6 research outputs found
A numerical model of blood oxygenation during veno-venous ECMO: analysis of the interplay between blood oxygenation and its delivery parameters
No full textLeveraging artificial intelligence for competitive advantage: a case study of Samsung
No full textThe author examines how it is possible to use artificial intelligence to the advantage of an organization that operates in the technology industry. The main focus of the research was to identify how effectively and in what ways Samsung used AI technologies in its smart phones and home appliance product segments to improve functionality and usability as well as operational effectiveness. This was to have been achieved through the use of Artificial Intelligence in manufacturing processes as well as in various ways to deliver unique customized services to customer unlike the rival companies. This work also shows how Bixby, Samsung’s own AI assistant or smart IoT solutions all help towards creating a smarter environment, thereby building goodwill for the brand and generating additional revenue streams. The study further shows how Samsung supports that the application of AI can enhance a firm’s competitive position by pushing product differentiation, monitoring and anticipating equipment wear and tear and making better decisions from analyzed data
Stromal Claudin14-Heterozygosity, but Not Deletion, Increases Tumour Blood Leakage without Affecting Tumour Growth
No full textPMCID: PMC3652830This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
The QChip1 knowledgebase and microarray for precision medicine in Qatar
No full textRisk genes for Mendelian (single-gene) disorders (SGDs) are consistent across populations, but pathogenic risk variants that cause SGDs are typically population-private. The goal was to develop "QChip1," an inexpensive genotyping microarray to comprehensively screen newborns, couples, and patients for SGD risk variants in Qatar, a small nation on the Arabian Peninsula with a high degree of consanguinity. Over 108 variants in 8445 Qatari were identified for inclusion in a genotyping array containing 165,695 probes for 83,542 known and potentially pathogenic variants in 3438 SGDs. QChip1 had a concordance with whole-genome sequencing of 99.1%. Testing of QChip1 with 2707 Qatari genomes identified 32,674 risk variants, an average of 134 pathogenic alleles per Qatari genome. The most common pathogenic variants were those causing homocystinuria (1.12% risk allele frequency), and Stargardt disease (2.07%). The majority (85%) of Qatari SGD pathogenic variants were not present in Western populations such as European American, South Asian American, and African American in New York City and European and Afro-Caribbean in Puerto Rico; and only 50% were observed in a broad collection of data across the Greater Middle East including Kuwait, Iran, and United Arab Emirates. This study demonstrates the feasibility of developing accurate screening tools to identify SGD risk variants in understudied populations, and the need for ancestry-specific SGD screening tools. 2022, The Author(s).This is a collaborative work between Qatar Genome, Qatar Biobank, Weill Cornell (New York and Qatar), Hamad Medical Corporation and Sidra Medicine. We are thankful for everyone who contributed to this endeavor from all participating institutes. We would like to especially thank all participants in this study for their continuous support. We thank Dr. Fatemeh Abbaszadeh, for quality control and implementing QChip in the diagnostic services; N. Mohamed for editorial support, E. Betancourt for administrative support, E. Guzman for IT support, and J. Pillardy for high-performance computing support. J.R.F. also thanks Alan R. Shuldiner and Regeneron Genetics Center for supporting, J.R.F. to help complete this project. Special thanks to Alphonse Tharangeval at the Dasman Diabetes Institute in Kuwait for providing allele frequency lookups, and to the Center for Arab Genetic Studies in UAE, the GME Variome at University of California at San Diego and the Iranomefor providing public access to their databases. The authors are saddened by the passing of Andrew Brooks after the manuscript was submitted to the journal for review. This publication was made possible by The Qatar Foundation, the Weill Cornell Medical College in Qatar; NPRP 09-741-3 193, NPRP 5-436-3-116, NPRP 7-1425-3-370, NPRP 7-1301-3-336, and NPRP P8-1913-3-396 from the Qatar National Research Fund (a member of the Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.Scopu
Automatic object classification for surveillance videos.
No full textPhDThe recent popularity of surveillance video systems, specially located in urban
scenarios, demands the development of visual techniques for monitoring purposes.
A primary step towards intelligent surveillance video systems consists on automatic
object classification, which still remains an open research problem and the keystone
for the development of more specific applications.
Typically, object representation is based on the inherent visual features. However,
psychological studies have demonstrated that human beings can routinely categorise
objects according to their behaviour. The existing gap in the understanding
between the features automatically extracted by a computer, such as appearance-based
features, and the concepts unconsciously perceived by human beings but
unattainable for machines, or the behaviour features, is most commonly known
as semantic gap. Consequently, this thesis proposes to narrow the semantic gap
and bring together machine and human understanding towards object classification.
Thus, a Surveillance Media Management is proposed to automatically detect and
classify objects by analysing the physical properties inherent in their appearance
(machine understanding) and the behaviour patterns which require a higher level of
understanding (human understanding). Finally, a probabilistic multimodal fusion
algorithm bridges the gap performing an automatic classification considering both
machine and human understanding.
The performance of the proposed Surveillance Media Management framework
has been thoroughly evaluated on outdoor surveillance datasets. The experiments
conducted demonstrated that the combination of machine and human understanding
substantially enhanced the object classification performance. Finally, the inclusion
of human reasoning and understanding provides the essential information to bridge
the semantic gap towards smart surveillance video systems
