Machine learning offers the ability to analyze large financial data and extract hidden knowledge for anomalous patterns. In the current study, we propose an ensemble machine learning framework using eXtreme Gradient Boosting (XGBoost) and random forest for the detection of financial frauds. The model used an adaptive synthetic (ADASYN) algorithm over sampling technique with out-of-bag scoring on a grid search algorithm for the 11 input features. We tested our framework with IEEE-CIS fraud detection benchmark dataset from Kaggle. The performance metrics were F1-Score $=$ 0.9982, area under curve $=$ 0.9999, recall $=$ 1.0, precision $=$ 0.9965, and accuracy $=$ 0.9999 A detailed comparison with state-of-the-art techniques, such as decision tree, logistic regression, gradient boosting, and particle swarm optimization models, shows scalability and robustness of the framework.

The objective of the paper is to identify predictive models in stock market prediction focusing on a scenario of the emerging markets. An exploratory analysis and conceptual modelling based on the extant literature during 1933 to 2020 have been used in the study. The databases of Web of Science, Scopus, and JSTOR ensure the reliability of the literature. Bibliometrics and scientometric techniques have been applied to the retrieved articles to create a conceptual framework by mapping interlinks and limitations in past studies. Focus of research is hybrid models that integrate big data, social media, and real-time streaming data. Key finding is that actual phenomena affecting stock market sectors are diverse and, hence, limited in generalization. The future research must focus on models empirically validated within the emerging markets. Such an approach will offer an insight to analysts and researchers, policymakers or regulators.

The study analyze stock index closing from myriad set of technical and fundamental analysis variables extracted from real market data to assist forecast of market closing. For this, major service sector indices of Bombay stock exchange (BSE) and Nati

The purpose of the paper is developing a common software based analytics framework that can be coupled to health devices which monitor health conditions using smart devices, mobile phones, tablets and gadgets. The diverse and heterogeneous sensor data generated can collectively be feed into local home based health information system. These information systems as a large group in cluster model form the basis of backbone networks for governmental health institutions and thereby societal impact as effect of such implementation can be studied as experimental basis of further research.

The telemetry data processing operation intended for a given mission are pre-defined by an onboard telemetry configuration, mission trajectory and overall telemetry methodology have stabilized lately for ISRO vehicles. The given problem on telemetry data processing is reduced through hierarchical problem reduction whereby the sequencing of operations evolves as the control task and operations on data as the function task. The function task Input, Output and execution criteria are captured into tables which are examined by the control task and then schedules when the function task when the criteria is being met.