Data Mining Techniques for Analysing Prediction of Time Series Data in Stock Trading by Using Big Data Analytics

Authors

  • Peta Mahesh  IT Department, Guru Nanak Institutions Technical Campus, Hyderabad, India
  • Gade Shivaram Reddy  IT Department, Guru Nanak Institutions Technical Campus, Hyderabad, India

DOI:

https://doi.org//10.32628/IJSRST2215112

Keywords:

Heavy Metal, In Vitro culture, Soil, Lantana camara, M. S. Media, NAA, BAP, Kinetin.

Abstract

This research was designed to study the metal stress on plant species, Lantana camara in culture. Plant Tissue Culture techniques are used as a tool, to study the growth of the plants in presence of the heavy metal Lead. Lantana camara is generally known as notorious weed. Indian region have been invaded by several exotic plants of which Lantana camara is of more concern, because of its rapid spread, intensity of infestation, and resistance to cutting and burning. Lantana is a native of tropical America, and was introduced to India as an ornamental to be planted in gardens and hedges. Since then, the species has spread rapidly into both farm and forest lands, and is one of the most widespread, terrestrial invasive species in India today. Despite of this Lantana camara has immense potential of phytoremediation of heavy metals. The industrial area Govindpura, Bhopal contains this plant in majority at heavy metal contaminated area, thus the study has been carried out to evaluate the response of Lantana camara towards lead. The cultures of Lantana camara were firstly established on M S Media supplemented with BAP, NAA and Kinetin and the stable in vitro plantlets then used for further study. The effect of heavy metals on Lantana camara plants under various concentrations of Lead was studied using in vitro culture. The tissue culture experiments were conducted using M S Media.

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Published

2022-12-30

Issue

Section

Research Articles

How to Cite

[1]
Peta Mahesh, Gade Shivaram Reddy, " Data Mining Techniques for Analysing Prediction of Time Series Data in Stock Trading by Using Big Data Analytics, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 6, pp.521-530, November-December-2022. Available at doi : https://doi.org/10.32628/IJSRST2215112