The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.

Analytics of health big data are very crucial for providing cost effective quality health care. Over recent years, the analytics on healthcare big data has evolved into a challenging task for getting insights into a very large data set for improving the health services. This enormous amount of data, which is being generated incessantly over a long period of time, has put a great deal of stress on the write performance as well as on scalability. Moreover, there is a requirement of efficient storage and meaningful processing of these data which is an another challenging issue. The traditional relational databases, which were used in the storage of health data, are now unable to handle due to its massive and varied nature. Besides, these databases have some inherent weakness in terms of scalability, storing varied data format, etc. So there is a necessity for a new kind of data storage management system. This paper proposes a new big data storage architecture consisting of application cluster and a storage cluster to facilitate read/write/update speedup as well as data optimization. The application cluster is used to provide efficient storage and retrieval functions from the users. The storage services will be provided through the storage cluster.