Horizons in Computer Science Research. Volume 19 (eBook, PDF)
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Horizons in Computer Science Research. Volume 19 first describes the principles, materials and processes in 2.5D printing and post-press enhancement based on the authors' research. This compilation goes on to discuss Hadoop, a pioneering open source framework that has revolutionized the big data world due to its ability to process vast amounts of unstructured and semi-structured data by distributing a massive amount of data across clusters. A new tool that uses general and query-based summarization to analyze students' log files at the Academic Center for Tutoring at Columbus State University,...
Horizons in Computer Science Research. Volume 19 first describes the principles, materials and processes in 2.5D printing and post-press enhancement based on the authors' research. This compilation goes on to discuss Hadoop, a pioneering open source framework that has revolutionized the big data world due to its ability to process vast amounts of unstructured and semi-structured data by distributing a massive amount of data across clusters. A new tool that uses general and query-based summarization to analyze students' log files at the Academic Center for Tutoring at Columbus State University, a small state university in Georgia, USA, is introduced. The concept of a Vehicle Accident Prediction System is presented using accident datasets based on actual vehicle accident data extracted from open-source datasets. The current problem of rapid data increase throughout the year is discussed, along with the available options for gaining useful insight from World Wide Web created data. A review of different methods for Audio-Visual Speech Recognition using Random Forest is presented, and a strategy based on the combination of Wavelet multiresolution analysis and Random Forest is proposed. The authors review six recent applications of autoencoders in cryo-EM and cryo-ET data analysis, discussing the autoencoders' strength and weaknesses to provide potential directions for future research. In closing, an algorithm is presented which uses ensembles of metamodels and classifiers to predict which candidate designs for computer simulations are expected to cause failures and to divert the search accordingly.
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