首页|Zhejiang Normal University Researchers Reveal New Findings on Machine Learning ( Optimized quantum LSTM using modified electric Eel foraging optimization for rea l-world intelligence engineering systems)
Zhejiang Normal University Researchers Reveal New Findings on Machine Learning ( Optimized quantum LSTM using modified electric Eel foraging optimization for rea l-world intelligence engineering systems)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Jinhua, Pe ople's Republic of China, by NewsRx correspondents, research stated, "The integr ation of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This a malgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitatin g enhanced convergence rates and solution quality in complex problem spaces." Funders for this research include King Saud University. Our news correspondents obtained a quote from the research from Zhejiang Normal University: "The Quantum Long Short-Term Memory (QLSTM) emerges as a highly effi cient deep learning model tailored to tackle such intricate engineering problems . The QLSTM's architecture, comprising data encoding, variational, and quantum m easurement layers, facilitates the effective encoding and processing of civil en gineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its N P-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability o f the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its predi ction performance."
Zhejiang Normal UniversityJinhuaPeop le's Republic of ChinaAsiaCyborgsEmerging TechnologiesEngineeringMachi ne Learning
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