1974 results about "Outcome predictor" patented technology

A method and system schedules inbound inquiries, such as inbound telephone calls, for response by agents in an order that is based in part on the forecasted outcome of the inbound inquiries. A scheduling module applies inquiry information to a model to forecast the outcome of an inbound inquiry. The forecasted outcome is used to set a priority value for ordering the inquiry. The priority value may be determined by solving a constrained optimization problem that seeks to maximize an objective function, such as maximizing an agent's productivity to produce sales or to minimize inbound call attrition. The inbound call may be placed on a virtual hold or be responded to on a real-time basis based on the inbound inquiry's priority value. A modeling module generates models that forecast inquiry outcomes based on a history and inquiry information. Statistical analysis such as regression analysis determines the model with the outcome related to the nature of the inquiry. Forecasted outcomes are based on the goal of the inbound calls and include factors such as probability an inbound caller will hang up, probability that an inbound caller will alter a business relationship based on hold time, probability that an inbound caller will make a purchase, and the relative probable reward of responding to an inbound call.

Rapid and accurate in-vivo assessment of cerebral white matter injury particularly for pre-term infants, for timely treatment and / or prediction of outcomes has been very limited. This invention exploits the discovery that reduced high-frequency EEG intensity, particularly as shown by the upper spectral edge frequency, is a good indicator of cerebral white matter neural injury and is well correlated with MRI results. With more experience of clinical cases, a set of simple rules such as “if the spectral edge value is below 8 Hz there is a high likelihood of injury” may be validated, yet the EEG technology involved is largely invisible to the user. In the invention, EEG signals are processed by software to obtain, store, and graphically display bilaterally collected EEG spectral edge and intensity values over from hours to weeks. Rejection of corrupted signals by filtering and gating means is responsive to incoming signal characteristics, to additional inputs such as motion sensors or impedance tests, and to patient data (gestational age in particular). The invention includes the software and methods of use.

A method comprises receiving a network of a plurality of nodes and a plurality of edges, each of the nodes comprising members representative of at least one subset of training data points, each of the edges connecting nodes that share at least one data point, grouping the data points into a plurality of groups, each data point being a member of at least one group, creating a first transformation data set, the first transformation data set including the training data set as well as a plurality of feature subsets associated with at least one group, values of a particular data point for a particular feature subset for a particular group being based on values of the particular data point if the particular data point is a member of the particular group, and applying a machine learning model to the first transformation data set to generate a prediction model.

The invention discloses a day-dimension regional traffic index prediction method considering influences of multiple factors. The method comprises the steps that regions are divided and aggregated; regional traffic index original data preprocessing is carried out; the influences of multiple factors are considered, and regional traffic index prediction under the day dimension is carried out. According to the specific technical scheme of the method, on the basis of traffic cell division, traffic cells with the same aggregation property are aggregated, and regional traffic indexes are calculated;on the basis of road network operation early warning requirements, a prediction time period and a prediction cycle are determined; regional traffic data is extracted, made up for and removed, and preprocessing such as comprehensive building of a historical data factor attribute set from different angles is conducted on the data; on the basis of a decision tree theory, regional road network operation congestion state prediction is carried out; a final prediction result of the regional traffic indexes is determined by means of the square euclidean distance. By means of the method, on the one hand, monitoring and application of the urban road network operation state is deepened, and on the other hand, technical support is provided for early warning and forecasting work of the road network operation state.

Disclosed is a rolling bearing remaining life prediction method based on feature fusion and particle filtering. According to an index calculation process, firstly, original features are extracted from bearing vibration signals, the extracted original features are clustered by the adoption of a relevance clustering method, then, one typical feature is selected from each cluster to form optimal feature sets, and finally the feature sets are fused by the adoption of a weight fusion method into a final recession index. According to a life prediction process, firstly, smoothing and resampling are carried out on the recession index, the time interval is adjusted to be an expected value, state-space model initial parameters are calculated by the adoption of least square fitting, then, model parameters are updated in real time according to new observation data, and finally the remaining life of a bearing can be predicted. According to the rolling bearing remaining life prediction method based on feature fusion and particle filtering, the difference between the life prediction result and a true value is small, and the application effect is good.

The invention discloses a short-term wind power prediction method based on integrated empirical mode decomposition and a deep belief network. The short-term wind power prediction method comprises the steps of: decomposing an original wind power sequence into a series of intrinsic mode functions with different features by adopting integrated empirical mode decomposition, calculating sample entropy of the original wind power sequence and the intrinsic mode functions, combining the intrinsic mode functions with similar sample entropy values into a new sequence, and forming a random component, a detail component and a trend component; selecting an input variable set by adopting a partial autocorrelation function; constructing a training sample set according to the input variable set of each component; and establishing a deep belief network short-term wind power prediction model for each component, and superposing prediction results of the components, so as to obtain a final short-term wind power predicted value. The short-term wind power prediction method provided by the invention effectively improves the short-term wind power prediction precision, and can effectively solve the wind power prediction problem of the electric power system, so as to provide more reliable guarantee for large-scale wind power integration.

The invention relates to a short-term load prediction method based on a clustering and sliding window. The method comprises the following steps of: preprocessing electric power load data; clustering historical data of a prediction user by utilizing a clustering algorithm, and adjusting clustering parameters; selecting k data from near to far of the prediction time in a category, containing most data, in clustering results to form a sliding window k; predicting the k selected data by utilizing a combination model based on the sliding window, and acquiring a primary prediction result; and correcting the primary prediction result of the combination model according to meteorological factors to obtain a final load prediction result. Compared with the prior art, the method has the advantages of high prediction precision, good adaptability and the like.

A method and system for utilizing neurophysiologic information obtained by techniques such as quantitative electroencephalography (QEEG), electrode recordings, MRI in appropriately matching patients with therapeutic entities is disclosed. The present invention enables utilization of neurophysiologic information, notwithstanding its weak correlation with extant diagnostic schemes for mental disorders, for safer and expeditious treatment for mental disorders, discovering new applications for therapeutic entities, improved testing of candidate therapeutic entities, inferring the presence or absence of a desirable response to a treatment, and deducing the mode of action of one or more therapeutic entities. In particular, methods for effectively comparing neurophysiologic information relative to a reference set are disclosed along with database-based tools for deducing therapeutic entity actions on particular patients such that these tools are readily accessible to remote users.

The invention relates to a prediction method suitable for a movement track of a non-cooperative spinning object in space. A position coordinate of the object in a camera coordinate system is obtained, and a position coordinate of the object in an inertia coordinate system is solved according to a known camera attitude; and an NAR neural network is constructed, a BPTT algorithm is used to train the neural network, the position value of the object is predicted and output after deviation convergence, and the system robustness and sampling continuity are ensured. Position information is used to calculate an attitude transformation quaternion, the attitude transformation quaternion is calculated via kinematical and kinetic equations according to estimated parameters, and an estimated parameter value of least square regression is used to calculate a prediction result via the equations. The method is applied to an in-orbit service task of the space, the object track can be traced rapidly and accurately, and long-term prediction information of the object track can be obtained after parameter convergence.

The invention provides a prediction method and device, and relates to the technical field of computers. The method comprises the steps of determining to-be-predicted time; according to the to-be-predicted time, obtaining target historical time sequence data in a first time period before the to-be-predicted time; extracting target characteristics according to the target historical time sequence data; Wherein the target feature comprises a time factor feature and an external factor feature, and the external factor feature is at least one feature except a time factor in the target historical timesequence data; and according to the target feature, determining a prediction result corresponding to the to-be-predicted time by using a preset target prediction model. According to the embodiment ofthe invention, by combining the time sequence and the machine learning model, prediction can be carried out by combining the characteristics of the external factors except the time factors in the target historical time sequence data, so that the prediction accuracy is improved, and the prediction accuracy of the unstable time sequence can also be improved.

A system and method for tangibly evaluating the impact that proposed daily or long term actions would have on an entity's strategic objectives, or other business goals, prior to implementing those proposed actions is provided by forecasting the probable results of those proposed actions and comparing them to the forecasted results of the existing planned actions. The invention also includes a provision to evaluate the impact of any proposed changes in terms of the changes' offsetting tradeoff impacts. The invention also includes the provision to limit the ability of different users of the system to over-ride the recommended actions based upon the individual user's authority level, based upon the expected results of the proposed actions and / or based upon the tradeoff results on the tradeoff threshold limits. In addition, all of the results and / or limits can be setup as a set of ranges to work within as opposed to just target values.

Systems and methods are provided that predict the accuracy of expert forecasts based on a corpus of prior expert forecasts. Expert forecasts (also referred to as opinions) are analyzed and processed to determine an opinion and related positions expressed by the expert. Each opinion is combined with relevant industry information to create a structured model for the opinion. A plurality of structured models for a given expert are then analyzed along with information related to the actual outcome of prior predictions to create a decision model for the particular expert. A new opinion issued by an expert is then analyzed using the decision model for that expert to predict the accuracy of the new opinion from the expert. Recommended actions in accordance with or against the new expert opinion may then be provided.

A system and method for administering reductions in future behavioral health care costs through interventions in an insurance plan participant's behavioral health regimen, is disclosed. Information is processed and provided to case managers and / or health care providers in a manner than significantly improves the ability of such individuals to selectively identify plan participants that are most likely to benefit from the intervention. A database is built from a larger set of insurance data, and this data is then further processed to generate, based at least in part on clinical data derived from medical and pharmacy claims, a predictive model that is used to predict the likelihood of future utilization of behavioral health services by a plan participant. The prediction results indicate the relative desirability of intervention in the participant's behavioral health regimen and are used to guide the case, disease, and behavioral health services utilization management for all plan participants.

The invention provides a short-term traffic flow prediction method based on a nerve network combination model. The method is used to construct a counterpropagation nerve network combination prediction model and the short-term traffic flow prediction method is provided based on the model. Aiming at a characteristic of a traffic flow, a fuzzy C mean value clustering algorithm is used to cluster the traffic flow. For bunch generated through clustering, a counterpropagation nerve network prediction model is constructed. According to grade of membership, a weighted sum of prediction model prediction results is calculated and is taken as a final prediction result. In order to increase prediction precision, a taguchi method is used to carry out test designing so as to test influences of different structure parameters on prediction model prediction precision, and an optimum structure parameter is used as an initial structure of the prediction model. By using the method in the invention, the prediction precision of the short-term traffic flow can be effectively increased, an influence of a noise on the prediction precision in training data is reduced and operation time is reasonable.

The invention discloses a target detection method based on full-automatic learning. The method comprises the following steps: carrying out the training of a model through employing a small-scale manual labeling data set after preprocessing and a deep neural network, carrying out the fine adjustment of the model trained through an Imagenet data set, and obtaining a deep model; carrying out reasoning prediction on the pseudo-labeled part of the original large-scale image data set by utilizing the deep model, removing repeated prediction of the same target after carrying out non-maximum suppression, and respectively storing bounding boxes and confidence of prediction results according to categories. Through self-supervised pseudo labeling and active learning sample selection, the informationentropy and the divergence degree predicted by the deep neural network are learned in a combined manner, unlabeled samples are sorted according to the weight, and pseudo labels are allocated to high-confidence samples ranked at the top. The objective of the invention is to solve the problems of too high labor cost of common target detection and labeling and poor mobility and adaptability of a training model in an actual scene.

A system and method which uses statistical analysis of college football games to predict an outcome is comprised of a unique combination of objective, quantitative factors. The system and method accounts for when in the college football season the contest takes place and uses varying sets of objective parameters based on the season game number. The system and method may be implemented on a variety of processing platforms which allows for rapid analysis of multiple contests.

The invention discloses a small-scale air quality index prediction method and system for a city, which firstly divides a city area into a plurality of to-be-predicted locations in a grid form; and then acquires historical data related to each model, and based on historical data: establishing corresponding correspondences The current time prediction and the time prediction model predicted at each moment in the future time, establish a spatial prediction model for air quality prediction at the specified coordinates, and establish a dynamic prediction model that characterizes the relationship between traffic data and geographic interest point data and air quality index, an indoor and outdoor prediction model that characterizes the relationship between the indoor air quality index and the outdoor air quality index; when performing the prediction, the established time prediction model, the spatial prediction model, and the dynamic prediction are performed for any real-time moments to be predicted. The model and the indoor and outdoor prediction models are cooperatively trained to fuse the prediction results of all the models, that is, the predicted values of the air quality index at each moment in the respective current and future time periods of each to-be-predicted location.

The invention discloses a support vector machine-based wind electric powder prediction device and a support vector machine-based wind electric powder prediction method, and belongs to the technical field of wind electric power prediction. The device comprises a microprocessor, a sensor, an external expanding memory, a data acquisition module, an energy management module (EMS) and a communication interface. The prediction method applying the device comprises the following steps of: 1, normalizing historical data; 2, acquiring meteorological information of a prediction day by using the data acquisition module to form a training sample set; 3, obtaining an optimal clustering centre matrix which meets accuracy requirement; 4, classifying the training sample set according to distances of clustering centers; and 5, obtaining the wind electric power prediction result. The device and the method have the advantages that: the induction principle of structural risk minimization is realized, the training is equivalent to that the quadratic programming problem of linear constraint is solved; and the device and the method have unique solution.

The invention discloses a small molecule drug virtual screening method based on deep migration learning and application thereof. A source domain is used as an input to be trained, converged and derived to obtain a weight matrix; a target domain is input into an improvement tool to serve as the initialization weight of the target domain; fine adjustment, training and convergence are conducted on the initialization weight and data in the target domain sequentially; a biological activity value of interaction of a lead compound and a drug target in the target domain is predicted, a target domain molecular fingerprint and a predicted value are obtained, and an evaluation index root mean square error and a correlation coefficient of a predicted result are output; the target domain is subjected to fine adjustment by repeating above steps, and the weight matrix of the source domain helps the target domain build a model. According to the small molecule drug virtual screening method and the application thereof, the effective virtual screening model can still be obtained under the condition that the information of a known active ligand sample is insufficient, and does not need to rely on a large number of data samples.

The invention discloses a personalized traffic accident risk prediction recommendation method based on depth learning, which comprises the following steps: dividing a city into grid regions; calculating traffic accident data, traffic flow data and weather characteristic data of each grid region and each period; using the depth learning method to train the model, the traffic accident risk prediction model being obtained. According to the traffic accident data, traffic flow data and weather characteristics data input at the present time, the traffic accident risk prediction model is used to calculate the traffic accident risk prediction at the next time. The invention utilizes the depth learning method to learn the non-linear, high-dimensional and complex correlation relationship between thetraffic accident influence factor and the traffic accident, predicts the traffic accident risk at the city level, and improves the accuracy of the prediction result.

There is provided a method and system for performing a machine learning process. The system comprises: a data collection unit for continuously collecting prediction data, a real result collection unitfor continuously collecting real results of the prediction data, a model automatic training unit for generating an updating training sample based on the collected prediction data and a correspondingreal result according to a configured model updating scheme and continuously obtaining an updated machine learning model by utilizing the updating training sample, and a service providing unit which is used for selecting an online machine learning module for providing an online prediction service from the machine learning models according to the configured model application scheme, and in responseto a prediction service request including the prediction data, providing a prediction result for the prediction data included in the prediction service request using the online machine learning model.

The invention relates to a collaborative anti-cancer pharmaceutical combination prediction method and a pharmaceutical composition. The collaborative anti-cancer pharmaceutical combination prediction method comprises the following steps: 1) data collection: according to different disease treatment effects of a pharmaceutical combination, classifying and obtaining a known collaborative anti-cancer pharmaceutical combination and a corresponding target; 2) model establishment: for the known collaborative anti-cancer pharmaceutical combination and an unknown pharmaceutical combination, calculating a characteristic of the collaborative anti-cancer pharmaceutical combination, and establishing a collaborative anti-cancer pharmaceutical combined prediction model; and and 3) result filtration: expressing spectrum information with the pharmacy, exploring and inducing the characteristic of the known collaborative anti-cancer pharmaceutical combination, and conducting screening with prediction results of the step 2). An anti-breast cancer pharmaceutical combination and an anti-lung cancer pharmaceutical combination can be acquired on the base of the collaborative anti-cancer pharmaceutical combination prediction method. Compared to the prior art, according to the invention, the collaborative anti-cancer pharmaceutical combination prediction method comprehensively uses various characteristics of the pharmaceutical combination, is designed dexterously, predicts accurately, has an important practical application and is suitable for large-scale popularization.

The invention discloses a prediction method for an unbalanced data set based on isolated forest learning. The prediction method comprises the following steps: receiving a prediction request; collecting data, and defining features and labels in the data set and the number of minority class samples and majority class samples; converting a non-numerical feature column and a label column in the data set into classification numerical values; synthesizing minority class samples by using a majority class weighted minority class oversampling technology to form a balance data set; performing abnormal point identification and removal on the balance data set by using an isolated forest algorithm; then performing data standardization, and dividing a training set and a test set; constructing and training a support vector machine classifier model by using the training set; adjusting hyper-parameters of the support vector machine classifier model through a genetic algorithm, and obtaining a prediction model after training is completed; and inputting the test set into the prediction model to obtain a prediction result. The prediction method for the unbalanced data set based on isolated forest learning has the characteristics of stable prediction result and high prediction precision.

The invention relates to a data processing method and discloses a method for processing abnormal data of a real-time data acquisition system in real time. The method comprises the steps of (1) initializing sample data and selecting an even number of normally operating sample data; (2) adopting 1 / 2 of the sample data to act as the moving step by using a single exponential smoothing method, and predicting the latter half part of the sample data by using a single exponential smoothing recurrence method; (3) the residual of a prediction result is calculated according to a prediction value and a measured value of the latter half part; (4) carrying out anomaly analysis on the residual sequence according to a Pauta criterion to confirm whether the measured value is abnormal data or not; and (5) replacing the measured value with the prediction value if the measured value is abnormal data. The method disclosed by the invention is mainly advantageous in that a prediction algorithm coefficient is adjusted in an adaptive mode, the error is analyzed by adopting a mobile exponential smoothing method, and the anomaly judging method better conforms to use conditions of the Pauta criterion, thereby improving the accuracy in judgment for the abnormal data, and preventing false judgment and missing judgment to a certain degree.

The invention discloses a rolling bearing residual life prediction method based on a deep generative adversarial network. The method comprises the following steps: collecting an original vibration signal of a rolling bearing; acquiring characteristic parameters of the original vibration signal; dividing the feature parameters into a training set and a prediction set; sending the training set intoa generator long short-term memory network for training; predicting the degradation process of the rolling bearing, and generating a prediction result; building an automatic encoder model as a discriminator, and discriminating whether a prediction result is from real historical data or not; enabling a generator long short-term memory network and a discriminator automatic encoder to carry out adversarial training to seek an optimal solution; and outputting a rolling bearing residual life prediction result. According to the method, the degradation process of the rolling bearing is predicted through long-term and short-term memory network learning, the prediction result of the long-term and short-term memory network is judged through the automatic encoder, the two methods conduct adversariallearning till the precision requirement is met, the prediction error superposition problem of a traditional method is reduced, and the prediction accuracy is improved.

The invention discloses a method for scale prediction of a regional distributed type comprehensive energy-supply system. A scenario analysis method, a factor correlation analysis method and a market predicting model method are selected for conducting comprehensive prediction at different angles; prediction models are respectively established combined with the three prediction methods and prediction condition setting and prediction parameter setting are conducted on the three modules respectively; parameters and conditions are introduced into the prediction modules to obtain prediction results; the prediction results of the three methods are processed through an equal-weight allocation method, the stability and the practicability of the prediction results can be improved, and therefore a relatively-reasonable scale prediction value of the regional distributed type comprehensive energy-supply system is obtained. The method is simple in principle, diversified in prediction angle, good in prediction accuracy, capable of accurately predicting the development scale of a future distribution type comprehensive energy-supply system of each region and directing development and construction of the regional distributed type comprehensive energy-supply system, and high in practicability and popularization.

The present invention provides Systems and Methods for Creating an Optimal Prediction Model and Obtaining Optimal Prediction Results Based on Machine Learning. In the method for creating an optimal prediction model, the steps are first to input a plural training data and at least one of machine learning algorithms, then convert the training data into a relay format. The method is further to select the automated predictive features, optimize the machine learning algorithm parameter, and then optimize the iterative prediction model. After that, a prediction model and an accuracy assessment data are outputted. In the process of obtaining the prediction result, the data to be predicted is converted into a relay format, and an automated program is used for iterative prediction to generate and output the prediction result and accuracy evaluation data.

The invention belongs to the technical field of short term power load forecasting, and discloses a power load forecasting method based on improved exponential smoothing and a gray model. The method includes the following steps: inputting original power load real-time data, and conducting a single exponential smoothing on the original power load real-time data, weakening the randomness of the original power load real-time data, such that the original power load real-time data approaches exponential development trend; predicting a smoothed sequence by using a gray forecasting model which optimizes background value; conducting inverse exponential smoothing on the forecasting result and returning the result to original power load data and a forecasting value at a next forecasting moment; determining whether the result reaches the requirements of knitting fitting errors, and outputting a forecasting result. According to the invention, the method expands the application range of the gray forecasting model, shortens search intervals, has higher forecasting reliability as high as 97%, can the meet requirements for maintaining the average error of short term power load forecasting at approximately 3% so as to address the problem of short term power load forecasting in future development of intelligent power grids.