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Relevant bibliographies by topics / Worm Algorithm

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Journal articles

Academic literature on the topic 'Worm Algorithm'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Worm Algorithm"

1

Rindlisbacher, Tobias, and Philippe de Forcrand. "Worm algorithm for theCPN−1model." Nuclear Physics B 918 (May 2017): 178–219. http://dx.doi.org/10.1016/j.nuclphysb.2017.02.021.

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2

Gaofei, Zheng, Wang Xiufeng, and Zhang Yanli. "The self-organizing worm algorithm." Journal of Systems Engineering and Electronics 18, no. 3 (2007): 650–54. http://dx.doi.org/10.1016/s1004-4132(07)60143-1.

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3

Delgado, Y., and A. Schmidt. "Worm Algorithm for Abelian Gauge-Higgs Models." Acta Physica Polonica B Proceedings Supplement 6, no. 3 (2013): 911. http://dx.doi.org/10.5506/aphyspolbsupp.6.911.

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4

Prokof'ev, N. V., B. V. Svistunov, and I. S. Tupitsyn. "“Worm” algorithm in quantum Monte Carlo simulations." Physics Letters A 238, no. 4-5 (1998): 253–57. http://dx.doi.org/10.1016/s0375-9601(97)00957-2.

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5

Kerl, John. "A worm algorithm for random spatial permutations." Physics Procedia 4 (2010): 61–65. http://dx.doi.org/10.1016/j.phpro.2010.08.009.

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6

Janke, Wolfhard, Thomas Neuhaus, and Adriaan M. J. Schakel. "Critical loop gases and the worm algorithm." Nuclear Physics B 829, no. 3 (2010): 573–99. http://dx.doi.org/10.1016/j.nuclphysb.2009.12.024.

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7

Yang, XinYu, Yi Shi, and HuiJun Zhu. "Detection and location algorithm against local-worm." Science in China Series F: Information Sciences 51, no. 12 (2008): 1935–46. http://dx.doi.org/10.1007/s11432-008-0132-z.

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8

Salunkhe, Shamal, and Surendra Bhosale. "Nature Inspired Algorithm for Pixel Location Optimization in Video Steganography Using Deep RNN." International Journal on Engineering, Science and Technology 3, no. 2 (2022): 146–54. http://dx.doi.org/10.46328/ijonest.67.

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The steganography is applied on text, image, video, and audio files. The steganography is useful for safe and secure data transmission. Video steganography is used to preserve confidential information of security applications. To improve security of the message, pixels locations are optimized using nature inspired algorithm. As conventional algorithms have a low convergence rate a new algorithm is proposed. A New algorithm is developed by combining two model algorithms namely, Water wave optimization (WWO) and Earth worm optimization (EWO) and is renamed as proposed Water-Earth Worm Optimizati

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9

Wang, Yifan, Prathamesh Pandit, Akhil Kandhari, Zehao Liu, and Kathryn A. Daltorio. "Rapidly Exploring Random Tree Algorithm-Based Path Planning for Worm-Like Robot." Biomimetics 5, no. 2 (2020): 26. http://dx.doi.org/10.3390/biomimetics5020026.

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Inspired by earthworms, worm-like robots use peristaltic waves to locomote. While there has been research on generating and optimizing the peristalsis wave, path planning for such worm-like robots has not been well explored. In this paper, we evaluate rapidly exploring random tree (RRT) algorithms for path planning in worm-like robots. The kinematics of peristaltic locomotion constrain the potential for turning in a non-holonomic way if slip is avoided. Here we show that adding an elliptical path generating algorithm, especially a two-step enhanced algorithm that searches path both forward and

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10

Hilool, Ali Khalid, Soukaena H. Hashem, and Shatha H. Jafer. "Intrusion detection system based on bagging with support vector machine." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 1100. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp1100-1106.

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Abstract:

<p>Due to their rapid spread, computer worms perform harmful tasks in networks, posing a security risk; however, existing worm detection algorithms continue to struggle to achieve good performance and the reasons for that are: First, a large amount of irrelevant data affects classification accuracy. Second, individual classifiers do not detect all types of worms effectively. Third, many systems are based on outdated data, making them unsuitable for new worm species. The goal of the study is to use data mining algorithms to detect worms in the network because they have a high ability to d

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