RETRACTED ARTICLE: New probabilistic models for managing user’s locations in PCS networks

Computing ◽  
2012 ◽  
Vol 95 (1) ◽  
pp. 25-66 ◽  
Author(s):  
Ahmed I. Saleh
2020 ◽  
Vol 17 (6) ◽  
pp. 76-91
Author(s):  
E. D. Solozhentsev

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.


2015 ◽  
Vol 6 (1) ◽  
pp. 63-69
Author(s):  
S. Szegedi ◽  
I. Lázár ◽  
T. Tóth

This article has been withdrawn - upon request by authors - by Akadémiai Kiadó due to suspected plagiarism.


2019 ◽  
Vol 3 ◽  
pp. 00047
Author(s):  
Nokiamy Sesena Tamba ◽  
Myrna Laksman-Huntley

<p class="MsoNormal" style="line-height: 24px;">As of October 15, 2019, the following article is being retracted from the UGM Digital Press Social Sciences and Humanities series.</p><p class="MsoNormal" style="line-height: 24px;"><span style="font-size: 1rem;">“Les structures des phrases dans les tracts du mai 1968” by Nokiamy Sesena Tamba and Myrna Laksman-Huntley, Social Sciences and Humanities Series Vol 3: 00033, Proceeding of Conférence internationale sur le français 2018, Joesana Tjahjani, Merry Andriani, Sajarwa, Wening Udasmoro (eds) DOI:&nbsp;</span><a href="https://doi.org/10.29037/digitalpress.43306" target="_blank" style="background-color: rgb(255, 255, 255); font-size: 1rem;">https://doi.org/10.29037/digitalpress.43306</a></p><p class="MsoNormal" style="line-height: 24px;">The original article is registered through this URL&nbsp;<a href="https://digitalpress.ugm.ac.id/article/306" target="_blank">https://digitalpress.ugm.ac.id/article/306</a></p><p class="MsoNormal" style="line-height: 24px;">as decided by authors and conference organizers on the basis of analytical error. It may encourage potential misleading circulation of information in the future.</p><p class="MsoNormal" style="line-height: 24px;">On the following exchange of information with the publisher, it has been decided that the article will be retracted.</p><p class="MsoNormal" style="line-height: 24px;">The retracted article will remain in public domain, that is maintaining its appearance on UGM Digital Press web archive and the Conférence internationale sur le français 2018 printed version. However, it will receive a watermark to accentuate its retracted status.</p>


Author(s):  
Saleh Abdou ◽  
Hany A. Amer ◽  
Hayam Abdel-ghany ◽  
mohsen albahar

2016 ◽  
Author(s):  
Stewart M. Edie ◽  
◽  
Peter D. Smits ◽  
David Jablonski

2016 ◽  
Vol 51 (1) ◽  
pp. 469-484 ◽  
Author(s):  
Damien Octeau ◽  
Somesh Jha ◽  
Matthew Dering ◽  
Patrick McDaniel ◽  
Alexandre Bartel ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doan Cong Le ◽  
Krisana Chinnasarn ◽  
Jirapa Chansangrat ◽  
Nattawut Keeratibharat ◽  
Paramate Horkaew

AbstractSegmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.


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