Google Docs: A Review

Attebury, Ramirose Ilene; George, Julie; Judd, Cindy; Marcum, Brad

doi:10.7771/2380-176x.2736

“…For experiment, Several Python libraries which are networkx [20] version 2.6.3, pandas [21] version 1.3.5, numpy [22] version 1.21.6, google.colab [23] version 0.0.1a2, matplotlib.pyplot [24], collections, io, tqdm [25] version 4.64.1 have been used in Google Colab platform [23]. Documentations done in Google Docs [26]. In this experiment, each brain network has been constructed from edges connection list.…”

Section: Methodsmentioning

confidence: 99%

Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

Pran

¹

2023

Preprint

0

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In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks. By analyzing the results of correlations among these network measurement algorithms, statistical comparison among selected brain networks has also been summarized. Besides that, to understand each brain network, the visualization of each brain network and each brain network’s degree distribution histogram have been extrapolated. Six network measurement algorithms have been chosen to apply time to time on sixteen brain networks based on applicability of these network measurement algorithms and the results of these network measurements are put into a correlation method to show the relationship among these six network measurement algorithms for each brain network. At the end, the results of the correlations have been summarized to show the statistical comparison among these sixteen brain networks.

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“…For experiment, Several Python libraries which are networkx [20] version 2.6.3, pandas [21] version 1.3.5, numpy [22] version 1.21.6, google.colab [23] version 0.0.1a2, matplotlib.pyplot [24], collections, io, tqdm [25] version 4.64.1 have been used in Google Colab platform [23]. Documentations done in Google Docs [26].…”

Section: Methodsmentioning

confidence: 99%

Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

Pran¹

2023

Preprint

0

View full text Add to dashboard Cite

In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks. By analyzing the results of correlations among these network measurement algorithms, statistical comparison among selected brain networks has also been summarized. Besides that, to understand each brain network, the visualization of each brain network and each brain network’s degree distribution histogram have been extrapolated. Six network measurement algorithms have been chosen to apply time to time on sixteen brain networks based on applicability of these network measurement algorithms and the results of these network measurements are put into a correlation method to show the relationship among these six network measurement algorithms for each brain network. At the end, the results of the correlations have been summarized to show the statistical comparison among these sixteen brain networks.

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“…Code has been written in Python language [17] and Packages are: Google.colab: 0.0.1a2, Networkx: 3.0 [18], Pandas: 1.3.5 [19], Numpy: 1.21.6 [20], Matplotlib: 3.5.3 [21], Scipy: 1.10.0 [22], Array, Statsmodels.api: 0.13.5 [23], Sklearn: 1.2.1 [24]. Documentation done in Google Docs [29].…”

Section: Computational Instrumentsmentioning

confidence: 99%

Advancing Network Securing Strategies with Network Algorithms for Integrated Air Defense System (IADS) Missile Batteries

Pran¹

2023

Preprint

0

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Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

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Google Docs: A Review

Cited by 13 publications

References 0 publications

Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms

Advancing Network Securing Strategies with Network Algorithms for Integrated Air Defense System (IADS) Missile Batteries

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