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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 Optimization (WEWO) algorithm. The frames are preprocessed and extracted using Discrete Cosine transform (DCT) and Structured Similarity index (SSIM), respectively, as regular processing. For pixel prediction, the fitness function is obtained from neighborhood entropies in proposed algorithm. In this method, secret message is embedded with two level decomposition of Wavelet Transform (WT). In the proposed work is tested with ‘CAVIAR’ dataset. The Proposed WEWO-Deep RNN algorithm performance is tested with modular noises such as, pepper, salt and pepper noises. The proposed method gives enhanced performance, which is seen with the parameters, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Correlation Coefficient (CC) which defines image quality indices.

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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 backward simultaneously, can make planning such waves feasible and efficient by reducing required iterations by up around 2 orders of magnitude. With this path planner, it is possible to calculate the number of waves to get to arbitrary combinations of position and orientation in a space. This reveals boundaries in configuration space that can be used to determine whether to continue forward or back-up before maneuvering, as in the worm-like equivalent of parallel parking. The high number of waves required to shift the body laterally by even a single body width suggests that strategies for lateral motion, planning around obstacles and responsive behaviors will be important for future worm-like robots.

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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 detect new types accurately. The proposal is based on the UNSW NB15 dataset and uses a support vector machine to train and test the ensemble bagging algorithm. To detect various types of worms efficiently, the contribution suggests combining correlation and Chi2 feature selection method called Chi2-Corr to select relevant features and using support vector machine (SVM) in the bagging algorithm. The system achieved accuracy reaching 0.998 with Chi2-Corr, and 0.989, 0.992 with correlation and chi-square separately.</p>

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