Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

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Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network

Sensors ◽

10.3390/s18124211 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4211 ◽

Cited By ~ 20

Author(s):

Miao Gao ◽

Guoyou Shi ◽

Shuang Li

Keyword(s):

Neural Network ◽

Collision Avoidance ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Automatic Identification ◽

Identification System ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Ship Behavior

The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

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Automatic Identification of Drill Condition During Drilling Process in Standard Laminated Chipboard with the Use of Long Short-Term Memory (LSTM)

19th International Conference Computational Problems of Electrical Engineering ◽

10.1109/cpee.2018.8506809 ◽

2018 ◽

Author(s):

Jaroslaw Kurek ◽

Grzegorz Wieczorek ◽

Bartosz Swiderski ◽

Michal Kruk ◽

Albina Jegorowa ◽

...

Keyword(s):

Short Term Memory ◽

Automatic Identification ◽

Drilling Process ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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On Identifying Hashtags in Disaster Twitter Data

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5387 ◽

2020 ◽

Vol 34 (01) ◽

pp. 498-506 ◽

Cited By ~ 1

Author(s):

Jishnu Ray Chowdhury ◽

Cornelia Caragea ◽

Doina Caragea

Keyword(s):

Disaster Response ◽

Short Term Memory ◽

Automatic Identification ◽

Short Term ◽

Term Memory ◽

Learning Framework ◽

Twitter Data ◽

Long Short Term Memory ◽

Search For Information ◽

Unique Dataset

Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1

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Fuzzy Logic on Long Short-Term Memory for Smart Person-Identification System through Electroencephalogram

10.1109/ispcc53510.2021.9609504 ◽

2021 ◽

Author(s):

Subhrangshu Adhikary ◽

Kushal Jain ◽

Biswajit Saha ◽

Chandan Koner

Keyword(s):

Fuzzy Logic ◽

Short Term Memory ◽

Identification System ◽

Short Term ◽

Person Identification ◽

Term Memory ◽

Long Short Term Memory

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EEG-based user identification system using 1D-convolutional long short-term memory neural networks

Expert Systems with Applications ◽

10.1016/j.eswa.2019.01.080 ◽

2019 ◽

Vol 125 ◽

pp. 259-267 ◽

Cited By ~ 27

Author(s):

Yingnan Sun ◽

Frank P.-W. Lo ◽

Benny Lo

Keyword(s):

Neural Networks ◽

Short Term Memory ◽

Identification System ◽

Short Term ◽

User Identification ◽

Term Memory ◽

Long Short Term Memory

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Long Short-term Power Load Forecasting Algorithm using Long Short-Term Memory Neural Network with Density-Based Spatial Clustering

2019 IEEE 5th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc47050.2019.9064382 ◽

2019 ◽

Author(s):

Yuhui Luo ◽

Yanguang Cai ◽

Yuanhang Qi ◽

Houren Chen ◽

Shihao Wang

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Spatial Clustering ◽

Load Forecasting ◽

Short Term ◽

Term Memory ◽

Power Load ◽

Power Load Forecasting ◽

Long Short Term Memory

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Fake News Classification: A Quantitative Research Description

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3447650 ◽

2022 ◽

Vol 21 (1) ◽

pp. 1-17

Author(s):

Rachna Jain ◽

Deepak Kumar Jain ◽

Dharana ◽

Nitika Sharma

Keyword(s):

Deep Learning ◽

Random Forest ◽

Quantitative Research ◽

English Language ◽

Short Term Memory ◽

Automatic Identification ◽

Fake News ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Social media can render content circulating to reach millions with a knack to influence people, despite the questionable authencity of the facts. Internet sources are the most convenient and easy approach to obtain any information these days. Fake news has become the topic of interest for academicians and the rest of society. This kind of propaganda has the power to influence the general perception, offering political groups the ability to control the results of democratic affairs such as elections. Automatic identification of fake news has emerged as one of the significant problems due to the high risks involved. It is challenging in a way because of the complexity levels of accurately interpreting the data. An extensive search has already been performed on English language news data. Our work presents a comparative analysis of fake news classifiers on the low resource Bengali language ‘ban fake news’ dataset from Kaggle. The analysis presented compares deep learning techniques such as LSTM (Long short-term Memory) and BiLSTM (Bi-directional Long short-term Memory) and machine learning methods like Naive Bayes, Passive Aggressive Classifier (PAC), and Random Forest. The comparison has been drawn based on classification metrics such as accuracy, precision, recall, and F1 score. The deep learning method BiLSTM shows 55.92% accuracy while Random Forest, in contrast, has outperformed all the other methods with an accuracy of 62.37%. The work presented in this paper sets a basis for researchers to select the optimum classifiers for their approach towards fake news detection.

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