Results 21 to 30 of about 160,535 (190)
A Novel Histogram Region Merging Based Multithreshold Segmentation Algorithm for MR Brain Images
International Journal of Biomedical Imaging, 2017 Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper.
Siyan Liu +3 more
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Symbolic Computation of Differential Equivalences [PDF]
, 2016 Ordinary differential equations (ODEs) are widespread in manynatural sciences including chemistry, ecology, and systems biology,and in disciplines such as control theory and electrical engineering.
Cardelli, Luca +3 more
core +3 more sources
Efficient Heuristics for Structure Learning of k-Dependence Bayesian Classifier
Entropy, 2018 The rapid growth in data makes the quest for highly scalable learners a popular one. To achieve the trade-off between structure complexity and classification accuracy, the k-dependence Bayesian classifier (KDB) allows to represent different number of ...
Yang Liu, Limin Wang, Minghui Sun
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The Summation Package Sigma: Underlying Principles and a Rhombus Tiling Application [PDF]
Discrete Mathematics & Theoretical Computer Science, 2004 We give an overview of how a huge class of multisum identities can be proven and discovered with the summation package Sigma implemented in the computer algebra system Mathematica. General principles of symbolic summation are discussed. We illustrate the
Carsten Schneider
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Symbolic WCET Computation [PDF]
ACM Transactions on Embedded Computing Systems, 2017 Parametric Worst-case execution time (WCET) analysis of a sequential program produces a formula that represents the worst-case execution time of the program, where parameters of the formula are user-defined parameters of the program (as loop bounds, values of inputs, or internal variables, etc).
Ballabriga, Clément +2 more
openaire +1 more source
Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies
IEEE Access, 2019 Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence ...
Yuguang Long +3 more
doaj +1 more source
Frontiers in Genetics, 2022 Studies have found that pathogenic fungi and plants have sRNA transboundary regulation mechanisms. However, no researchers have used computer methods to carry out comprehensive studies on whether there is a more remarkable similarity in the transboundary
Junxia Chi +14 more
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, 2017 A model of computation that is widely used in the formal analysis of reactive systems is symbolic algorithms. In this model the access to the input graph is restricted to consist of symbolic operations, which are expensive in comparison to the standard ...
Chatterjee, Krishnendu +3 more
core +1 more source
SymPy: symbolic computing in Python
PeerJ Computer Science, 2016 SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem.
Aaron Meurer +26 more
openaire +3 more sources
IEEE Access, 2020 Accurately identifying protein-ATP (Adenosine-5'-triphosphate) binding sites is significant for protein function annotation and new drug invention. Previous studies often utilize classical machine learning classification algorithms to predict protein-ATP
Jiazhi Song +5 more
doaj +1 more source