Recent Advances in Numerical Methods, Machine Learning, and Computer Science

2021 ◽  
pp. 1-5
Author(s):  
Margarita N. Favorskaya ◽  
Alena V. Favorskaya ◽  
Igor B. Petrov ◽  
Lakhmi C. Jain
Keyword(s):  
Machine Learning ◽  
Numerical Methods ◽  
Computer Science ◽  
Recent Advances

Withdrawal Notice: Prognosis And Prediction Of Breast Cancer (Bc) Using Growing Mlt (Machine Learning Techniques)

2020 ◽  
Vol 13 ◽  
Author(s):  
Rajesh Kumar Maurya
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Computer Science ◽  
Editorial Policy ◽  
Machine Learning Techniques ◽  
Editorial Policies ◽  
Recent Advances ◽  
Learning Techniques ◽  
Prognosis And Prediction ◽  
Copyright Permission

The article has been withdrawn at the request of the editor of the journal Recent Advances in Computer Science and Communications. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

scholarly journals Latest Advances in Cardiac CT

2020 ◽  
Vol 15 ◽  
Author(s):  
Thomas D Heseltine ◽  
Scott W Murray ◽  
Balazs Ruzsics ◽  
Michael Fisher
Keyword(s):  
Machine Learning ◽  
Fractional Flow Reserve ◽  
Cardiac Ct ◽  
Disease Risk ◽  
Clinical Care ◽  
Plaque Burden ◽  
Extensive Literature ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Recent Advances

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

scholarly journals John Senior and Éva Gyarmathy: AI and Developing Human Intelligence, Future Learning and Educational Innovation

2021 ◽  
Vol 28 (3) ◽  
pp. 442-446
Author(s):  
Valentin Kuleto ◽  
Milena Ilić
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Problem Solving ◽  
Computer Science ◽  
Image Recognition ◽  
Language Processing ◽  
Educational Innovation ◽  
Human Intelligence ◽  
Intelligent Machines ◽  
Future Learning

AI is a branch of computer science that emphasises the development of intelligent machines that think and work like humans. Examples of AI applications are speech recognition, natural language processing, image recognition etc. The term ML represents the application of AI to enable systems’ ability to learn and improve based on experience, without the explicit need for programming, using various problem-solving algorithms. For example, in machine learning, computers learn based on the data they process, not program instructions

Machine Learning for Vehicular Networks: Recent Advances and Application Examples

2018 ◽  
Vol 13 (2) ◽  
pp. 94-101 ◽  
Author(s):  
Hao Ye ◽  
Le Liang ◽  
Geoffrey Ye Li ◽  
JoonBeom Kim ◽  
Lu Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vehicular Networks ◽  
Recent Advances

Guest Column

2021 ◽  
Vol 52 (2) ◽  
pp. 46-70
Author(s):  
A. Knop ◽  
S. Lovett ◽  
S. McGuire ◽  
W. Yuan
Keyword(s):  
Machine Learning ◽  
Approximation Algorithms ◽  
Computer Science ◽  
Data Structures ◽  
Coding Theory ◽  
Communication Complexity ◽  
Property Testing ◽  
Theoretical Computer Science ◽  
Streaming Algorithms ◽  
Theoretical Computer

Communication complexity studies the amount of communication necessary to compute a function whose value depends on information distributed among several entities. Yao [Yao79] initiated the study of communication complexity more than 40 years ago, and it has since become a central eld in theoretical computer science with many applications in diverse areas such as data structures, streaming algorithms, property testing, approximation algorithms, coding theory, and machine learning. The textbooks [KN06,RY20] provide excellent overviews of the theory and its applications.

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