Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer.

Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage. Radiologists have great difficulty identifying and categorizing lung cancers in the initial phase. So, several methods were used to predict the lung cancer but does not provide accurate solutions with increased error rate. To overcome these issues, a Deep Volcanic Residual U-Net (DVR U-Net) for nodal metastasis is proposed in this manuscript which identifies the LC accurately in the early stage. Initially, the input images are taken from two datasets. After that, these input data are pre-processed using Anisotropic Diffusion Filter with a Fuzzy based Contrast-Limited Adaptive Histogram Equalization (ADFFCLAHE) method. Then the pre-processed images are given to the DVR U-Net to segment and extract the volume of interest for estimating the nodal stage of each volume of interest. Finally, DVR U-Net effectively detects and classifies the N + (nodal metastasis) or N- (non-nodal metastasis). The introduced method attains 99.9% higher accuracy as compared with the existing methods. Also, the statistical analysis of the Shapiro-Wilk test, Friedman test and Wilcoxon Signed-Rank test are executed to prove the statistical effectiveness of the implemented method.

Moving object detection for video surveillance.

The emergence of video surveillance is the most promising solution for people living independently in their home. Recently several contributions for video surveillance have been proposed. However, a robust video surveillance algorithm is still a challenging task because of illumination changes, rapid variations in target appearance, similar nontarget objects in background, and occlusions. In this paper, a novel approach of object detection for video surveillance is presented. The proposed algorithm consists of various steps including video compression, object detection, and object localization. In video compression, the input video frames are compressed with the help of two-dimensional discrete cosine transform (2D DCT) to achieve less storage requirements. In object detection, key feature points are detected by computing the statistical correlation and the matching feature points are classified into foreground and background based on the Bayesian rule. Finally, the foreground feature points are localized in successive video frames by embedding the maximum likelihood feature points over the input video frames. Various frame based surveillance metrics are employed to evaluate the proposed approach. Experimental results and comparative study clearly depict the effectiveness of the proposed approach.

Green synthesis of silver nanoparticles from the extract of the inflorescence of Cocos nucifera (Family: Arecaceae) for enhanced antibacterial activity.

Green synthesis of nanoparticles using plant source has been given much importance. In the present study, silver nanoparticles (AgNPs) were synthesized using the ethyl acetate and methanol (EA: M 40:60) extracts of the inflorescence of the tree Cocous nucifera. The synthesized nanoparticles were characterized by UV-visible spectroscope, FTIR and TEM analysis. The particle size of the synthesized AgNPs was 22nm as confirmed by TEM. The qualitative assessment of reducing potential of the extracts of inflorescence indicated the presence of reducing agents. Synthesized AgNPs exhibited significant antimicrobial activity against human bacterial pathogens viz., Klebsiella pneumoniae, Bacillus subtilis, Pseudomonas aeruginosa and Salmonella paratyphi.