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Hyperspectral sensor generates huge datasets which conveys abundance of information. However, it poses many challenges in the analysis and interpretation of these data. Deep networks like VGG16, VGG19 are difficult to directly apply for hyperspectral image (HSI) classification because of its higher number of layers which in turn requires high level of system resources. This article suggests a novel framework with lesser number of layers for hyperspectral image classification (HSIC) that takes into account spectral-spatial context sensitivity of HSI, which focuses on enhancing the discriminating capability of HSIC. The model uses available spectral feature as well as spatial contexts of HSI and consecutively learn the distinctive features. A small training set has been used to optimize the network parameters while the overfitting problem is alleviated using the validation set. Regularization has been performed using batch normalization (BN) layer after each convolution layer. The cost of the model is measured in terms of training and testing time duration under the same platform, which has further been compared with some ensemble learning methods, SVM and other three recent state-of-the-art methods. Experimental results establish that the proposed model performs very well with the three benchmark datasets: Indian Pines, Salinas and University of Pavia, which mostly contain land cover of agriculture, forest, soil, rural, and urban area.

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Tuberculosis (T.B.) is a disease that occurs due to infection by the bacterium, Mycobacterium tuberculosis (Mtb), which is responsible for millions of deaths every year. Due to the emergence of multidrug and extensive drug-resistant Mtb strains, there is an urgent need to develop more powerful drugs for inclusion in the current tuberculosis treatment regime. In this study, 1778 molecules from four medicinal plants, Azadirachta indica, Camellia sinensis, Adhatoda vasica, and Ginkgo biloba, were selected and docked against two chosen drug targets, namely, Glutamine Synthetase (G.S.) and Isocitrate Lyase (I.C.L.). Molecular Docking was performed using the Glide module of the Schrӧdinger suite to identify the best-performing ligands; the complexes formed by the best-performing ligands were further investigated for their binding stability via Molecular Dynamics Simulation of 100 ns. The present study suggests that Azadiradione from Azadirachta indica possesses the potential to inhibit Glutamine Synthetase and Isocitrate Lyase of M. tuberculosis concomitantly. The excellent docking score of the ligand and the stability of receptor-ligand complexes, coupled with the complete pharmacokinetic profile of Azadiradione, support the proposal of the small molecule, Azadiradione as a novel antitubercular agent. Further, wet lab analysis of Azadiradione may lead to the possible discovery of a novel antitubercular drug.