Extensive growth in the volume of irregular brain cells is known as brain tumor. Human brain is surrounded by stiff skull. There are various issues that occur due to the growth of any tumor inside this restricted space. The malignant and benign are two main categories of the brain tumor. The skull is pressurized to enlarge from inside in case of growth of any benign or malignant tumor. This tumor leads to harm in brain and it may be dangerous to life also. The brain tumor is divided into two kinds - primary or secondary. The brain tumor detection techniques have various phases. In this paper, comparative study of CNN with GLCM approach and superpixel based spectral clustering is done tumor. This work takes into account metrics like accuracy, sensitivity and specificity for drawing the comparison between both the techniques.

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and latest heuristic approaches reported in the literature.

Background: Chronic Obstructive Pulmonary Disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) are chronic, progressive lung ailments which are characterized by distinct pathologies. Early detection biomarkers and disease mechanisms for these debilitating diseases are lacking. Exosomes are small extracellular vesicles attributed to carry proteins, mRNA, miRNA and sncRNA to facilitate cell-to-cell communication under normal and diseased conditions. Exosomal miRNAs have been studied in relation to many diseases. However, there is little to no knowledge regarding the miRNA population of BALF or the lung tissue derived exosomes in COPD and IPF. Here, we determined and compared the miRNA profiles of BALF and lung tissue-derived exosomes from healthy non-smokers, healthy smokers, and patients with COPD and IPF in independent cohorts. Results: Exosome characterization using NanoSight particle tracking and TEM demonstrated that the BALF-derived exosomes were approximately 89.85 nm in size and ~2.95 X 1010 particles/mL. Lung-derived exosomes were ~146.04 nm in size and ~2.38 X 1011 particles/mL. NGS results identified three differentially expressed miRNAs in the BALF, while one in the lung-derived exosomes from COPD patients as compared to healthy non-smokers. Of these, three- and five-fold downregulation of miR-122-5p amongst the lung tissue-derived exosomes from COPD patients as compared to healthy non-smokers and smokers, respectively. Interestingly, there were key 55 differentially expressed miRNAs in the lung tissue-derived exosomes of IPF patients compared to non-smoking controls. Conclusions: Overall, we identified specific miRNAs to develop as biomarkers or targets for pathogenesis of these chronic lung diseases.