다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

(1977). Markov Chains — Theory and Applications. Journal of the Operational Research Society: Vol. 28, Volume 28, issue 1, pp. 236-237.

Markov Chains — Theory and Applications

Objective@#To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection.@*Methods@#A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV. They were admitted to the third people’s Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed.@*Results@#Among the 15 children, there were 5 males and 10 females, aged from 4 to 14 years old. Five of the 15 children were febrile and 10 were asymptomatic on first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammation lesions. Seven cases of small nodular ground glass opacities and 2 cases of speckled ground glass opacities were found. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. Other 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities.@*Conclusions@#The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.

Analysis of CT features of 15 Children with 2019 novel coronavirus infection/ 中华儿科杂志/ Analysis of CT features of 15 Children with 2019 novel coronavirus infection/ 中华儿科杂志

Spatial-based skin detection using discriminative skin-presence features

Introduction.- Data-Driven Discovery of Physical, Chemical, and Pharmaceutical Materials.- Cross-Validation and Inference in Bioinformatics/Cancer Genomics.- Applying MQSPRs - New Challenges and Opportunities.- Data Mining in Materials Science.- Data Science in the Defense Establishment.- Combining Heuristic and Physics-Based Methods for Predicting Nanocomposite Properties.- From Ferroelectrics to Fuel Cells: In Search of Descriptors for the Transport Properties of Complex Oxides.- Computationally Driven Targeting of Advanced Thermoelectric Materials.- The MGI, Materials Informatics, and NIST) Microstructure Informatics for Mining Structure-Property-Processing Linkages.- A Genomic Approach to Properties of MAX Phase Compounds.- Accelerating Discovery of Complex Formulations and Molecules.- Optimal Learning for Discovering Minimal Peptide Substrates.- Model-based Classification: Predictive and Optimal.

Information science for materials discovery and design

http://ce.sharif.ir/~abin/files/pr2014.pdf

Active constrained fuzzy clustering

Medical imaging and computational image analysis in COVID-19 diagnosis: A review.

In this paper, we propose a novel algorithm that combines color and texture information of skin with cellular learning automata to segment skin-like regions in color images. First, the presence of skin colors in an image is detected, using a committee structure, to make decision from several explicit boundary skin models. Detected skin-color regions are then fed to a color texture extractor that extracts the texture features of skin regions via their color statistical properties and maps them to a skin probability map. Cellular learning automatons use this map to make decision on skin-like regions. The proposed algorithm has demonstrated true positive rate of about 83.4% and false positive rate of about 11.3% on the Compaq skin database. Experimental results show the effectiveness of the proposed algorithm.

/pdf/skin-segmentation-based-on-cellular-learning-automata-30683l2u0c.pdf

Skin segmentation based on cellular learning automata

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.

http://www.ce.sharif.edu/~abin/files/msj2009.pdf

A new dynamic cellular learning automata-based skin detector

http://ce.sharif.ir/~abin/files/pr2014-1.pdf

Ahmad Ali Abin

Papers

Active constrained fuzzy clustering

Medical imaging and computational image analysis in COVID-19 diagnosis: A review.

Skin segmentation based on cellular learning automata

A new dynamic cellular learning automata-based skin detector

Active selection of clustering constraints