Auto-Encoder Classification Model for Water Crystals with Fine-Tuning

Abstract

Water is one of the important, though scarce, resources on earth. The 2021 World Water Resource Report claims that environmental challenges threaten the sustainability of water resources. Therefore, it is vital to screen water quality to sustain water resources. Water quality is related to water crystal structure in its solid state. Intelligent models classify water crystals to predict their quality. Methods to analyze water crystals can aid in predicting water quality. Therefore, the major contribution of our research is the prediction of water crystal classes. The proposed model analyzes water crystals in solid states, employing image analysis and the deep learning method. The model specifies several feature groups, including crystal shape factors, solid-state features, crystal geometry and discrete cosine transform coefficients. The model utilizes feature fusion for better training. The proposed model utilized the EP water crystal dataset from the WC image depository and its accuracy was tested with the multi-feature Validation technique. The nature of our data inclined us to utilize F-Measure and sensitivity for the testing phase. Our proposed model outperformed other state of the art water crystal classification models by more than 6% in accuracy and 7% in f-measures, with performance exceeding 11% for triple feature fusion. Furthermore, our model was faster in training time (10% of the training time of the comparative models) and had 1.42 s classification time.