A Framework for End-to-End Ontology Management System

Author(s):  
Anusha Indika Walisadeera ◽  
Athula Ginige ◽  
Gihan Nilendra Wikramanayake ◽  
A. L. Pamuditha Madushanka ◽  
A. A. Shanika Udeshini

Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.


Author(s):  
Ivo Jesus ◽  
Tomas Pereira ◽  
Pedro Marques ◽  
Joao Sousa ◽  
Luis Perdigoto ◽  
...  

2009 ◽  
Vol 22 (4) ◽  
pp. 292-301 ◽  
Author(s):  
M. Gaeta ◽  
F. Orciuoli ◽  
P. Ritrovato

Transfusion ◽  
2012 ◽  
Vol 52 (12) ◽  
pp. 2502-2512 ◽  
Author(s):  
Michael F. Murphy ◽  
Edward Fraser ◽  
David Miles ◽  
Simon Noel ◽  
Julie Staves ◽  
...  

2021 ◽  
Author(s):  
Osama Hasan Khan ◽  
Samad Ali ◽  
Mohamed Ahmed Elfeel ◽  
Shripad Biniwale ◽  
Rashmin Dandekar

Abstract Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives. This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies. The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives. Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.


2002 ◽  
Vol 10 (04) ◽  
pp. 285-302 ◽  
Author(s):  
SUDESHNA ADAK

The advent of high-density microarrays has made it possible for scientists to measure the expression levels of thousands of genes simultaneously. Understanding and interpreting the massive volumes of microarray data is necessary to unravel the molecular basis of diseases and will someday lead to medicines tailored for individual genetic profiles. One of the main barriers to realizing the full potential of microarrays today is the need for specialized bioinformatics and knowledge management solutions required to mine the microarray data for biological information. After initial efforts at clustering expression data based on similarity, scientists have recognized the need to cross-reference and correlate experimental data with external data sources, to improve the quality of the biological conclusions that can be drawn. This paper describes e2eXpress, such an end-to-end Bioinformatics and Knowledge Management System for Microarrays. e2eXpress incorporates basic data management and analysis tasks with novel approaches for mining various molecular biological databases to summarize information regarding coregulated gene clusters. In particular, this paper describes two new algorithms: (a) Text Mining for Gene Clusters: a statistical algorithm that is aimed at deriving biologically relevant information for gene clusters from the biomedical literature; (b) Pathway Scoring for Gene Clusters: a computational algorithm that is aimed at deriving pathway related information for gene clusters. This paper describes the variety of statistical and computational algorithms that are required to mine the transcriptome in conjunction with extraneous data sources that can lead to real biological advances.


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