A Point-Based Ship Route Prediction Algorithm based on Multiclass Classification

2021 ◽  
Author(s):  
Angelica Lo Duca
Keyword(s):  
Multiclass Classification ◽  
Prediction Algorithm ◽  
Route Prediction ◽  
Ship Route

Automatic Vehicle Route Prediction based on Multi-Sensor Fusion

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Huajie Xu ◽  
Baolin Feng ◽  
Yong Peng
Keyword(s):  
Sensor Fusion ◽  
Prediction Algorithm ◽  
Motion Model ◽  
Optimal Motion ◽  
Fusion Algorithm ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Vehicle Route ◽  
Route Prediction ◽  
Basic Probability

To solve the problem of inaccurate results of vehicle routing prediction caused by a large number of uncertain information collected by different sensors in previous automatic vehicle route prediction algorithms, an automatic vehicle route prediction algorithm based on multi-sensor fusion is studied. The process of fusion of multi-sensor information based on the D-S evidence reasoning fusion algorithm is applied to automatic vehicle route prediction. According to the contribution of a longitudinal acceleration sensor and yaw angular velocity sensor detection information to the corresponding motion model, the basic probability assignment function of each vehicle motion model is obtained; the basic probability assignment function of each motion model is synthesized by using D-S evidence reasoning synthesis formula. The new probability allocation of each motion model is obtained under all evidence and then deduced according to the decision rules. Guided by the current optimal motion model, the optimal motion model at each time is used to accurately predict the vehicle movement route. The simulation results show that the prediction error of the algorithm is less than 4% in the process of 30 minutes of automatic vehicle route prediction.

Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta

2020 ◽  
Vol 12 (3) ◽  
pp. 289-307
Author(s):  
Angelica Lo Duca ◽  
Andrea Marchetti
Keyword(s):  
Decision Trees ◽  
Nearest Neighbors ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Algorithms ◽  
Content Type ◽  
Route Prediction ◽  
Linear Algorithms

Purpose Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP. Design/methodology/approach Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta. Findings Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance. Research limitations/implications This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems. Practical implications The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing. Originality/value The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

Shipboard Fire Evacuation Route Prediction Algorithm Development

2018 ◽  
Vol 24 (5) ◽  
pp. 519-526
Author(s):  
Kwang-Il Hwang ◽  
◽  
So-Hyung Cho ◽  
Hoo-Sang Ko ◽  
Ik-Soon Cho ◽  
...  
Keyword(s):  
Prediction Algorithm ◽  
Algorithm Development ◽  
Route Prediction ◽  
Fire Evacuation ◽  
Evacuation Route

scholarly journals Metagenomics Biomarkers Selected for Prediction of Three Different Diseases in Chinese Population

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Honglong Wu ◽  
Lihua Cai ◽  
Dongfang Li ◽  
Xinying Wang ◽  
Shancen Zhao ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Human Microbiome ◽  
Multiclass Classification ◽  
Prediction Algorithm ◽  
Selection For ◽  
Binary Classifiers ◽  
Metagenome Sequencing ◽  
Better Than

The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples. In this study, a total of 300 biomarkers were selected from the microbiome of 806 Chinese individuals (383 controls, 170 with type 2 diabetes, 130 with rheumatoid arthritis, and 123 with liver cirrhosis), and then logistic regression prediction algorithm was applied to those markers as the model intrinsic features. The estimated model produced an F1 score of 0.9142, which was better than other popular classification methods, and an average receiver operating characteristic (ROC) of 0.9475 showed a significant correlation between these selected biomarkers from microbiome and corresponding phenotypes. The results from this study indicate that machine learning is a vital tool in data mining from microbiome in order to identify disease-related biomarkers, which may contribute to the application of microbiome-based precision medicine in the future.

Sign in / Sign up

Export Citation Format

Share Document