Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Improved Growth Pattern of Tiger Prawn (TP) Arthropoda in a Pond by Analytical Hierarchical Process (AHP)

Version 1 : Received: 8 March 2021 / Approved: 10 March 2021 / Online: 10 March 2021 (11:07:15 CET)

How to cite: Khan, A.A.; Ali, A. Improved Growth Pattern of Tiger Prawn (TP) Arthropoda in a Pond by Analytical Hierarchical Process (AHP). Preprints 2021, 2021030276 (doi: 10.20944/preprints202103.0276.v1). Khan, A.A.; Ali, A. Improved Growth Pattern of Tiger Prawn (TP) Arthropoda in a Pond by Analytical Hierarchical Process (AHP). Preprints 2021, 2021030276 (doi: 10.20944/preprints202103.0276.v1).

Abstract

Artificial intelligence (AI) is a versatile term that is a conclusive remedy to solve the problem using past rational data after deep contemplation using these terms i-e basic statistics, carving data, familiarity with common AI algorithms. Seafood especially tiger prawn export as a busi-ness will provide enormous foreign exchange to any country if the farmers overcome the corre-lated vulnerabilities in prawn farming. This research is elucidating lacking in Tiger prawn (TP) farming like curbing of Oxygen, pH, water temperature, and nutrients, etc. Moreover, hatchery statistics in terms of juveniles will depict this study's clear picture of curbed aquaculture. For normative decisions, the Analytical Hierarchical Process (AHP) is used. The problem which has been faced by local prawn farmers that there is a stagnant TP growth in ponds, the reason is the predominant sensitivity factor in TP. For this reason, they need indemnification of thirteen fac-tors with natural resources to get the plausible results to get calmness in their lives. This study will solely focus on the TP growth model, and the monitoring effect will be established by the Artificial Intelligence algorithm. This study will employ the AHP, 0-1 scaling method, data cura-tion techniques, and ecological statistics. The life of Tiger Prawn (TP) depends upon these factors mainly, a) Physical and b) Chemical parameters. Physical parameters contain environment (E) provided to TP like season (S) and temperature (T) etc. whereas the quality of Ammonia NH3 (N) from fish waste, Oxygen level (O), and water quality hard & soft (W) lies in chemicals do-main. This research will Elucidate the factors which cause conceptual muddles in the aquamarine life of TP, for this reason, Statistical tools will assess the current result, forecast the gap. AHP will analyze the domain inputs, circumspect ramification which will depict visceral factors, later results depict which pond suits the TP. In curtail, these factors will be curbed to improve the growth of TP in a control conditioned environment.

Subject Areas

Artificial Intelligent algorithms; Analytical Hierarchical Process (AHP); Prediction methods; unsupervised learning; Biological neural networks

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