Retracted: Blockchain‐based covert software information transmission for bitcoin

Reis, Marcello Carvalho dos; Barbosa, Paulo C. S.; Albuquerque, Victor Hugo C. de; Kautish, Sandeep

doi:10.1049/sfw2.12120

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Shanmuganathan et al 45 presented a software‐based analysis of the opinions of persons about the service and goods supplied by the healthcare sector. Dhiman et al 46 presented a tool for secured data transmission from source to receiver. Nayak et al 47 described the success rate of Particle Swarm Optimization (PSO) in dealing with real‐life problems.…”

Section: Introductionmentioning

confidence: 99%

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

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In this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

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Section: Introductionmentioning

confidence: 99%

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

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A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

Alam

Xin

Niazi

et al. 2023

Decision Analytics Journal

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Node Multi-Attribute Network Community Healthcare Detection Based on Graphical Matrix Factorization

Alferaidi,

Yadav,

Yasmeen

et al. 2023

J CIRCUIT SYST COMP

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The significance of community structure in complex networks, such as social, biological, and online networks, has been widely recognized. Detecting communities in social media networks typically relies on two sources of information: the network’s topological structure and node attributes. Incorporating rich node content attribute information poses both flexibility and challenges for community detection. Traditional approaches either focus on mining one information source or linearly combining results from both sources, which fails to effectively fuse the information. This paper introduces a practical collaborative learning approach that explores the multi-dimensional attribute characteristics of nodes to facilitate community division. By leveraging graphical matrix decomposition, the proposed algorithm, CDGMF, improves the effectiveness and robustness of community detection. Experimental results demonstrate the method’s ability to effectively utilize node attribute information for guiding community detection, resulting in higher-quality community divisions.

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Retracted: Blockchain‐based covert software information transmission for bitcoin

Cited by 3 publications

References 38 publications

An enhanced arithmetic optimization algorithm for optimal control of reactive power

An enhanced arithmetic optimization algorithm for optimal control of reactive power

A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

Node Multi-Attribute Network Community Healthcare Detection Based on Graphical Matrix Factorization

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