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Sema Atasever, Zafer Aydin, Hasan Erbay and Mostafa Sabzekar
Predicting the secondary structure from protein sequence plays a crucial role in estimating the 3D structure, which has applications in drug design and in understanding the function of proteins. As new genes and proteins are discovered, the large size of...
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Nitin patidar,Kushboo patidar
The management and analysis of big data has been recognized as one of the majority significant promising requirements in recent years. This is because of the pure volume and growing complexity of data creature created or composed. Existing clustering alg...
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Sina Shabani, Antonio Candelieri, Francesco Archetti and Gholamreza Naser
This article proposes a new general approach in short-term water demand forecasting based on a two-stage learning process that couples time-series clustering with gene expression programming (GEP). The approach was tested on the real life water demand da...
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You-Da Jhong, Chang-Shian Chen, Hsin-Ping Lin and Shien-Tsung Chen
This study proposed a hybrid neural network model that combines a self-organizing map (SOM) and back-propagation neural networks (BPNNs) to model the rainfall-runoff process in a physically interpretable manner and to accurately forecast typhoon floods. ...
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Ziyuan Gu, Meead Saberi, Majid Sarvi, Zhiyuan Liu
Pág. 901 - 921
Existing methods for calibrating link fundamental diagrams (FDs) often focus on a limited number of links and use grouping strategies that are largely dependent on roadway physical attributes alone. In this study, we propose a big data-driven two-stage c...
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