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International Journal of Fisheries and Aquatic Studies
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E-ISSN: 2347-5129, P-ISSN: 2394-0506

International Journal of Fisheries and Aquatic Studies

2022, Vol. 10, Issue 3, Part B

Ecotechnological relations between aquatic-microbes & turbidity with machine learning techniques


Author(s): Debabrata Das and Aranya Das

Abstract: Ecotechnology may be a never-ending applied science to all mankind. Often applied and we all may know that in recent days Machine learning Techniques can be used in Ecotechnology for mankind in obtaining significant relations. Present research communication dealt with long-run fisheries research when we find that cation exchange capacity viz. CEC and total dissolved solids viz. TDS have roles in fisheries in controlling fish diseases hence also growth and fecundity. There can be no fish diseases often in waters may consisting a minimum to below 190 ppm of TDS or below 50 meq of colloidal CEC. The present communication dealt that water microbes have definite correlations with turbidity mentioned and this is controlled by other environmental measures TDS and CEC found with artificial intelligence viz.AI and Machine Learning Techniques.

DOI: 10.22271/fish.2022.v10.i3b.2676

Pages: 101-105  |  81 Views  13 Downloads

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How to cite this article:
Debabrata Das, Aranya Das. Ecotechnological relations between aquatic-microbes & turbidity with machine learning techniques. Int J Fish Aquat Stud 2022;10(3):101-105. DOI: https://doi.org/10.22271/fish.2022.v10.i3b.2676
International Journal of Fisheries and Aquatic Studies