首页|Reports from Umm Al-Qura University Describe Recent Advances in Machine Learning (From data to durability: Evaluating conventional and optimized machine learnin g techniques for battery health assessment)
Reports from Umm Al-Qura University Describe Recent Advances in Machine Learning (From data to durability: Evaluating conventional and optimized machine learnin g techniques for battery health assessment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from M ecca, Saudi Arabia, by NewsRx correspondents, research stated, “In the electroni c era, the demand for efficient storage systems has rapidly increased, making th e health and durability of batteries crucial. This research investigates the per formance of distinct Machine Learning (ML) techniques-namely, Logistic Regressio n (LR), Convolutional Neural Network (CNN), and CNN performance tuning using Par ticle Swarm Optimization (PSO)-for Battery Health Analysis (BHA).” The news reporters obtained a quote from the research from Umm Al-Qura Universit y: “The dataset comprises various parameters related to battery health, with Rem aining Useful Time (RUL) as the target variable. The proposed work is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared ( R2) scores. Initially, the basic LR Model is employed for BHA, followed by the C NN Model to capture complex data patterns. Subsequently, the CNN Model’s perform ance is optimized using the PSO algorithm, aiming for improved performance. Expe rimental results demonstrate that the CNN Model significantly outperforms the LR approach in terms of accuracy, lower RMSE and MAE, and higher R2 scores. The co nventional CNN model significantly outperformed the LR approach, resulting a low er RMSE of 20.11, MAE of 15.26, and higher R2 score of 0.996; whereas, the PSO-O ptimized-CNN further enhanced the performance metrics with RMSE of 14.97, MAE of 8.03 and R2 score of 0.998.”
Umm Al-Qura UniversityMeccaSaudi Ara biaAsiaCyborgsEmerging TechnologiesMachine Learning
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