163 results about "Predictor variable" patented technology

A method useful for facilitating choosing a treatment or treatment regime and for predicting the outcome of a treatment for a disorder which is diagnosed and monitored by a physician or other appropriately trained and licensed professional, such as for example, a psychologist, based upon the symptoms experienced by a patient. Unipolar depression is an example of such a disorder, however the model may find use with other disorders and conditions wherein the patient response to treatment is variable. In the preferred embodiment, the method for predicting patient response includes the steps of performing at least one measurement of a symptom on a patient and measuring that symptom so as to derive a baseline patient profile, such as for example, determining the symptom profile with time; defining a set of a plurality of predictor variables which define the data of the baseline patient profile, wherein the set of predictor variables includes predictive symptoms and a set of treatment options; deriving a model that represents the relationship between patient response and the set of predictor variables; and utilizing the model to predict the response of said patient to a treatment. A neural net architecture is utilized to define a non-linear, second order model which is utilized to analyze the patient data and generate the predictive database from entered patient data.

Methods, systems, and apparatuses for generating relevance functions for ranking documents obtained in searches are provided. One or more features to be used as predictor variables in the construction of a relevance function are determined. The relevance function is parameterized by one or more coefficients. An ideal query error is defined that measures, for a given query, a difference between a ranking generated by the relevance function and a ranking based on a training set. According to a structured output learning framework, values for the coefficients of the relevance function are determined to substantially minimize an objective function that depends on a continuous upper bound of the defined ideal query error. The query error is determined using a structured output learning technique. The query error is defined as a maximum over a set of permutations.

Predictive models are developed automatically for a plurality of modeling variables. The plurality of modeling variables is transformed, based on a transformation rule. A clustering of the transformed modeling variables is performed to create variable clusters. A set of variables is selected from the variable clusters based on a selection rule. A regression of the set of variables is performed to determine prediction variables. The prediction variables are utilized in developing a predictive model. The development of the predictive model may include modification of the predictive model, review of the plurality of transformations, and validation of the predictive model.

The invention relates to the technical field of intelligent irrigation, and provides an irrigation method and device based on machine learning, and the method comprises the steps: reading environmentdata which comprises weather, soil humidity and plant growth condition data; performing feature engineering processing on the environment data to obtain prediction variable data; sending the prediction variable data to an irrigation effect prediction model, the irrigation effect prediction model being obtained based on labeled historical data training, the label being generated by using a manual method or a machine learning method; obtaining a target variable value output by the irrigation effect prediction model, and generating an irrigation scheme by utilizing the target variable value; andsending a control signal to the water outlet valve with the specified number according to the irrigation scheme. The irrigation opportunity is modeled and controlled based on plant growth conditions and environmental factors, the problem that irrigation cannot be carried out at the optimal opportunity through manual decision making or decision making based on a single environmental factor can be solved, the crop yield can be ensured, and labor and water resources are saved.

A system and methodology for encoding or decoding hidden data, such as a digital watermark, in visual raster media is provided. The lossless data hiding methodology uses multiple predictors to choose an embedding location to be either a low variance region or a high variance region. Bijective mirror mapping is used to encode hidden data at an embedding location and bijective pixel value shifting is performed to ensure reversibility back to the original image without additional information. The system and methodology can be used either in the spatial domain or the wavelet domain. The Peak Signal to Noise Ratio and the payload capacity are relatively high with the methodology.

A system and method for predictive modeling of building energy consumption provides predicted building energy load values which are determined using kernel smoothing of historical building energy load values for a building using defined scaling factors for scaling predictor variables associated with building energy consumption. Predictor variables may include temperature, humidity, windspeed or direction, occupancy, time, day, date, and solar radiation. Scaling factor values may be defined by optimization training using historical building energy load values and measured predictor variable values for a building. Predicted and measured building energy load values are compared to determine if a preset difference threshold has been exceeded, in which case an alert signal or message is generated and transmitted to electronically and / or physically signal a user. The building energy monitoring system may be integrated with a building automation system, or may be operated as a separate system receiving building energy and predictor variable values.

Parallelize a computer program by scoping program variables at compile time and inserting code into the program. Identify as value predictable variables, variables that are: defined only once in a loop of the program; not defined in any inner loop of the loop; and used in the loop. Optionally also: identify a code block in the program that contains a variable assignment, and then traverse a path backwards from the block through a control flow graph of the program. Name in a set all blocks along the path until a loop header block. For each block in the set, determine program blocks that logically succeed the block and are not in the first set. Identify all paths between the block and the determined blocks as failure paths, and insert code into the failure paths. When executed at run time of the program, the inserted code fails the corresponding path.

Provided are systems, methods and techniques for facilitating allocation of resources based on available data estimates, using steps to: input a set of predictor variables and data values pertaining to them; generate a first transfer function that maps values for the predictor variables to values for a predefined set of factor variables; generate distribution information for the predictor variables based on the input data values; conduct a simulation using the distribution information for the predictor variables; map the simulated values for the predictor variables to information regarding the factor variables, using the first transfer function; map such information to estimation information for a set of target variables using a second transfer function; and display the estimation information, use the estimation information to recommend a purchase or sale of an asset, and / or directly purchase and / or sell an asset based on the estimation information.