is to actively wake up the body and mind before hitting the snooze button. Users can also place their phones on the mattress nextto them, and EARLYBUDDY will analyze their sleeping habits using the device’s sensors. This may be used to assess the quality of the user’s night’s sleep and set the alarm at the most natural time.

A Spoof news is a fraud content meant to misguide the reader about the event with ill motive. In this article a reactive technique using deep learning is proposed to deal with it effectively. Spoof news are innumerable in number over microblog twitter and have wide range of bad effects overall. This is causing chaos and hoax among the readers about the issue. They are getting mislead about the issue a lot. As of now automatic locators of fake news are ineffective and few in number. This emphasized us to come up with smart locator with deep learning mechanism. One way of dealing with this issue is to make “blacklist” of origins and composers of counterfeit news. Here we need to examine all irksome instances of origins and creators in gradual manner. To cater this need we came up with a classifier based on deep learning mechanism that studies linguistic, network account aspects of twitter news and then distinguishes them into spoof and legitimate ones. We set up a deep learning model that takes both legitimate and spoof news elements as input and learns by analyzing their constructs. Then do the binary classification of news effectively thus avoiding the user not to misled by fake.

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