Machine Learning with IoT Enhancing Car Performance through Supervised Algorithms for Vehicle Automation
Keywords:
vehicle automation, supervised learning algorithms, Internet of Things (IoT), car performance enhancement, machine learningAbstract
The present research explores the combined application of supervised learning algorithms and the Internet of Things (IoT) to improve automotive performance in the context of vehicle automation. Our study makes use of neural networks, decision trees, and support vector machines along with a variety of datasets, well-placed sensors, and communication protocols. Across ten trials, the selected algorithms consistently displayed excellent performance, generating accuracy values ranging from 91.7% to 93.5%, precision values between 93.7% and 94.8%, recall values spanning from 89.8% to 91.7%, and F1 scores ranging between 91.5% and 93.4%. These striking results underline the potential of this integrated strategy to transform driving experiences, increase safety, and contribute to the continued growth of intelligent vehicle systems. This research not only lays the framework for new developments in the automotive sector but also demonstrates the revolutionary impact of advanced technology on the landscape of modern transportation.
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