A recent study conducted in Tamil Nadu, India, has focused on autism spectrum disorders (ASD) and their impact on behavior and communication abilities. The study highlights the increasing number of people being diagnosed with ASD and the high medical expenses associated with it. To address these challenges, the researchers propose a novel deep recurrent neural network algorithm for the detection of autism levels. The algorithm utilizes an artificial algae algorithm for feature extraction and an intelligent water droplet algorithm for obtaining optimal weights and biases. The experimental results show promising classification accuracy, sensitivity, and cost reduction. For more information, readers can refer to the research article published in the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. [Extracted from the article]