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Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach
Author(s) -
Pravin R. Kshirsagar,
Hariprasath Manaoharan,
Vineet Tirth,
Saiful Islam,
Sandeep Srivastava,
Varsha Sahni,
M. Thangamani,
Milind Khanapurkar,
Venkatesa Prabhu Sundramurthy
Publication year - 2022
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2022/2345600
Subject(s) - computer science , preprocessor , set (abstract data type) , whale , underwater , fish <actinopterygii> , real time computing , data mining , optimization algorithm , operations research , artificial intelligence , mathematical optimization , fishery , engineering , mathematics , oceanography , biology , programming language , geology
This article examines distinctive techniques for monitoring the condition of fishes in underwater and also provides tranquil procedures after catching the fishes. Once the fishes are hooked, two different techniques that are explicitly designed for smoking and drying are implemented for saving the time of fish suppliers. Existing methods do not focus on the optimization algorithms for solving this issue. When considering the optimization problem, the solution is adequate for any number of inputs at time t. For this combined new flanged technique, a precise system model has been designed and incorporated with a set of rules using contention protocols. In addition, the designed system is also instigated with a whale optimization algorithm that is having sufficient capability to test the different parameters of assimilated sensing devices using different sensors. Further to test the effectiveness of the proposed method, an online monitoring system has been presented that can monitor and in turn provides the consequences using a simulation model for better understanding. Moreover, after examining the simulation results under three different scenarios, it has been observed that the proposed method provides an enhancement in real-time monitoring systems for an average of 78%.

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