Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce
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DOI: 10.1371/journal.pone.0157551
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- Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
- Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
- Younes Saadi & Iwan Tri Riyadi Yanto & Tutut Herawan & Vimala Balakrishnan & Haruna Chiroma & Anhar Risnumawan, 2016. "Ringed Seal Search for Global Optimization via a Sensitive Search Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-31, January.
- Haruna Chiroma & Sameem Abdul-kareem & Abdullah Khan & Nazri Mohd Nawi & Abdulsalam Ya’u Gital & Liyana Shuib & Adamu I Abubakar & Muhammad Zubair Rahman & Tutut Herawan, 2015. "Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
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Cited by:
- Shikha Agarwal & Prabhat Ranjan, 2018. "MR-TP-QFPSO: map reduce two phases quantum fuzzy PSO for feature selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 888-900, August.
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