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.

According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

The disclosure relates to method and system for determining correctness of predictions performed by deep learning model. The method includes extracting a neuron activation pattern of a layer of the deep learning model with respect to the input data, and generating an activation vector based on the extracted neuron activation pattern. The method further includes determining the correctness of the prediction performed by the deep learning model with respect to the input data using a prediction validation model and based on the activation vector. The prediction validation model is a machine learning model that has been generated and trained using training activation vectors derived from correctly predicted test dataset and incorrectly predicted test dataset of the deep learning model. The method further includes providing the correctness of the prediction performed by the deep learning model with respect to the input data for subsequent rendering or subsequent processing.