ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs) APPROACH

2021 ◽  
Vol 157 (A3) ◽  
Author(s):  
D Handayani ◽  
W Sediono ◽  
A Shah
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

Anomaly Detection in Vessel Tracking – A Bayesian Networks (BNs) Approach

2015 ◽  
Vol 157 (A3) ◽  
pp. 145-152
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

"The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour."

Image Processing and Machine Learning Techniques for Facial Expression Recognition

2009 ◽  
pp. 247-262 ◽  
Author(s):  
Anastasios Koutlas ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Expression Recognition ◽  
Basic Emotions ◽  
Learning Techniques ◽  
The Face

The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression recognition. A comprehensive review of recently proposed methods is provided along with an analysis of the advantages and the shortcomings of existing systems. Moreover, an example for the automatic identification of basic emotions is presented: Active Shape Models are used to identify prominent features of the face; Gabor filters are used to represent facial geometry at selected locations of fiducial points and Artificial Neural Networks are used for the classification into the basic emotions (anger, surprise, fear, happiness, sadness, disgust, neutral); and finally, the future trends towards automatic facial expression recognition are described.

Nowcasting Lightning Occurrence Using Machine Learning Techniques: The Challenge of Identifying Outliers

2020 ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Meteorological Parameters ◽  
Warning System ◽  
Weather Forecast ◽  
Field Analysis ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Learning Techniques ◽  
Kennedy Space

<p>Lightning is formed in the atmosphere through the combination of complex dynamic and microphysical processes. Lightning can have a considerable influence on the environment and on the economy since it causes energy supply outages, forest fires, damages, injury and death of humans and livestock worldwide. Therefore, it is of great importance to be able to predict lightning incidence in order to protect people and installations. Despite numerous attempts to solve the important problem of lightning prediction (e.g., [1]–[3]), the complex processes and large number of parameters involved in the problem lend themselves to the potential application of a machine learning (ML) approach.</p><p>We recently proposed a ML-based lightning early-warning system with promising performance [4]. The proposed ML model is trained to nowcast lightning incidence during any one of  three consecutive 10-minute time intervals and within a circular area of 30 km radius around a meteorological station. The system uses the real-time measured values of four meteorological parameters that are relevant to the mechanisms of electric charge generation in thunderstorms, namely the air pressure at station level (QFE), the air temperature 2 m above ground, the relative humidity, and the wind speed. The proposed algorithm was implemented using the data from 12 meteorological stations in Switzerland between 2006-2017 with a granularity of ten minutes. The stations were selected to be well distributed among different ranges of altitude and terrain topographies.</p><p>The algorithm requires the filtering out of a portion of the data which are identified as outliers. However, the process of the automatic identification of outliers is a challenging task which could also affect the model’s performance. In this presentation, we discuss this problem and present approaches that can be used to optimize the process.</p><p> </p><p><strong>References</strong></p><p>[1]      D. Aranguren, J. Montanya, G. Solá, V. March, D. Romero, and H. Torres, “On the lightning hazard warning using electrostatic field: Analysis of summer thunderstorms in Spain,” J. Electrostat., vol. 67, no. 2–3, pp. 507–512, May 2009.</p><p>[2]      G. N. Seroka, R. E. Orville, and C. Schumacher, “Radar Nowcasting of Total Lightning over the Kennedy Space Center,” Weather Forecast., vol. 27, no. 1, pp. 189–204, Feb. 2012.</p><p>[3]      Q. Meng, W. Yao, and L. Xu, “Development of Lightning Nowcasting and Warning Technique and Its Application,” Adv. Meteorol., vol. 2019, pp. 1–9, Jan. 2019.</p><p>[4]      A. Mostajabi, D. L. Finney, M. Rubinstein, and F. Rachidi, “Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques,” npj Clim. Atmos. Sci., vol. 2, no. 1, p. 41, 2019.</p>

scholarly journals Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 40
Author(s):  
Antonia Terán-Bustamante ◽  
Antonieta Martínez-Velasco ◽  
Griselda Dávila-Aragón
Keyword(s):  
Machine Learning ◽  
Knowledge Management ◽  
Bayesian Networks ◽  
Business Strategy ◽  
Machine Learning Techniques ◽  
Global Business ◽  
Adequate Knowledge ◽  
Learning Techniques ◽  
Relevant Factors

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian networks with machine learning techniques, for which a model is made to identify and quantify the various factors that affect the correct management of knowledge in an organization, allowing you to generate value. As a case study, a technology-based services company in Mexico City is analyzed. The evidence found shows the optimal and non-optimal management of knowledge management, and its various factors, through the causality of the variables, allowing us to more adequately capture the interrelationship to manage it. The results show that the most relevant factors for having adequate knowledge management are information management, relational capital, intellectual capital, quality and risk management, and technology assimilation.

scholarly journals Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models

2019 ◽  
Vol 8 (12S) ◽  
pp. 92-94 ◽  
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Detection System ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Identification System ◽  
Financial Loss ◽  
Learning Techniques ◽  
Free Data

In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.

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