Data-Driven with IoT Sensing and Deep Learning Model for Dynamic Skin Cancer Diagnosis

This research introduces an innovative approach to continuously assess the condition of the skin barrier by combining advanced deep learning algorithms with Internet of Things (IoT) sensor technologies. For the system to function, it constantly uses IoT sensors to collect real-time data from various skin metrics, including moisture levels, pH balance, and environmental conditions. A complex analysis, made possible by deep learning, may be built using this vast amount of information. A type of deep learning technique that works well with data, recurrent neural networks (RNNs) are at the heart of this system. RNNs capture subtle changes over time in skin health by capitalizing on the temporal dependsequentialencies in skin data. Training RNNs iteratively on historical data covering a wide range of skin types and conditions allows them to detect patterns indicating healthy and damaged skin barriers. The proposed system has several benefits, such as individualized skincare suggestions, real-time information to guide adaptive skincare regimens, and continuous monitoring to identify skin concerns early. The system can discover complex patterns and correlations in the skin data, which results in better skincare solutions.