Cosmetics consumers need to be aware of their skin type before purchasing products. Identifying skin types can be challenging, especially when they vary from oily to dry in different areas, with skin specialist providing more accurate results. In recent years, artificial intelligence and machine learning have been utilized across various fields, including medicine, to assist in identifying and predicting situations. This study developed a skin type classification model using a Convolutional Neural Networks (CNN) deep learning algorithms. The dataset consisted of normal, oily, and dry skin images, with 112 images for normal skin, 120 images for oily skin, and 97 images for dry skin. Image quality was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, with data augmentation by rotation applied to increase dataset variety, resulting in a total of 1,316 images. CNN architectures including MobileNet-V2, EfficientNet-V2, InceptionV2, and ResNet-V1 were optimized and evaluated. Findings showed that the EfficientNet-V2 architecture performed the best, achieving an accuracy of 91.55% with average loss of 22.74%. To further improve the model, hyperparameter tuning was conducted, resulting in an accuracy of 94.57% and a loss of 13.77%. The Model performance was validated using 10-fold cross-validation and tested on unseen data, achieving an accuracy of 89.70% with a loss of 21.68%.

It has been previously shown using synthetic data that dispersion tracking with particle filtering can be used for sediment sound speed inversion. Here, dispersion tracking is performed with data collected in the Gulf of Mexico for sediment sound speed and thickness and water column depth estimation. In this experiment, sound that propagates a long distance from the source allows the identification of dispersion curves reflecting the different group velocities of modal frequencies within and across modes. Although the data are noisy, dispersion curves are tracked with sequential filtering and used for inversion. Probability density functions of the three unknown parameters are obtained. Water column depth is estimated with little uncertainty. The estimated sound speed is representative of sandy sediment and the sediment thickness matches to a large extent prior knowledge.