Advanced Insights in Nursing Science: Applying ARIMA-CLSTM for Concept Analysis

The phrase "nursing science" extends much beyond the theoretical underpinnings of topics like asepsis and body mechanics. This lecture takes a classical Aristotelian stance in its examination of scientific concepts, natural phenomena, scientific knowledge, and the procedures of inquiry and proof. After that, it will show how the science is useful by applying traditional approaches to nursing. Preprocessing, feature extraction, and model training must proceed without pauses. A part of preparing data is making sure it is normalized and removing stop words. The GLCM is utilized for feature extraction; however, while it provides a large amount of data, it is not optimal for picture feature extraction, image recognition, or classification. It is necessary to extract features before training an ARIMA-CLSTM model. The suggested method outperforms the state-of-the-art algorithms using the current data. A considerable improvement was noted in terms of accuracy, with the findings reaching 97.33%.