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Abstract-This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and
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The AI software provider usually works with the likes of Airbus and Honda to find the best engine or car prototype.
However, it s applied its AI to arguably more important matters – helping the British public create the best pancakes for Pancake Day today (Tuesday 16). We found over 800 recipes for American style fluffy pancakes, said the firm in a blog post. However, we needed recipes with enough positive and negative reviews to train the model to predict what amount of ingredients would give us the fluffiest pancakes. After elimination, we ended up with a dataset of 31 recipes with enough reviews to train a model to predict the right output.