Abstract: Embodiments of the present invention provide a system for performing misappropriation detection and prevention using Siamese Neural Networks. The system is configured for identifying initiation of a resource interaction by a user, via an interaction device, determining if the resource interaction meets criteria associated with an unauthorized interaction based on applying a filtering mechanism, determining that the resource interaction meets the criteria associated with the unauthorized interaction and flag the resource interaction, passing the resource interaction to a Siamese Neural Network to determine if the resource interaction is unauthorized, and approving or denying the resource interaction based on determining if the resource interaction is unauthorized.

Abstract: Embodiments of the present invention provide a system for performing recovery of misappropriated interactions via neuromorphic computing. The system is configured for receiving a recovery request associated with an unauthorized resource interaction from a user, extracting interaction data associated with the unauthorized resource interaction from a data repository, communicating with one or more external systems to gather third party information associated with third party entity linked with the unauthorized resource interaction, communicating with one or more internal systems to gather user recovery information associated with the user, passing the interaction data, the third party information, and the user recovery information to a Spiking Neural Network to determine if the recovery request is accurate, and processing or denying the recovery request based on determining if the recovery request is accurate.

Abstract: An apparatus for activating a physical card in a virtual environment comprises a processor associated with a server. The processor is configured to receive a request to issue a physical card to a first user and to generate a first non-fungible token (NFT) for storage in the physical card, wherein the first NFT is then displayed as a digital card in a virtual environment. The processor is further configured to receive the digital card in an interaction and to generate a second NFT. The processor is further configured to combine the first NFT and the second NFT to generate an activation NFT that allows the physical card to access a profile corresponding to the first user during an interaction. The processor is further configured to transmit the activation NFT and an instruction to the first user device to store the activation NFT in the physical card.

Abstract: In some examples, embedded designer implementation may include, upon receiving an indication of actuation of a second designer launch element that is included in a first designer, launching a second designer inline from the first designer. Further, embedded designer implementation may include, upon receiving an indication of actuation of a first designer return element that is included in the second designer, returning to the first designer inline from the second designer. The first designer and the second designer may include a configurable component. Further, embedded designer implementation may include utilizing a common portion of the memory for the configurable component.

Abstract: In some examples, embedded designer implementation may include, upon receiving an indication of actuation of a second designer launch element that is included in a first designer, launching a second designer inline from the first designer. Further, embedded designer implementation may include, upon receiving an indication of actuation of a first designer return element that is included in the second designer, returning to the first designer inline from the second designer. The first designer and the second designer may include a configurable component. Further, embedded designer implementation may include utilizing a common portion of the memory for the configurable component.

Abstract: A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv),charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ?0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.

Abstract: A computational method for identifying adhesin and adhesin-like proteins, said method comprising steps of computing the sequence-based attributes of a neural network software wherein the attributes are (i) amino acid frequencies, (ii) multiplet frequency, (iii) dipeptide frequencies, (iv),charge composition, and (v) hydrophobic composition, training the artificial neural Network (ANN) for each of the computed five attributes, and identifying the adhesin and adhesin-like proteins having probability of being an adhesin (Pad) as ?0.51; a computer system for performing the method; and genes and proteins encoding adhesin and adhesin-like proteins.