Unique Face Identification System using Machine Learning

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.

Download Full-text

A proposed framework for robust face identification system

2014 9th International Conference on Computer Engineering & Systems (ICCES) ◽

10.1109/icces.2014.7030929 ◽

2014 ◽

Cited By ~ 2

Author(s):

Noura A. Semary ◽

Ahmed Fawzi Gad

Keyword(s):

Face Identification ◽

Identification System

Download Full-text

Applying machine learning to satellite imagery and vessel-tracking data to detect chronic oil pollution from ships at sea and identify the polluters

10.5194/egusphere-egu21-3737 ◽

2021 ◽

Author(s):

Jona Raphael ◽

Ben Eggleston ◽

Ryan Covington ◽

Tatianna Evanisko ◽

Sasha Bylsma ◽

...

Keyword(s):

Machine Learning ◽

Oil Spills ◽

Oil Pollution ◽

Satellite Image ◽

Petroleum Products ◽

Training Data ◽

Automatic Identification ◽

Identification System ◽

Sar Imagery ◽

Pollution Events

Operational oil discharges from ships, also known as &#8220;bilge dumping,&#8221; have been identified as a major source of petroleum products entering our oceans, cumulatively exceeding the largest oil spills, such as the Exxon Valdez and Deepwater Horizon spills, even when considered over short time spans. However, we still don&#8217;t have a good estimate of<ul><li>How much oil is being discharged;</li> <li>Where the discharge is happening;</li> <li>Who the responsible vessels are.</li> </ul>This makes it difficult to prevent and effectively respond to oil pollution that can damage our marine and coastal environments and economies that depend on them.&#160;In this presentation we will share SkyTruth&#8217;s recent work to address these gaps using machine learning tools to detect oil pollution events and identify the responsible vessels when possible. We use a convolutional neural network (CNN) in a ResNet-34 architecture to perform pixel segmentation on all incoming Sentinel-1 synthetic aperture radar (SAR) imagery to classify slicks. Despite the satellites&#8217; incomplete oceanic coverage, we have been detecting an average of 135 vessel slicks per month, and have identified several geographic hotspots where oily discharges are occurring regularly. For the images that capture a vessel in the act of discharging oil, we rely on an Automatic Identification System (AIS) database to extract details about the ships, including vessel type and flag state. We will share our experience<ul><li>Making sufficient training data from inherently sparse satellite image datasets;</li> <li>Building a computer vision model using PyTorch and fastai;</li> <li>Fully automating the process in the Amazon Web Services (AWS) cloud.</li> </ul>The application has been running continuously since August 2020, has processed over 380,000 Sentinel-1 images, and has populated a database with more than 1100 high-confidence slicks from vessels. We will be discussing preliminary results from this dataset and remaining challenges to be overcome.&#160;Our objective in making this information and the underlying code, models, and training data freely available to the public and governments around the world is to enable public pressure campaigns to improve the prevention of and response to pollution events. Learn more at https://skytruth.org/bilge-dumping/

Download Full-text

Content-Based Machine Learning Approach for Hardware Vulnerabilities Identification System

Advances in Computer Science for Engineering and Education IV - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-80472-5_10 ◽

2021 ◽

pp. 117-126

Author(s):

Giorgi Iashvili ◽

Zhadyra Avkurova ◽

Maksim Iavich ◽

Madina Bauyrzhan ◽

Avtandil Gagnidze ◽

...

Keyword(s):

Machine Learning ◽

Identification System ◽

Learning Approach ◽

Machine Learning Approach

Download Full-text

Face Recognition Model Using Back Propagation

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.34.18973 ◽

2018 ◽

Vol 7 (3.34) ◽

pp. 237

Author(s):

R Aswini Priyanka ◽

C Ashwitha ◽

R Arun Chakravarthi ◽

R Prakash

Keyword(s):

Neural Network ◽

Deep Learning ◽

Face Recognition ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Face Identification ◽

Identification System ◽

Scientific World ◽

The Face ◽

Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.

Download Full-text