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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.

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.