Abstract:
A method for generating an artificial intelligence model for determining probability of rainfall, by applying a decision tree ensemble learning process on a dataset, the method comprising: receiving a first dataset comprising at least two variables; determining at least one split criteria for each variable within the first dataset; partitioning the first dataset based on each determined split criteria; calculating a measure of directionality for each partition of data; performing a constrained node selection process by selecting a candidate variable and split criteria, wherein the selection is made to keep a consistent directionality for the selected variable based on existing nodes; updating a directionality table at the end of a constrained node selection; reiterating the constrained node selection process for every node selection throughout the decision tree ensemble learning process until an ensemble model is generated; and processing a second dataset with the generated ensemble model to determine probability of rainfall; wherein the first dataset contains data received from one or more sensors, the received data including data pertaining to temperature.
Abstract:
This invention concerns a method for processing cheque and deposit vouchers by a financial institution (Proof of Deposit). The method involves an operator preparing vouchers for feeding into a machine reader. Capturing an image from the voucher. Correcting incorrectly read scan lines. Automatically notifying the operator when a transaction boundary is detected and suspending processing of vouchers until the transaction is manually balanced. Finalising processing of each voucher by printing captured and trace details on each voucher.
Abstract:
A method of storing secret information on a secret information injection device includes generating a transport key and encrypting the secret information using the transport key. The transport key and the encrypted secret information are received at a data storage and processing device. The data storage and processing device is used to generate an encryption key pair which consists of a secret key and a public key. The encrypted secret information is transferred from the data storage and processing device to the secret information injection device. The transport key is encrypted using the secret key and is then transferred to the secret information injection device.
Abstract:
Embodiments generally relate to a method for selecting hybrid variables. The method comprises sampling at least one interaction effect structure of at least one multivariable dataset, sampling at least one hybrid variable for each sampled interaction effect structure, calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria, labelling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria, training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria, and retaining only hybrid variables with a likelihood value that exceeds a decision criteria. The training of the machine learning model is performed using the labelled sampled hybrid variables.
Abstract:
Embodiments generally relate to a method for selecting hybrid variables. The method comprises sampling at least one interaction effect structure of at least one multivariable dataset, sampling at least one hybrid variable for each sampled interaction effect structure, calculating a lift value for each sampled hybrid variable, and comparing the lift value to a threshold lift criteria, labelling each sampled hybrid variable based on determining that the lift value of the sample hybrid variable exceeds the threshold lift criteria, training a machine learning model to predict the likelihood of a hybrid variable having a lift which exceeds the threshold lift criteria, applying the trained machine learning model to each hybrid variable within each sampled interaction effect structure to determine a value corresponding to the likelihood of each hybrid variable having a lift which exceeds the threshold lift criteria, and retaining only hybrid variables with a likelihood value that exceeds a decision criteria. The training of the machine learning model is performed using the labelled sampled hybrid variables.
Abstract:
An automated method of optimising a security value of an investor investment portfolio financed using a margin lending facility, the investment portfolio including a plurality of security holdings at least some of which comprise a diversified portion of the portfolio, the method including the following steps: A) determining a market value and composition of the portfolio based on a level of diversification in the portfolio, the composition of the portfolio including a diversified portion and a standard portion; B) applying a diversified LVR to the diversified portion of the portfolio and a standard loan to value ratio to the standard portion of the portfolio to calculate a maximum security value; and C) comparing the maximum security value calculated against a minimum security value required to secure the loan to determine whether the margin loan requires clearing.
Abstract:
A method for administering a distributed network (2) of managed database servers (4, 5) from a central database server (3), the method including the steps of: at predefined intervals, (a) receiving at the central database server (3) a heart beat signal identifying each managed database server (4, 5); (b) receiving notification at the central database server (3) of events resulting from execution of tasks by each managed database server (4, 5); (c) checking in the central database server (3) for new changes or tasks to be implemented by one or more of the managed database servers (4, 5); and (d) distributing the new changes or tasks from the central database server (3) to the one or more database servers (4, 5) for execution.