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A massive bulk of data is being created due to digitalisation in various industries, including medical, manufacturing, sales, internet of things (IoT) devices, the web, and businesses. To find data patterns for data attributes machine learning (ML) algorithms are used. In this fast-growing world, we can see that data is generated in abundance by people, machines, and corporations. With the increase in computer science market, researchers are integrating heterogeneous and diverse data into accurate patterns by applying machine learning algorithms and complex strategies on data sets. The overabundance of high-dimensional big data has made it more difficult for scientists to extract important information from these data efficiently. Conventional data mining approaches are ineffective when dealing with large amounts of data. As big data increase exponentially, predictive analytics has become widely known. To evaluate a large number of data patterns, data driven technology predictive big data analytics (PBA) can be used and ML algorithms to investigate the present and future data based on the records of data patterns. In this research paper, predictive analysis on big data has been proposed using the splitting random forest (SRF) methodology with help of hyperparameter optimization and dimension reduction technique.

Wind tunnel tests were carried out to characterize the RAE 2822 supercritical airfoil and implement an active flow control technique. Tests were carried out at various subsonic and transonic Mach numbers and angles of attack. Two load cells connected to the airfoil ends along the quarter chord axis were used to quantify the aerodynamic forces acting on the airfoil. The transonic airfoil was integrated, and the control technique successfully implemented at the Florida State University Polysonic wind tunnel. The paper presents a few preliminary experimental results and describes the lessons learned during the implementation process. Oil flow visualizations revealed the presence of corner vortices on the airfoil suction surface and wedge-like patterns on the lower surface, which indicates a combination of localized regions of transitional and turbulent flow with no shocks or very weak shocks. The measured lift coefficient on the baseline airfoil is much lower than the estimated value based on literature. These results indicate that the airfoil tested need to be modified both regarding its aspect ratio and cross-sectional area to suit the facility. The active flow control technique based on co-flow jet show promise in the improvement of aerodynamic performance.

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Underwater acoustic sensor networks plays vital role in many applications such as monitoring underwater environment, location and object detection etc., In environment monitoring, the sensor nodes monitors the unwanted obstacles and transmits the information about the obstacles to Base Station(BS). The major problem is when sending data from two or more number of sensor nodes at same time the time delay in data transmission will be increased. In this project, to overcome the problem of end to end delay, mobile data collector is employed with waterproof ultrasonic sensor nodes in underwater acoustic Sensor network.

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