Aditya Mahendra Nathani
Selected for ASEAN-India Hackathon 2021
I am very proud to share with you all that I Mr. Aditya Mahendra Nathani (BE Computer), from SSBT’s COET ,Jalgaon (Maharashtra) is selected in ASEAN-INDIA Hackathon as one of the team member by Ministry of Education’s Innovation Cell, India. I will be representing India in competition.
ASEAN-India Hackathon is a joint Hackathon between India & Asean Countries. The ASEAN-INDIA Hackathon, an initiative by the Hon’ble Prime Minister of India Mr. Narendra Modi, is proposed to be a 36 hours International Hackathon which will offer unique opportunities to all 10 ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, Brunei, Laos, Myanmar, Cambodia and Vietnam) and India to forward their economic development through collaboration in education, science and technology, exchange of thoughtful leadership, work and collaboration on projects involving varied skilled individuals to develop cross country bonds and learn from each other’s strengths and also get acquainted with each other’s culture, values and work ethics.
Along with me the other members are from Singapore, Vietnam, Thailand and Philippines.
Team members :-
Aditya Mahendra Nathani (India)
Sai Mahidhar Vanumu (India)
Lê Đình Duy (Vietnam)
Yu Yue (Singapore)
Mykaela Nicole Biagtan Nillos (Phillipines)
Sekhaveayeam Hoeung (Thailand)
Problem Statement Given : Flagging of (AIS) INactivity Data (FIND)
Problem/Current Situation : Automatic Identification System (AIS) is a useful system that helps avoiding ship collision and allows managers for fleet management especially for emergencies e.g. adverse sea conditions. Vessels keep the AIS beacons on in order to stay safe. However, sometime the vessels do not transmit. The reasons for this may be unknown. As much as the AIS activity data are important, the inactivity data can also be put to use to understand the possible reasons behind such behavior and if needed, respond in the appropriate manner.
Solution : A highly trained AI model which predicts the reason behind the AIS inactivity of data. Prediction of Suspicious ships or abnormal behavior using Machine learning. Frontend to show target regions for a ship which has missing data-points producing latitude and longitude and predicting the label.
Technology used: ML-Python , Javascript, HTML, Google maps API .
