According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training conversational turn analysis neural networks. One of the methods includes obtaining unsupervised training data comprising a plurality of dialogue transcripts; training a turn prediction neural network to perform a turn prediction task on the unsupervised training data using unsupervised learning, wherein: the turn prediction neural network comprises (i) a turn encoder neural network and (ii) a turn decoder neural network; obtaining supervised training data; and training a supervised prediction neural network to perform a supervised prediction task on the supervised training data using supervised learning.

In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

Systems and methods of robotic process automation system for negotiation are disclosed. An example system for negotiating refinancing of a loan includes a data collection and monitoring circuit for collecting a training set of interactions between entities for at least one loan refinancing activity; an automated loan classification circuit that is trained on the training set of interactions to classify at least one loan refinancing action; and a robotic process automation circuit that is trained on a plurality of loan refinancing actions classified by the automated loan classification circuit and a plurality of loan refinancing outcomes to undertake a loan refinancing activity on behalf of a party to a loan.

Systems, apparatuses, and methods for automated data aggregation. In some embodiments, this is achieved by use of techniques such as natural language processing (NLP) and machine learning to enable the automation of data aggregation from websites without the use of pre-programmed scripts.