Abstract: Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

Abstract: Systems, computer program products, and methods are described herein for continuous cognitive code logic detection and prediction using machine learning techniques. The present invention is configured to receive, from a user input device, source code scripts and target code scripts for functional code logic components of a full stack, wherein the source code scripts and the target code scripts are associated with one or more tiers; generate a training dataset based on at least the source code scripts, the target code scripts, and the functional code logic components of the full stack; train, using a machine learning algorithm, a machine learning model using the training dataset; determine a prediction accuracy associated with the machine learning model; determine that the prediction accuracy is greater than a predetermined threshold; and deploy the machine learning model on unseen source code scripts.

Abstract: Systems, methods, and apparatus are provided for system mapping based on processing of custom network packets. A token processor may deploy a network agent to a system of record (SOR). The network agent may scan the SOR and extract SOR metadata through a set of API calls. The network agent may aggregate the SOR data and generate a token according to parameters established by the token processor. The token processor may process the token through a neural network to generate a set of output vectors for the SOR. A system mapping engine may determine relationships between network SORs based on the output vectors and generate an integrated visual representation of enterprise systems. The output vectors may be adjusted based on new tokens received from the SOR and the visual representation may be updated in real time.

Abstract: A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.

Abstract: A system is configured for determining a technology stack in a software application to perform a work project. The system receives and evaluates the work based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance of each technology stack is evaluated. The system identifies a first technology stack performing at a level higher than a performance threshold and at a highest performance level among the plurality of technology stacks. The system deploys the first technology stack in the software application to perform the work project.

Abstract: A system is configured for determining a technology stack in a software application to perform a work project. The system receives the work project from a user. The work project is evaluated based on its characteristics. A plurality of technology stacks is generated by implementing different combinations of technology stack components. The technology stack components include application servers and webservers. Each of the technology stacks is simulated performing the work project. Based on the simulation results of each technology stack, a performance value corresponding to a success rate of performing the work project by the software application. The system identifies a first technology stack having a first performance value that is the highest performance value and above a performance threshold value. The system deploys the first technology stack in the software application to perform the work project.