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Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

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dc.contributor.author Akash, Nazmus Sakib
dc.contributor.author Rouf, Shakir
dc.contributor.author Jahan, Sigma
dc.contributor.author Chowdhury, Amlan
dc.contributor.author Chakrabarty, Amitabha
dc.contributor.author Uddin, Jia
dc.date.accessioned 2024-02-27T11:39:28Z
dc.date.available 2024-02-27T11:39:28Z
dc.date.issued 2022-04-07
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11543
dc.description.abstract Raw starch degrading enzyme specially glucoamylase with starch binding domain (SBD) has great values in the starch processing industry because it digests the starch particles below the gelatinization temperature by releasing glucose from the non-reducing ends sequentially. The purpose of the study was to measure the secretion levels of recombinant glucoamylase from Pichia pastoris, by using the α-mating factor secretion signal peptide (α-MF) and the native signal peptide of glucoamylase from Aspergillus flavus NSH9. The Aspergillus flavus NSH9 gene (with and without native signal sequences), encoding a pH and thermostable glucoamylase with an SBD, was successfully cloned and expressed in Pichia pastoris to produce recombinant glucoamylases. The constructed recombinant plasmids pPICZB_GA2 (having a native signal peptides) and pPICZαC_GA2 (having the α-MF) were 5144 and 5356 bp in length respectively. Recombinant pichia having α-MF signal sequence (plasmid, pPICZαC_GA2) gave the highest level of secretions of recombinant glucoamylase after 6 days of incubation period with 0.5% methanol. In conclusion, yeast expression vector signal peptide is more efficient for heterologous expression/secretions of recombinant glucoamylase compared to its native signal sequences. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Factors influencing en_US
dc.subject Architecture en_US
dc.subject Influence of climate en_US
dc.title Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis en_US
dc.type Article en_US


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