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TLR

TLR is an easy USER focused app, to allow them to report crimes on the spot, and get incentivised for that. Build over Blockchain and Machine Learning it leverages the crime reporting facility.

The problem TLR solves

TLR enables the users to report crime anonymously on the spot. Based on the voting procedure and helpfulness of the report, the reporter is incentivised in the form of TLR Tokens automatically through Smart Contracts. The system constitutes of state of the art- Blockchain and ML Technologies which solves unnecessary spam-reports and provide simplification and stemming.
The map element in the web app provides the view of the city crimes and its severity at specific places, so that police, as well as the users, gets attentive towards that place. The system holds transaction over Matic Network, which makes the transactions very fast. For real-time- updates of events in the map, it uses Matic Dagger to capture the BLockchain Events in real-time.

Challenges we ran into

Unavailability of Machine Learning Data-Sets
Integration of Matic and Dagger for the first time, in a complex application.
Segregation of tasks and collaborating by taking charge of very specific and different modules.
Decoding the transaction inputs in its original form, but figured it out in the end.

Discussion