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Your search keyword '"*SCIENTIFIC computing"' showing total 2 results
2 results on '"*SCIENTIFIC computing"'

1. Can we identify the similarity of courses in computer science?

Author

KARADAĞ, Tugay, PARIM, Coşkun, and BÜYÜKLÜ, Ali Hakan

Subjects
*COMPUTER science, *SCIENTIFIC computing, *ARTIFICIAL intelligence, *DEEP learning, *BIG data, *DATA mining
Abstract

Especially on the Internet, popular topics in computer sciences which are artificial intelligence, big data, business analytics, data mining, data science, deep learning, and machine learning have been compared or classified using confusing Venn diagrams without any scientific proof. Relationships among the topics have been visualized in this study with the help of Venn diagrams to add scientificity to visualizations. Therefore, this study aims to determine the interactions among the seven popular topics in computer sciences. Five books for each topic (35 books) were included in the analysis. To illustrate the interactions among these topics, the Latent Dirichlet Allocation (LDA) analysis, a topic modeling analysis method, was applied. Further, the pairwise correlation was applied to determine the relationships among the chosen topics. The LDA analysis produced expected results in differentiating the topics, and pairwise correlation results revealed that all the topics are related to each other and that it is challenging to differentiate between them. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

Author

Miriam A. M. Capretz, Luke Seewald, and Katarina Grolinger

Subjects
Big Data, Artificial Intelligence and Robotics, Computational complexity theory, Computer science, 020209 energy, Oxdata H2O, Big data, Computational Engineering, 02 engineering and technology, Power and Energy, Machine learning, computer.software_genre, Reduction (complexity), Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, local learning, business.industry, Deep learning, consumption prediction, deep learning, Energy consumption, Support vector machine, deep neural networks, local SVR, Data mining, Artificial intelligence, business, computer, Numerical Analysis and Scientific Computing, Energy (signal processing)
Abstract

In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is not a novel concept, it has great potential in the Big Data domain because it reduces computational complexity. The local SVR approach presented here is compared to traditional SVR and to deep neural networks with an H2O machine learning platform for Big Data. Local SVR outperformed both SVR and H2O deep learning in terms of prediction accuracy and computation time. Especially significant was the reduction in training time, local SVR training was an order of magnitude faster than SVR or H2O deep learning.

Published
2016

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