Abstract: The disclosed computer-implemented method includes normalizing tabular data corresponding to a query target, and inputting the normalized tabular data into a shallow neural network corresponding to a fully-connected three-layer model comprising an input layer, a hidden layer, and an output layer. Normalizing the tabular data may replace feature detection for the shallow neural network. The method may further include predicting a plurality of classifications for the query target, wherein a plurality of nodes of the output layer respectively correspond to the plurality of classifications. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: There are provided systems and methods for intelligent forecasting with limited data availability utilizing embeddings from auto-encoders and machine learning models. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users. In order to provide actionable insights into users, accounts, and/or activities associated with the service provider, such as to provide computing or other services to users, the service provider may utilize DNNs and other ML models that are trained for forecasting. The models may be trained by encoding vectors from initial training data using an encoder having an embedding, attention, and LSTM layer, which may retain temporal aspects to data for users or groups that have limited past data availability. Once trained, the models may be used to determine risk and/or engagement scores of users, which may predict or forecast users' future actions to offer services to the users.

Abstract: The present invention discloses an electrochemical system and process for the reduction of nitric acid concentration present in industrial waste using electrolytic cell. The present invention further relates to an electrochemical process for the reduction of nitric acid concentration using an electrolytic cell useful in reducing the volume of the high level liquid waste solution (HLLW) and avoiding corrosion of the storage tanks for high level liquid waste solution (HLLW).