Abstract: A system is provided for detecting multi-dimensional linkage and layering of resource transfers using a distributed server network. In particular, the system may comprise a plurality of distributed server nodes that each host a copy of a distributed register, where each of the nodes may be operated by an entity. Each distributed server node may submit, to the distributed register, data records that may contain data regarding potential unauthorized users, accounts, and/or resource transfers. Based on the information within the distributed register, along with various other data inputs, the system may use a deep learning-based graph processing algorithm to identify a multi-dimensional linkage between the users, accounts, and/or resource transfers to extract hidden relationships and potentially unauthorized activity.

Abstract: Systems, methods, and apparatus are provided for integrating access to third-party data using DNA computing. Requests for third-party data may be received from a plurality of applications. The request structure may be tagged with an NFT, linking the request to the originating application. DNA strands may be synthesized from the request structures and clustered based on the encoded attributes. A DNA cluster may be converted to digital data to generate an integrated request structure. Machine learning models may generate an extraction schedule using update information for each third-party vendor. A bot array may apply license credentials to access the vendors and execute an integrated request. An integrated response structure generated from extracted subscription data may be mapped back to the DNA strands. The DNA strands may be decoded to identify the original requests and responses may be transmitted to the originating applications using the NFT linkage associated with the requests.

Abstract: A system is provided for detecting multi-dimensional linkage and layering of resource transfers using a distributed server network. In particular, the system may comprise a plurality of distributed server nodes that each host a copy of a distributed register, where each of the nodes may be operated by an entity. Each distributed server node may submit, to the distributed register, data records that may contain data regarding potential unauthorized users, accounts, and/or resource transfers. Based on the information within the distributed register, along with various other data inputs, the system may use a deep learning-based graph processing algorithm to identify a multi-dimensional linkage between the users, accounts, and/or resource transfers to extract hidden relationships and potentially unauthorized activity.

Abstract: A system is provided for code base sharing during computing application failure using a distributed register network. In particular, the system may comprise a plurality of computing application systems that are associated with one another and stored on a distributed register. In this regard, the distributed register may share a code base as well as processing logs for each application. The system may use a deep learning based machine learning process for identifying the dependencies, input data sources for each application, and expected data outputs. Accordingly, if an application experiences a failure, the payload of the failed application may be read and/or processed by another application to maintain the workflow. Furthermore, the system may use AI/ML processes to analyze the code base and/or system logs associated with application failures and generate recommendations for remediating failures.

Abstract: An application monitoring system for identifying and removing unused components from memory associated with an application. Embodiments identify a range, a set of historical logs associated with the range, and components in each historical log. Each component is classified based on a frequency of use of the component, the most recent time the component was used, the number of periods between the most recent use of the component and the range end point, and the number of periods between the most recent use of the component and the previous use of the component. Unused and rarely used components are isolated or removed from the memory.

Abstract: An application monitoring system for identifying and removing unused components from memory associated with an application. Embodiments identify a range, a set of historical logs associated with the range, and components in each historical log. Each component is classified based on a frequency of use of the component, the most recent time the component was used, the number of periods between the most recent use of the component and the range end point, and the number of periods between the most recent use of the component and the previous use of the component. Unused and rarely used components are isolated or removed from the memory.