Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Recent Advances in Internet of Things and Emerging Social Internet of Things: Vision, Challenges and Trends

In recent years, the Internet of Things (IoT), together with its related emerging technologies, has been driving a revolution in the way people perceive and interact with the surrounding environment [...

A Machine Learning Framework to Predict Determinant Factors of Seeds

In this paper, we audit the machine learning apparatuses for foreseeing determinant components of seeds. We depict this issue regarding Big Data, ANN, Hadoop and R. We consider Machine-learning techniques especially suited to forecasts dependent on existing information, yet exact expectations about the far off future are frequently on a very basic level unthinkable. Farming is an industry where recorded and current information flourish. This survey researches the various information sources accessible in the horticultural field and dissects them for utilization in Seed determinant factor Predictions. We recognized certain relevant information and researched techniques for utilizing this information to improve forecast inside the farming action