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Marble and limestone possess calcium carbonate (chemical formula: CaCo3) as major ingredient that’s why they are called carbonates or carbonated mineral. Carbonates are 70% of the total minerals’ deposits of the intended study area which are divulged and mapped using the significant deep learning neural network models and latest Sentinel-2 imagery. While delineating them, an overall accuracy of 96% for 1-dimensional convolution neural network and 95% for artificial neural network was achieved while targeted carbonates class accuracy remained 99% and 100% respectively. Sentinel-2 sensors record data in visible, Near infrared, and short wave infrared bands which are much appropriate to delineating carbonated minerals as they show greater absorption features in these bands. Sentinel-2 data was downloaded in Level-2 format and resampled to 10 meter spatial resolution using bilinear nearest neighbors algorithm. Significant amount of data polygons (2500+) were drawn and cleaned up for various class members in order to prepare them for various deep learning and machine learning models. Data was split in the ratio of 70:30 as training-test sets which provided with the most optimal mapping results. Classification and accuracy assessment reports of the models with high quality resultant imageries were overlaid in ArcGIS 10.2 and presented in article.

Big data is a challenging issue as its volume, shape, and size need to be modified in order to extract important information for a specific purpose. The amount of data is rising with the passage of time. This increase in volume can be a challenging issue to analyze the data for smooth industry and the Internet of things. Several tools, techniques, and mechanisms are available to support the handling and management process of such data. Decision support systems can be one of the important techniques which can support big data in order to make decisions on time. The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things. The study has used the tool of SuperDecisions to plot the hierarchy of situations of big data and scientific programming and to select the best alternative among the available.

Big data is ever playing an important role in the industry as well as many other organizations. With the passage of time, the volume of data is increasing. This increase will create huge bulk of data which needs proper tools and techniques to handle its management and organization. Different techniques and tools are being used to properly handle the management of data. A detailed report of these techniques and tools is needed which will help researchers to easily identify a tool for their data and take help to easily manage the data, organize the data, and extract meaningful information from it. The proposed study is an endeavour toward summarizing and identifying the tools and techniques for big data used in Industrial Internet of Things. This report will certainly help researchers and practitioners to easily use the tools and techniques for their need in an effective way.

Breast cancer afflicts more than one million women in the world each year. It is the second leading cause of death in cancer patients globally. It is the second most prevailing cancer following lungs cancer. Wavelet transformation and wavelet mathematical functions have proved extremely useful for processing digital signals and digital images in last few decades. In this research, breast cancer digital mammograms are processed through wavelet transforms for extracting useful features which can then be used to train classifier. This work shows that processing digital mammograms with the aid of multilevel wavelet transformation yields much better performance and can help radiologists identify and classify breast cancer with better accuracy.

Exposure of plants to long periods of water scarcity, mainly in arid and semi-arid regions, is one of the major reasons for over 50% reduction in average yields. Proper management of Brassica napus to enhance its ability to survive under drought and high temperature stress at early growth stages, besides development of tolerant genotypes, could improve its production in the rainfed areas. In the present study, we report the interactive effects of Ca 2+ , K + and N supplementation and water availability on the fresh biomass, N and C content, as well as C/N ratio of crop plants. Exposure to water stress significantly reduced the fresh biomass, Nitrogen (N), Carbon (C) content and the C/N ratio. Ca 2+ and K + supplementation before drought positively affected fresh biomass by stimulating N uptake and C assimilation. However, the C/N ratio was reduced after supplementation with Ca 2+ and K + . The N supplementation before drought imposition, though enhanced the N uptake, but the excessive damage to cell membranes and electrons leakage from Electron Transport Chain (ETC) during photosynthesis resulted in a decrease in C assimilation. Consequently, there was a decrease in the C/N ratio in seedlings exposed to drought after supplementation with N. Key words : Drought, nutrients supplementation, carbon, nitrogen, C/N ratio.