• Journal of Microbiology and Biotechnology
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• v.26 no.1
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• pp.197-206
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• 2016

Listeria monocytogenes is a foodborne pathogen of considerable genetic diversity with varying pathogenicity. Initially, we found that the strain M7 was far less pathogenic than the strain Lm850658 though both are serovar 4a strains belonging to the lineage III. Comparative genomic approaches were then attempted to decipher the genetic basis that might govern the strain-dependent pathotypes. There are 2,761 coding sequences of 100% nucleotide identity between the two strains, accounting for 95.7% of the total genes in Lm850658 and 92.7% in M7. Lm850658 contains 33 specific genes, including a novel 20K prophage whereas strain M7 has 130 specific genes, including two large prophages (38K and 44K). To examine the roles of these specific prophages in pathogenicity, the 20K and 38K prophages were deleted from their respective strains. There were virtually no differences of pathogenicity between the deletion mutants and their parent strains, although some putative virulent factors like VirB4 are present in the 20K region or holin-lysin in the 38K region. In silico PCR analysis of 29 listeria genomes show that only strain SLCC2540 has the same 18 bp integration hotspot as Lm850658, whereas the sequence identity of their 20K prophages is very low (21.3%). The 38K and 44K prophages are located in two other different hotspots and are conserved in low virulent strains M7, HCC23, and L99. In conclusion, the 20K and 38K prophages of L. monocytogenes serovar 4a strains Lm850658 and M7 are not related to virulence but contribute to genetic diversity.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.143-148
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• 2008

A real-time face detection is to find human faces robustly under the cluttered background free from the effect of occlusion by other objects or various lightening conditions. We propose a face detection system for real-time applications using wavelet decomposition method based on Gabor features. Firstly, skin candidate regions are extracted from the given image by skin color filtering and projection method. Then Gabor-feature based template matching is performed to choose face cadidate from the skin candidate regions. The chosen face candidate region is transformed into 2-level wavelet decomposition images, from which feature vectors are extracted for classification. Based on the extracted feature vectors, the face candidate region is finally classified into either face or nonface class by the Levenberg-Marguardt back-propagation neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4367-4385
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• 2016

With millions of people across the globe suffering from Parkinson's disease (PD), an objective, confirmatory test for the same is yet to be developed. This research aims to develop a system which can assist the doctor in objectively saying whether the patient is normal or under risk of PD. The proposed work combines the eye-hand co-ordination behaviour with the DaTscan images in order to determine the risk of this disorder. Initially, eye-hand coordination level of the patient is assessed through a hardware module. Then, the DaTscan image is analysed and used to extract certain geometrical parameters which shall indicate the presence of PD. These parameters are then finally fed into a Multi-Layer Perceptron Neural Network using Levenberg-Marquardt (LM) Back propagation training algorithm. Experimental results indicate that the proposed system exhibits an accuracy of around 93%.

• Computers and Concrete
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• v.20 no.6
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.