scholarly journals A Trajectory Scoring Tool for Local Anomaly Detection in Maritime Traffic Using Visual Analytics

2021 ◽  
Vol 10 (6) ◽  
pp. 412
Author(s):  
Fernando H. O. Abreu ◽  
Amilcar Soares ◽  
Fernando V. Paulovich ◽  
Stan Matwin
Keyword(s):  
Visual Analytics ◽  
Domain Knowledge ◽  
Surveillance Systems ◽  
Maritime Surveillance ◽  
Maritime Traffic ◽  
Sea Transportation ◽  
Maritime Navigation ◽  
Illegal Activities ◽  
Scoring Tool ◽  
Local Anomalies

With the recent increase in the use of sea transportation, the importance of maritime surveillance for detecting unusual vessel behavior related to several illegal activities has also risen. Unfortunately, the data collected by surveillance systems are often incomplete, creating a need for the data gaps to be filled using techniques such as interpolation methods. However, such approaches do not decrease the uncertainty of ship activities. Depending on the frequency of the data generated, they may even confuse operators, inducing errors when evaluating ship activities and tagging them as unusual. Using domain knowledge to classify activities as anomalous is essential in the maritime navigation environment since there is a well-known lack of labeled data in this domain. In an area where identifying anomalous trips is a challenging task using solely automatic approaches, we use visual analytics to bridge this gap by utilizing users’ reasoning and perception abilities. In this work, we propose a visual analytics tool that uses spatial segmentation to divide trips into subtrajectories and score them. These scores are displayed in a tabular visualization where users can rank trips by segment to find local anomalies. The amount of interpolation in subtrajectories is displayed together with scores so that users can use both their insight and the trip displayed on the map to determine if the score is reliable.

scholarly journals A Review over AI Methods Developed for Maritime Awareness Systems

2020 ◽  
Vol XXIII (2) ◽  
pp. 287-299
Author(s):  
Pohontu Alexandru
Keyword(s):  
Artificial Intelligence ◽  
Safety Management ◽  
Vital Role ◽  
Data Sources ◽  
Maritime Surveillance ◽  
Maritime Traffic ◽  
Multiple Data ◽  
Collision Prevention ◽  
Traffic Security ◽  
Illegal Activities

Due to their operations against illegal activities, maritime threats or collision prevention analysis, maritime surveillance plays a vital role in maritime traffic security and safety management. Today's maritime surveillance and awareness systems can integrate multiple data sources like: coastal, HFSWR and SAR radars, AIS or satellite imagery; and this process produces massive amounts of data. That available data can be processed, with the use of Artificial Intelligence (AI) methods and algorithms, to automatically monitor the maritime traffic and its implications in safety, security, economy and environment. This paper's purpose is to briefly reveal current AI techniques that have been researched and deployed in the industry, and to seize the opportunity of implementing them.

AIS Data Analytics for Intelligent Maritime Surveillance Systems

2020 ◽  
pp. 393-411
Author(s):  
Xiuju Fu ◽  
Zhe Xiao ◽  
Haiyan Xu ◽  
Vasundhara Jayaraman ◽  
Nasri Bin Othman ◽  
...  
Keyword(s):  
Data Analytics ◽  
Surveillance Systems ◽  
Maritime Surveillance

scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

scholarly journals An analecta of visualizations for foodborne illness trends and seasonality

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ryan B. Simpson ◽  
Bingjie Zhou ◽  
Tania M. Alarcon Falconi ◽  
Elena N. Naumova
Keyword(s):  
Visual Analytics ◽  
Disease Surveillance ◽  
Temporal Trends ◽  
Disease Outbreaks ◽  
Data Interpretation ◽  
Population Based ◽  
Annual Reports ◽  
Surveillance Systems ◽  
The Us ◽  
Annual Time Series

Abstract Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention’s (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.

Visual Analytics in Ship Performance and Navigation Information for Sensor Specific Fault Detection

2017 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo
Keyword(s):  
Fault Detection ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Data Sets ◽  
Data Set ◽  
Navigation Data ◽  
Hidden Data ◽  
Ship Performance ◽  
Navigation Information ◽  
Erroneous Data

Ocean internet of things (IoT - onboard and onshore) collects big data sets of ship performance and navigation information under various data handling processes. That extract vessel performance and navigation information that are used for ship energy efficiency and emission control applications. However, the quality of ship performance and navigation data can play an important role in such applications, where sensor faults may introduce various erroneous data regions and that may degrade to the outcome. This study proposes visual analytics, where hidden data patterns, clusters, correlations and other useful information are visually from the respective data set extracted, to identify such erroneous data regions. The domain knowledge (i.e. ship performance and navigation conditions) has also been used to interpret such erroneous data regions and identify the respective sensors that relate to the same situations. Finally, a ship performance and navigation data set of a selected vessel is analyzed to identify erroneous data regions for three selected sensor fault situations (i.e. wind, log speed and draft sensors) under the proposed visual analytics. Hence, this approach can be categorized as a sensor specific fault detection methodology by considering the same results.

scholarly journals Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach (Preprint)

2020 ◽  
Author(s):  
Rui Li ◽  
Changchang Yin ◽  
Samuel Yang ◽  
Buyue Qian ◽  
Ping Zhang
Keyword(s):  
Heart Failure ◽  
Deep Learning ◽  
Risk Prediction ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Knowledge Graph ◽  
Data Set ◽  
Medical Domain ◽  
Clinical Risks

BACKGROUND Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction. Both the electronic health record (EHR) data set and the deep model results are complex and abstract, which impedes clinicians from exploring and communicating with the model directly. OBJECTIVE The objective of this study is to develop an interpretable and accurate risk prediction model as well as an interactive clinical prediction system to support EHR data exploration, knowledge graph demonstration, and model interpretation. METHODS A domain-knowledge–guided recurrent neural network (DG-RNN) model is proposed to predict clinical risks. The model takes medical event sequences as input and incorporates medical domain knowledge by attending to a subgraph of the whole medical knowledge graph. A global pooling operation and a fully connected layer are used to output the clinical outcomes. The middle results and the parameters of the fully connected layer are helpful in identifying which medical events cause clinical risks. DG-Viz is also designed to support EHR data exploration, knowledge graph demonstration, and model interpretation. RESULTS We conducted both risk prediction experiments and a case study on a real-world data set. A total of 554 patients with heart failure and 1662 control patients without heart failure were selected from the data set. The experimental results show that the proposed DG-RNN outperforms the state-of-the-art approaches by approximately 1.5%. The case study demonstrates how our medical physician collaborator can effectively explore the data and interpret the prediction results using DG-Viz. CONCLUSIONS In this study, we present DG-Viz, an interactive clinical prediction system, which brings together the power of deep learning (ie, a DG-RNN–based model) and visual analytics to predict clinical risks and visually interpret the EHR prediction results. Experimental results and a case study on heart failure risk prediction tasks demonstrate the effectiveness and usefulness of the DG-Viz system. This study will pave the way for interactive, interpretable, and accurate clinical risk predictions.

scholarly journals A Novel Adaptive Visual Analytics Framework for Multiship Encounter Identification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rong Zhen ◽  
Ziqiang Shi
Keyword(s):  
Collision Avoidance ◽  
Visual Analytics ◽  
Surveillance System ◽  
Spatial Clustering ◽  
Automatic Identification ◽  
Dbscan Clustering ◽  
Maritime Surveillance ◽  
Visual Identification ◽  
Safety Surveillance ◽  
Ship Collision

The automatic identification of multiship encounter is a vital criterion for ship collision avoidance and intelligent maritime safety surveillance. However, the parameters of ship encounter identification in the existing studies are fixed, and the methods are weak to give an automatic and visual performance in the multiship encounter identification. In order to fix the existed gap, this paper proposed a novel adaptive visual analytics framework for automatic multiship encounter identification based on density-based spatial clustering of applications with noise (DBSCAN) and visual analytics by adjusting the parameters of ship encounter adaptively. The DBSCAN clustering method was applied to detect the clusters of encounter ships and filter out the nonencounter ship, and the distribution and density of the encounter ship had been visualized on the nautical chart to give a better perception of ships’ behavior with a potentially high navigational risk. The framework had been designed and developed using DBSCAN and visual analytics, and the effectiveness was evaluated and validated by adjusting different parameters of multiship encounter within the Southwest waters of Zhoushan Island, China. The results showed that the proposed framework had a good performance in the visual identification of multiship encounter within confined waters, which could assist the ship collision avoidance and intelligent maritime surveillance system.

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