Computational intelligence in bacterial spore detection and identification

2004 ◽  
Author(s):  
Bruno Bosacchi ◽  
Manjusha Mehendale ◽  
Warren S. Warren ◽  
Herschel Rabitz ◽  
Marlan O. Scully
Keyword(s):  
Computational Intelligence ◽  
Bacterial Spore ◽  
Detection And Identification ◽  
Spore Detection

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

Bacterial Spore Detection and Determination by Use of Terbium Dipicolinate Photoluminescence

1997 ◽  
Vol 69 (6) ◽  
pp. 1082-1085 ◽  
Author(s):  
David L. Rosen ◽  
Charles Sharpless ◽  
Linda B. McGown
Keyword(s):  
Bacterial Spore ◽  
Spore Detection

In Situ Surface-Etched Bacterial Spore Detection Using Dipicolinic Acid–Europium–Silica Nanoparticle Bioreporters

2011 ◽  
Vol 65 (8) ◽  
pp. 866-875 ◽  
Author(s):  
Clint B. Smith ◽  
John E. Anderson ◽  
Jarrod D. Edwards ◽  
Kinson C. Kam
Keyword(s):  
Dipicolinic Acid ◽  
Silica Nanoparticle ◽  
Bacterial Spore ◽  
Spore Detection

scholarly journals Bacterial spore detection and analysis using hyperpolarized 129Xe chemical exchange saturation transfer (Hyper-CEST) NMR

2014 ◽  
Vol 5 (8) ◽  
pp. 3197-3203 ◽  
Author(s):  
Yubin Bai ◽  
Yanfei Wang ◽  
Mark Goulian ◽  
Adam Driks ◽  
Ivan J. Dmochowski
Keyword(s):  
Bacillus Subtilis ◽  
Nmr Spectroscopy ◽  
Bacillus Anthracis ◽  
Chemical Exchange ◽  
Bacterial Spore ◽  
Chemical Exchange Saturation Transfer ◽  
Saturation Transfer ◽  
Hyperpolarized 129Xe ◽  
Spore Detection

Hyper-CEST 129Xe NMR spectroscopy was employed to detect Bacillus anthracis and Bacillus subtilis spores in solution, and interrogate the layers that comprise their structures.

Semi-automated bacterial spore detection system with micro-fluidic chips for aerosol collection, spore treatment and ICAN DNA detection

2009 ◽  
Vol 24 (11) ◽  
pp. 3299-3305 ◽  
Author(s):  
Hisao Inami ◽  
Kouichiro Tsuge ◽  
Mitsuhiro Matsuzawa ◽  
Yasuhiko Sasaki ◽  
Shigenori Togashi ◽  
...  
Keyword(s):  
Detection System ◽  
Dna Detection ◽  
Bacterial Spore ◽  
Spore Detection

Bacterial Spore Detection by [Tb3+(macrocycle)(dipicolinate)] Luminescence

2007 ◽  
Vol 129 (6) ◽  
pp. 1474-1475 ◽  
Author(s):  
Morgan L. Cable ◽  
James P. Kirby ◽  
Karn Sorasaenee ◽  
Harry B. Gray ◽  
Adrian Ponce
Keyword(s):  
Bacterial Spore ◽  
Spore Detection

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

115: Detection and Identification of Bacterial DNA and HIV Gag in the Semen of HIV Infected Men

2004 ◽  
Vol 171 (4S) ◽  
pp. 30-30
Author(s):  
Robert C. Eyre ◽  
Ann A. Kiessling ◽  
Thomas E. Mullen ◽  
Rachel L. Kiessling
Keyword(s):  
Bacterial Dna ◽  
Detection And Identification
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