Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

A determination apparatus generates an interval vector having a plurality of components that are adjacent occurrence intervals between a plurality of events that have occurred in chronological order. The determination apparatus generates a plurality of local variable points each of which includes specific components as one set of coordinates, using a predetermined number of consecutive interval vectors in the chronological order. The determination apparatus generates a Betti sequence by applying persistent homology transform to the plurality of local variable points for which the interval vectors serving as starting points are different. The determination apparatus determines a type of the plurality of events based on the Betti sequence.

Systems and methods for predicting forward market pricing for renewable energy credit based on human behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market where the forecast is based at least in part on a human behavior information collected from at least one human behavioral data source. The example system may further include wherein the energy credit includes a renewable energy credit associated with a renewable energy system, and a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

A system for transportation includes a vehicle occupied by a rider, and an artificial intelligence system for processing a voice of the rider to classify an emotional state of the rider and optimizing at least one operating parameter of the vehicle to improve the emotional state of the rider.

Transaction-enabling systems including a controller are disclosed. The controller can access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and, in response to the instruction set access request, provide a provable access to the instruction set.

An apparatus includes a processor programmed to define an input matrix and kernel matrix based upon the encrypted data, identify an algebraic structure of an encryption method applied to the encrypted data, determine a primitive root of unity in the algebraic structure in response to an input matrix size and a kernel matrix size, transform the input matrix and kernel matrix utilizing the primitive root of unity into a transformed input matrix and a transformed kernel matrix, compute an element-wise multiplication of the transformed input matrix and transformed kernel matrix, apply a reverse discrete Fourier transformation, and output a convolution of the input matrix and the kernel matrix based upon the encrypted data.

Described herein are embodiments for performing pattern recognition using a hierarchical network. The hierarchical network is made up of fractal cognitive computing nodes that manage their own interconnections and domains in an unsupervised manner. The fractal cognitive computing nodes are also self-replicating and may create new levels within the hierarchical network in an unsupervised manner. The form of signals processed in the hierarchical network may take on the form of key-value pairs. This may allow the hierarchical network to replicate and perform adaptive pattern recognition in non-domain-specific manner with regards to the input signals.

An information detection method includes: determining key point information in a target identification from a target image based on a preset deep learning algorithm; obtaining an image of the target identification from the target image according to the key point information; and determining information of a preset field from the image of the target identification according to the image of the target identification and a preset identification template matching the target identification.

A key generation method based on a deep learning generative adversarial network includes: preparing a training set image; construction of a key generation network: constructing the key generation network according to a generator network and a discriminator network, and inputting the training set image to the key generation network; and training of the key generation network: training the key generation network by a deep learning method to generate a key.

A surveillance system is coupled to a plurality of sensor data sources arranged at locations within a plurality of regions of a site under surveillance. The surveillance system accesses a threat model that identifies contextual events classified as threats. The surveillance system identifies at least one contextual event for a site in real-time by processing sensor data generated by the sensor data sources, and co-occurring contextual data for at least one of the regions. Each identified contextual event is classified as one of a threat and a non-threat by using the threat model.