CNN for bacteria and archaea classification using FCGR images DOI

Nadia Selmi,

Zeineb Chebbi Babchia,

Afef Elloumi Oueslati

et al.

Published: July 15, 2022

Prokaryotes, which comprise both bacteria and archaea, are found everywhere around us. Their detecting, counting, classification is still a hard matter. This paper's main aim the prokaryotes using frequency chaos representation (FCGR) image convolutional neural networks (CNN). First, we mapped each archaebacterial DNA sequence by FCGR images with different orders. Next, apply binary CNN technique. Our model has shown precision that exceeds 92%. result shows proposed method's performance.

Language: Английский

Magnetic Nanoparticles Modified With di(Pyridin-2-yl)Amine Ligand Supported Copper Complex: A Novel and Efficient Magnetically Reusable Catalyst for A 3 Coupling and C-S Cross-Coupling Reactions DOI

Ahmad Azhar Mansoor Al Sarraf,

Raed Obaid Saleh, Mustafa Z. Mahmoud

et al.

Polycyclic aromatic compounds, Journal Year: 2022, Volume and Issue: 43(5), P. 4407 - 4425

Published: June 28, 2022

A well-dispersed magnetically separable copper nanocatalyst via the immobilization of (II) complex on surface silica-coated magnetic nanoparticles functionalized with di(pyridin-2-yl)amine as ligand. After fabrication Fe3O4@SiO2-di(pyridin-2-yl)amine-Cu, structure this nanomaterial was fully characterized by a number spectroscopic techniques including FT-IR, SEM, TEM, EDS, XRD, TGA,VSM, AAS, and ICP-AES. This nanocomposite found to be an efficient versatile catalyst for A3-synthesis propargylamines C-S cross-coupling thiophenols aryl iodides. The presence Fe3O4@SiO2-di(pyridin-2-yl)amine-Cu very vital because reactions failed in absence nanomaterial. reaction, easily separated external effect reused several runs consistent catalytic activity without any detectable leaching.

Language: Английский

Citations

2
CNN for bacteria and archaea classification using FCGR images DOI

Nadia Selmi,

Zeineb Chebbi Babchia,

Afef Elloumi Oueslati

et al.

Published: July 15, 2022

Prokaryotes, which comprise both bacteria and archaea, are found everywhere around us. Their detecting, counting, classification is still a hard matter. This paper's main aim the prokaryotes using frequency chaos representation (FCGR) image convolutional neural networks (CNN). First, we mapped each archaebacterial DNA sequence by FCGR images with different orders. Next, apply binary CNN technique. Our model has shown precision that exceeds 92%. result shows proposed method's performance.

Language: Английский

Citations

1