953 results about "Adaptive learning" patented technology

A method and system that dynamically ranks electronic messages based on their situational and inherent dimensions, which are judged by a set of filters. These filters evaluate the different elemental metadata constituting a message and produce a priority value based on filters relevance and importance. The system iterates through queued messages, examine the structured content for expected attributes, statistically analyze unstructured content, apply dynamically weighted rules and policies to deliver a priority ranking, and then display the message and its vital attributes in accordance with the priority ranking. The system also adaptive learns and adjusts its weighted rules and policies to permit priority ranking to change on real-time or interval-based (may be user-defined) schedule. The system includes a GUI for increasing reading and processing efficiency. The GUI performs supervised and unsupervised learning from the user's behaviors, and displays messages in accordance with their priority classification.

A method is disclosed for controlling operation of an engine coupled to an exhaust treatment catalyst. Under predetermined conditions, the method operates an engine with a first group of cylinders combusting a lean air/fuel mixture and a second group of cylinders pumping air only (i.e., without fuel injection). In addition, the engine control method also provides the following features in combination with the above-described split air/lean mode: idle speed control, sensor diagnostics, air/fuel ratio control, adaptive learning, fuel vapor purging, catalyst temperature estimation, default operation, and exhaust gas and emission control device temperature control. In addition, the engine control method also changes to combusting in all cylinders under preselected operating conditions such as fuel vapor purging, manifold vacuum control, and purging of stored oxidants in an emission control device.

The present invention provides an integrated prevention of header, state, rate and content anomalies along with network policy enforcement. A hardware based apparatus classifies layers 2, 3, 4 and 7 network data and maintains rate-thresholds through continuous and adaptive learning. In the process of classifying the packets, the apparatus can determine header and state anomalies and drop packets containing those anomalies. Accurate detection and prevention of layer 7 content anomalies is achieved using fragment assembly, TCP reorder and retransmission removal components, which also identify anomalies in those areas. Content inspection is achieved at high speed through a Content Inspection Engine. The apparatus integrates advantageous solutions to prevent anomalous packets and enables a policy based packet filter.

The present invention provides an integrated prevention of header, state, rate and content anomalies along with network policy enforcement for domain name service (DNS). A hardware-based apparatus helps identifying DNS rate-thresholds through continuous and adaptive learning. The apparatus can determine DNS header and DNS state anomalies and drop packets containing those anomalies. DNS queries and responses are inspected for known malicious contents using a Content Inspection Engine. The apparatus integrates advantageous solutions to prevent anomalous packets and enables a policy based packet filter for DNS.

A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and/or how the user interacts with or responds to the presented learning object.

A workload associated with a task is assessed with respect to each of a plurality of computing paradigms offered by a cloud computing environment. Adaptive learning is employed by maintaining a table of Q-values corresponding to the computing paradigms and the workload is distributed according to a ratio of Q-values. The Q-values may be adjusted responsive to a performance metric and/or a value, reward, and/or decay function. The workload is then assigned to available computing paradigms to be performed with improved utilization of resources.

An automated license plate recognition (ALPR) system and method using a human-in-the-loop based adaptive learning approach. One or more images with respect to an automotive vehicle can be segmented in order to determine a license plate of the automotive vehicle within a scene. An optical character recognition (OCR) engine loaded with an OCR algorithm can be further adapted to determine a character sequence of the license plate based on a training data set. A confidence level with respect to the images can be generated in order to route a low confidence image to an operator for obtaining a human interpreted image. The parameters with respect to the OCR algorithm can be adjusted based on the human interpreted image and the actual image of the license plate. A license plate design can be then incorporated into the OCR engine in order to automate the process of recognizing the license plate with respect to the automotive vehicle in a wide range of transportation related applications.

The invention relates to an adaptive extraction and diagnosis method for degree features of a mechanical fault through a stack-type sparse automatic coding depth neural network, and belongs to the technical field of mechanical equipment state monitoring and reliability evaluation. The method aims at a problem of intelligent diagnosis of the degree of the mechanical fault, and comprises the steps: carrying out the stacking of sparse automatic coding, adding a classification layer, and constructing the stack-type sparse automatic coding depth neural network which integrates the adaptive learning and extraction of the degree features of the fault and fault recognition; employing the advantage that the sparse automatic coding can automatically learn the internal features of data, and adding noise coding to be integrated in the sparse automatic coding for improving the robustness of feature learning; carrying out the layer-by-layer no-supervision adaptive learning and supervision fine tuning of the original input complex data through multilayer sparse automatic coding, completing the automatic extraction and expression of the degree features of the mechanical fault and achieving the intelligent diagnosis of the degree of the fault. The method is used for the diagnosis of the degree of faults of rolling bearings under different work conditions, and obtains a good effect of feature extraction and diagnosis.

A method for detecting surveillance activity in a computer communication network comprising automatic detection of malicious probes and scans and adaptive learning. Automatic scan/probe detection in turn comprises modeling network connections, detecting connections that are likely probes originating from malicious sources, and detecting scanning activity by grouping source addresses that are logically close to one another and by recognizing certain combinations of probes. The method is implemented in a scan/probe detector, preferably in combination with a commercial or open-source intrusion detection system and an anomaly detector. Once generated, the model monitors online activity to detect malicious behavior without any requirement for a priori knowledge of system behavior. This is referred to as “behavior-based” or “mining-based detection.” The three main components may be used separately or in combination with each other. The alerts produced by each may be presented to an analyst, used for generating reports (such as trend analysis), or correlated with alerts from other detectors. Through correlation, the invention prioritizes alerts, reduces the number of alerts presented to an analyst, and determines the most important alerts.

In a method of generating an anomaly detection model for classifying activities of a computer system, using a training set of data corresponding to activity on the computer system, the training set comprising a plurality of instances of data having features, and wherein each feature in said plurality of features has a plurality of values. For a selected feature and a selected value of the selected feature, a quantity is determined which corresponds to the relative sparsity of such value. The quantity may correspond to the difference between the number occurrences of the selected value and the number of occurrences of the most frequently occurring value. These instances are classified as anomaly and added to the training set of normal data to generate a rule set or other detection model.

A noise reduction device is configured by use of: means for calculating a predetermined constant, and a predetermined reference signal Rω(T) in the frequency domain, respectively by use of adaptive coefficients Wω(m), and for thereby obtaining estimated values Nω and Qω(T) respectively of stationary noise components, and non-stationary noise components corresponding to the reference signal, which are included in a predetermined observed signal Xω(T) in the frequency domain; means and for applying a noise reduction process to the observed signal on the basis of each of the estimated values, and for updating each of the adaptive coefficients on the basis of a result of the process; and an adaptive learning means and for repeating the obtaining of the estimated values and the updating of the adaptive coefficients, and for thereby learning each of the adaptive coefficients.

A method of determining a road surface condition for a vehicle driving on a road. Probabilities associated with a plurality of road surface conditions based on an image of a capture scene are determined by a first probability module. Probabilities associated with the plurality of road surface conditions based on vehicle operating data are determined by a second probability module. The probabilities determined by the first and second probability modules are input to a data fusion unit for fusing the probabilities and determining a road surface condition. A refined probability is output from the data fusion unit that is a function of the fused first and second probabilities. The refined probability from the data fusion unit is provided to an adaptive learning unit. The adaptive learning unit generates output commands that refine tunable parameters of at least the first probability and second probability modules for determining the respective probabilities.

A method is disclosed for controlling operation of an engine coupled to an exhaust treatment catalyst. Under predetermined conditions, such as after an engine cold start, the method operates an engine with a first group of cylinders having a first ignition timing, and a second group of cylinders having a second ignition timing more retarded than the first group. In addition, the engine control method also provides the following features in combination with the above-described split air/lean mode: idle speed control, sensor diagnostics, air/fuel ratio control, adaptive learning, fuel vapor purging, catalyst temperature estimation, default operation, and exhaust gas and emission control device temperature control. In addition, the engine control method also can change to combusting all cylinders at substantially the same ignition timing under preselected operating conditions such as fuel vapor purging, manifold vacuum control, and purging of stored oxidants in an emission control device.

Methods and apparatus for analyzing financial data generally includes a predictive modeling system. The predictive modeling system may include an artificial agent responsive to an input data set. The artificial agent may produce an estimated data set including a market conditions data set. The market conditions data may include an estimate of at least one of liquidity of a market, strategy of a counterparty, and an effect of information leakage. The artificial agent may determine a predictability value for the estimated data set. The predictive modeling system may also include an agent factory responsive to the input data set. The agent factory may generate an artificial agent in response to the input data set.

Systems and methods are disclosed that allow for indexing, processing, or both of information from physical media or electronic media, which may be received from a plurality of sources. In embodiments, a document file may be matched using pattern matching methods and may include comparisons with a comparison reference database to improve or accelerate the indexing process. In embodiments, information may be presented to a user as potential matches thereby improving manual indexing processes. In embodiments, one or more additional actions may occur as part of the processing, including without limitation, association additional data with a document file, making observations from the document file, notifying individuals, creating composite messages, and billing events. In an embodiment, data from a document file may be associated with a key word, key phrase, or word frequency value that enables adaptive learning so that unindexed data may be automatically indexed based on user interaction history.

An adaptive learning system and method provides for automatically generating question types to a user for word comprehension and selecting multiple choice answers for display. Questions are developed for the user by obtaining online content and indexing the content into individual sentences and questions. The system provides questions in a series of rounds to the user and then adaptively tracks the progress of the user based on the categorization of each question.

A subset of a set of components is selected for inclusion on a dynamically-generated web page within a particular user context. Each component may, for example, contain content generated by a respective code module or service. The subset of components is selected based on scores associated with the components in the set. The score of a component is preferably determined based on measurements of desired user activity resulting from exposures of the components to users in the particular context, and reflects a likelihood that other users within the same context will perform a desired action. The disclosed methods may also be used to select items, such as products, to suggest to users within specific contexts.

The invention interacts with a subject to query, challenge and identify multiple aspects and multidimensional influences that provide access to, allow modulation of, and entrainment of, state-specific global bio-regulatory self-organizing controllers and evocable triggers. Modulation of state-specific regulatory triggers may evoke optimized newly emergent self-organizing principles within the subject and ultimately support up-regulation of “states of presence” that include newly emergent controllers of additional optimal regulation of bio-chemical expressions. The “state” measures of a person may include the status of a combination of any number of identified biological qualities. The invention includes systems and methods that support access to a subject's state controller functions, for various aliments, to empower shifting ones' biology from a symptomatic to an asymptomatic state and to optimal adaptive learning and readiness. The system of the invention investigates and accesses the capacities that control such state shifts so that they can be broadly challenged, expanded, and entrained for optimized global regulatory function in reversing a myriad of pathological symptoms, learning limitation, and adaptive dysregulations.

A computer implemented data processing system for adaptively blending a plurality of content recommendation engines. The system comprising: a computer implemented blending module; a plurality of computer implemented recommendation engines in operative association with the blending module; a user terminal in communication with the blending module, wherein the plurality of recommendation engines are in operative association with a content repository; and wherein the user terminal is operable to detect a particular user profile of a particular user associated therewith and deliver the particular user profile to the blending module; and wherein each recommendation engine is associated with specific predefined recommendation rules and operable to select particular content from the content repository in accordance with a particular user profile and predefined recommendation rules; and wherein the blending module is operable to adaptively assign weights to the plurality of recommendation engines for each particular user, responsive to the particular user profile thereof.