Machine learning-based management of electric vehicles charging: Towards highly-dispersed fast chargers حسب Shibl, M.

"…LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. …"
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Compressive SensingBased Remote Monitoring Systems for IoT applications حسب Djelouat, Hamza

"…The high energy consumption decreases the sensor's lifespan immensely, hence, a severe degradation in the overall performance of the REMS platform. …"
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