Data analytics, the science of analyzing raw data is widely being used in a broad range of applications to makeeffective, efficient and timely decisions in the real-time prediction problems. Analytics requires the user to havean in-depth knowledge about the various steps involved in the process for arriving at the conclusions. Hence, tomake it easier for the naïve users, Data Analytics and Reporting API (DARAPI) provides Analytics as a Service inthe form of an Application Programming Interface (API) has been proposed. DARAPI is aimed to ease the processof analyzing the data by the users without requiring their expertise in the field. It accepts the input file from theuser and performs pre-processing techniques at various stages including imputation of missing data, detectionand replacement of outliers, encoding of categorical variables, and etc. Furthermore, feature engineering is performed, based on which DARAPI will execute the different classification/regression models and finally deliversthe model which provides the best accuracy for future predictions. This entire system is rendered to the user inthe form of an API that can be called from any device that is Internet enabled. DARAPI stands unique with its embedded feedback mechanism which generates constructive input for future predictions. This feature enhancesthe performance of the system in comparison with the existing tools. DARAPI proves beneficial not only to thenaïve users but also to the experts by saving their time and efforts needed in understanding the data.

Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people's lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

In today's digital environment, large volume of digital data is created daily and this data accumulates to unforeseen levels. Industries are finding it increasingly difficult to store data in an effective and trustworthy manner. Distributed storage appears to be the greatest approach for meeting current data storage demands at the moment. Furthermore, due to disc crashes or failures, efficient data recovery is becoming an issue. At present, new data storage techniques are required in order to restore data effectively even if some discs or servers are crashed. Hence, this proposed work aims to improve the storage efficiency and reliability in distributed storage systems (DSSs) to withstand disk failures and data erasures by modifying the degree distribution of Luby Transform (LT) codes. Recently, deriving an optimal degree distribution in LT codes becomes an upcoming research field. Hence, in this work, a novel approach called Lower Triangular Matrix (LTM) based degree distribution is presented for LT encoding. Unlike the traditional schemes such as Robust Soliton Distribution (RSD), there is no need for transmitting the neighbor symbols information along with each and every encoded symbol using LTM-LT codes which results in an efficient utilization of the bandwidth. Further, the overhead of LTM encoder is reduced by performing only one XOR operation for the generation of every new encoded symbol. The performance of LTM-LT codes is evaluated in terms of varying data erasure and disk failure conditions. Simulation results show that the proposed LTM-LT codes perform better compared to the traditional schemes such as RSD used for LT codes in Distributed Storage System.

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