Abstract:
Pandemics and the current world situation affected overall world business. The maritime
industry currently depends on different vessel management systems that use Automatic
Identification System (AIS) messages to manage Ship activities. AIS messages include both Static
and Dynamic data, which contains information related to the position, Heading, and Ship features
as AIS data is in massive volume, so the possibility of missing data and anomalous information is
present. Different Machine Learning and deep learning algorithms are used to train and detect
anomalies to identify those anomalies related to ship classification and ship messages.
CCN, SVM, Random Forest and other deep learning algorithm are used to identify
anomalies regarding the position and ship flows. While SQL, PostGre are used for storage
purposes. The main goal is to identify which Database is best to store AIS data and which deep
learning algorithm performs betterto identify anomalies in Ship classification and transceiver class
data.
This thesis proposes a solution regarding the storage of AIS data to MongoDB database
and identifying anomalies in ship classification and Transceiver class using Deep Learning
Algorithm. This approach includes Long Short-Term Memory (LSTM), Convolution Neural
Network (CNN), and Artificial Neural Network (ANN) deep Learning algorithm for identifying
anomalies in Maritime Traffic Management System.
So, we can conclude that the CNN model provides the highest accuracy, approximately
81%, while other deep learning algorithms like LSTM have 72% and ANN have 79.95% accuracy.
The total time consumed to get 1658575 rows takes approximately 0.04sec using MongoDB cloud
Database
