Abstract: Systems, computer program products, and methods are described herein for improving computer energy consumption and computer processing by implementing a pluggable carbon emission gateway. The present disclosure is configured to identify, by a pluggable emission gateway, at least one application programming interface request; generate at least one transaction per second load attribute; identify at least one application consumption; determine at least one emission; identify an emission threshold for the at least one emission; compare the at least one emission to the emission threshold; apply, in an instance where the at least one emission associated with the at least one API request exceeds the emission threshold, at least one API parameter adjustment for the API request; and generate an updated application by applying the at least one API parameter adjustment to at least one of the at least one application or the at least one API request.

Abstract: Systems, methods, and apparatus are provided for generating dynamic metaverse accommodations using artificial intelligence. A user device may initiate a metaverse session. Deep learning networks may derive a user profile based on data from the user device and generate a set of samples from the user profile. The generated samples may include user characteristics and avatar features associated with the user characteristics. The generated samples may be used to train a multitask machine learning model. Deep learning networks may generate a metaverse accommodation targeted to the user profile. The accommodation may be rendered in the metaverse environment. The accommodation may be transmitted to an actuator at the user device. The accommodation may be stored in association with user characteristics in a distributed ledger.

Abstract: Systems, methods, and apparatus are provided for generating dynamic metaverse accommodations using artificial intelligence. A user device may initiate a metaverse session. Deep learning networks may derive a user profile based on data from the user device and generate a set of samples from the user profile. The generated samples may include user characteristics and avatar features associated with the user characteristics. The generated samples may be used to train a multitask machine learning model. Deep learning networks may generate a metaverse accommodation targeted to the user profile. The accommodation may be rendered in the metaverse environment. The accommodation may be transmitted to an actuator at the user device. The accommodation may be stored in association with user characteristics in a distributed ledger.

Abstract: Systems, computer program products, and methods are described herein for improving computer energy consumption and computer processing by implementing a pluggable carbon emission gateway. The present disclosure is configured to identify, by a pluggable emission gateway, at least one application programming interface request; generate at least one transaction per second load attribute; identify at least one application consumption; determine at least one emission; identify an emission threshold for the at least one emission; compare the at least one emission to the emission threshold; apply, in an instance where the at least one emission associated with the at least one API request exceeds the emission threshold, at least one API parameter adjustment for the API request; and generate an updated application by applying the at least one API parameter adjustment to at least one of the at least one application or the at least one API request.

Abstract: Systems and methods for predicting application failures using a hybrid neural network with multi-threaded inputs are provided. A method includes storing, in a first database, information relating to a plurality of digital applications, and storing, in a second database, information relating to historical performance issues associated with the plurality of digital applications. The method may include training the hybrid neural network according to the particulars disclosed herein. The method may also include detecting a trigger event relating to one of the plurality of digital applications, and via the particulars disclosed herein, using the hybrid neural network to output a set of predicted application failures.

Abstract: Systems, apparatus, methods, and computer program products are provided for managing the productivity and performance of a business' offsite service providers. Productivity is measured multi-dimensionally taking into account the unique business metrics that result in increased productivity. In addition, the framework herein provided can dynamically be adjusted over time at allow for different business metrics to be considered and/or assign a pre-determined level of importance to a business metric (i.e., proper weighting), resulting in a final productivity score that allows for comparison amongst service providers.