A Deep Neural Network (DNN) is a type of artificial neural network with multiple layers between the input and output layers. These layers, also known as hidden layers, help the network learn complex features and patterns in the data. DNNs are a foundational element of deep learning and are used for tasks like image and speech recognition, natural language processing, and other complex computational tasks.
Researchers introduce a hybrid approach combining a multi-layer deep neural network (DNN) and the mountain gazelle optimizer (MGO) to accurately estimate the state of charge (SoC) in electric vehicle (EV) batteries. The proposed technique outperforms existing methods, offering superior accuracy and faster convergence, with the potential to optimize EV operations and extend battery lifespan.
Researchers have developed the PETAL sensor patch, a paper-like wearable device that incorporates five colorimetric sensors for comprehensive wound monitoring. With the aid of artificial intelligence and deep learning algorithms, the patch accurately classifies wound healing status, providing early warning for timely intervention and enhancing wound care management.
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