首页|De Montfort University Researchers Update Knowledge of Machine Learning (Revolut ionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions)
De Montfort University Researchers Update Knowledge of Machine Learning (Revolut ionising waste management with the impact of Long Short-Term Memory networks on recycling rate predictions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Leicester, United Kingdom, b y NewsRx journalists, research stated, “This study explores the efficacy of Long Short-Term Memory (LSTM) networks in predicting recycling rates and enhancing r esource allocation in waste management systems.” The news journalists obtained a quote from the research from De Montfort Univers ity: “It addresses the limitations of traditional statistical models and machine learning algorithms that struggle with sequential data and temporal dependencie s. The methodology comprised collecting extensive datasets from public repositor ies, configuring the LSTM network architecture, training the model with historic al data, and testing various activation functions and hyperparameters. The model ’s performance was rigorously compared to traditional models and alternative mac hine learning algorithms using metrics such as Mean Absolute Error (MAE), Root M ean Square Error (RMSE), and R-squared (R2). The findings demonstrate that the L STM model significantly outperformed traditional approaches, achieving an MAE of 3.5%, an RMSE of 2.8%, and an R2 of 0.92. These resul ts underscore the superior capability of LSTM networks to capture complex tempor al patterns in recycling data, offering substantial improvements in predictive a ccuracy and reliability.”
De Montfort UniversityLeicesterUnite d KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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