1 . A data analysis system comprising: a processor; a latent learner executable by the processor, the latent learner is to arrange data, using latent causal variables, in a multi-dimensional structure based on a target activity defined by a smart card, wherein the data pertains to activities comprising a current activity and the target activity associated with a smart card; an inference deducer executable by the processor to perform analysis on the data arranged in the multi-dimensional structure to predict an outcome of the target activity, wherein the inference deducer employs a proactive-retroactive learning technique to deduce the outcome based on the latent causal variables; a probability determiner executable by the processor, the probability determiner to determine a probability of success of the outcome that has been predicted; and wherein the processor is further to: determine, based on the outcome and probability of success, a plurality of choices associated with the activities; determine patterns and changes in the data pertaining to the activities detected by an access device with access to the smart card; perform a combination of mapping, transformation, parallel computation, distributed task dispatching, scheduling, and I/O functionalities, exposed through at least one application programming interface for the data pertaining to the activities; communicate with the access device and the smart card over a network; schedule to arrange the plurality of choices and the probability of success of the outcome for the access device to be rendered on the smart card; cue the plurality of choices and the probably of success of the outcome for particular listing on the smart card; and transmit the plurality of choices and the probability of success of the outcome to the access device for rendering on the smart card.
2 . The data analysis system of claim 1 , further comprising a data receiver executable by the processor, the data receiver to receive the data pertaining to the current activity and the target activity, wherein the data is received from at least one of the smart card or a data source.
3 . The data analysis system as claimed in claim 2 , wherein the smart card is a first smart card, and wherein the data receiver is further to: receive data associated with a second smart card; and transmit, to the first smart card, the data pertaining to an activity associated with the second smart card.
4 . The data analysis system of claim 1 , wherein the plurality of choices correspond to at least one of a state of an individual that owns the smart card, an action to be taken by the individual, an expected response for the action taken, or a reward for the current activity of the individual.
5 . The data analysis system of claim 1 , comprising: a state identifier executable by the processor to determine a state of an individual that owns the smart card based on the outcome and the probability of success of the outcome; an action indicator executable by the processor to indicate an action to be taken based on the state of the individual; a response provider executable by the processor to provide an expected response based on the outcome and the probability of success of the outcome, the response provider to provide the expected response for a group of individuals of which the individual is a member; and a reward identifier executable by the processor to identify a reward to be provided to the individual based on the current activity of the individual.
6 . The data analysis system as claimed in claim 5 , wherein the action indicator is further to: determine a cognitive state for each choice based on a plurality of attributes, features, and latent variables associated with the action; and determine a cognitive state probability based on the cognitive state of the individual performing an activity at a particular time and a set of multi-layered multidimensional optimizing factors.
7 . The data analysis system as claimed in claim 5 , wherein the action indicator is further to: model each individual of the group of individuals as a quantum particle based on a time and a location; sheave quantum particles to form subgroups of the group of individuals; and determine a data set containing various choices based on predictions for a subgroup of the group of individuals of which the individual is a member.
8 . The data analysis system as claimed in claim 1 , further comprising a communicator executable by the processor to: establish a connection with the smart card as a peer-to-peer network; and establish a communication thread between the smart card and a second smart card within the peer-to-peer network.
9 . A method comprising: receiving, by a processor of a computing device, data pertaining to a current activity, the data comprising a time and a geographic location associated with a plurality of smart cards being used by a plurality of individuals while performing the current activity; arranging, by the processor, the data into a multi-layered multi-dimensional structure based on a self-organized cognitive algebraic neural network (SCANN); aggregating, using SCANN-based learning, the data from each smart card of the plurality of smart cards into multiple groups, wherein the aggregating is based on historical transactions performed by the plurality of individuals using the plurality of smart cards; determining, by using the processor to apply a proactive-retroactive learning technique to the aggregated data, a plurality of choices for an individual of a group of the multiple groups based on the current activity and activity-based predictions for the group, the plurality of choices being associated with actions expected to be performed with respect to the current activity of the individual and with use of a smart card owned by the individual; ranking, by the processor, the plurality of choices in a sequential order based on highest to lowest probability that the individual will choose each respective choice of the plurality of choices; and transmitting, by the computing device via an access device, the plurality of choices for the individual to the smart card to be rendered on the smart card for selection by the individual.
10 . The method of claim 9 , further comprising: performing, by an inference deducer executed by the processing device, the proactive-retroactive learning technique on the multi-layered multi-dimensional structure to predict an outcome of the current activity based on latent causal variables of the aggregated data; determining, using a probability determiner executed by the processing device, a probability of success of the outcome that has been predicted; and determining, based on the outcome and probability of success, the plurality of choices associated with the activities.
11 . The method as claimed in claim 9 , further comprising determining an action taken by the smart card on a choice from the plurality of choices as shared with the individual, wherein the action comprises a cognitive state probability.
12 . The method as claimed in claim 11 , wherein the determining the action further comprises: identifying attributes associated with each of the plurality of choices rendered on the smart card; determining a cognitive state for each of the plurality of choices based on the attributes, features, and variables associated with the plurality of choices; and determining a cognitive state probability, based on the cognitive state and using a set of multi-layered multi-dimensional optimization factors, comprising a probability of the individual being engaged in the action at a particular time.
13 . The method as claimed in claim 9 , wherein the determining the plurality of choices comprises: accumulating cognitive states, an expected response, and a reward for the plurality of individuals; modeling each individual from the plurality of individuals as a quantum particle based on an activity, time, and a location of each individual; identifying groups of individuals by sheaving the modeled quantum particles; and determining an order of the plurality of choices based on transactions and predictions for the individual as well as on the predictions for the group of individuals of which the individual is a member.
14 . The method as claimed in claim 9 , further comprising: determining actions associated with the plurality of choices based on the historical transactions in asymmetric and incomplete information conditions; and transmitting the plurality of choices to the smart card over a network.
15 . The method as claimed in claim 9 , wherein the ranking the plurality of choices comprises: obtaining the data associated with one of the individual or a group of individuals, and which pertains to a particular activity; based on determining whether the data is related to one of a previous activity or a new activity of the individual, making connections with smart cards of other individuals, which are related to a smart card of the individual through the one of the previous activity or the new activity; based on determining that a new smart card of a new individual has initiated the particular activity, identifying a second plurality of choices associated with the particular activity for the new individual; upon identifying the plurality of choices, forecasting actions associated with each of the plurality of choices; based on the forecasting, ranking the second plurality of choices in a second sequential order; and transmitting the plurality of choices for rendering on the new smart card.
16 . The method of claim 9 , further comprising: extracting incomplete cognitive data from features, attributes, and latent variables that existed in historical activity data associated with the historical transactions, but retained in a Voronoi region of the historical activity data; associating, using a Voronoi geometry algorithm, the incomplete cognitive data with features, attributes, and latent variables of the data associated with the current activity to generate a complete set of cognitive data; and performing, using the complete set of cognitive data, the aggregating the data and the determining the plurality of choices.
17 . A non-transitory computer-readable medium of a first smart card storing instructions, which when executed by a processor, perform operations comprising: authenticating and securing a connection with one or more second smart cards via one or more access devices that can read data from the one or more second smart cards; negotiating a transaction with the one or more second smart cards, wherein negotiating the transaction generates data associated with historical transaction activities; transmitting the data to a computing device that performs self-organized cognitive algebraic neural network (SCANN)-based learning on the data and generates a plurality of choices associated with a proposed current activity as well as a probability of success of each respective choice of the plurality of choices; receiving the plurality of choices and the probabilities of success; and displaying the plurality of choices and the probabilities of success through a first display unit of the first smart card for selection by an individual carrying the first smart card.
18 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise: generating second data associated with execution of a further transaction in response to selection of a choice of the plurality of choices; transmitting the second data to the computing device for further SCANN-based learning; receiving a plurality of updated choices and updated probabilities of success; and displaying the plurality of updated choices and the updated probabilities of success through the first display unit.
19 . The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise: providing, to the computing device, security data including a touch identifier of the individual; providing, based on authentication of the security data, a public key to authenticate communication with the one or more second smart cards and the one or more access devices; and verifying an authentication mechanism for performing the authentication via one of a group or a sub-group protocol comprising at least one of Transmission Control Protocol Internet Protocol (TCP/IP), Individual Datagram Protocol (UDP), Simple Mail Transfer Protocol (SMTP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), or Dynamic Host Configuration Protocol (DHCP).
20 . The non-transitory computer-readable medium of claim 19 , wherein the operations further comprise: in response to successful authentication using the public key, generating a digital signature using or more algebraic structure of an elliptic curve digital signature algorithm; and providing, to the computing device, the digital signature in order to perform identification verification for conducting the proposed current activity with the one or more second smart cards.
