3183results about How to "Improve learning efficiency" patented technology

The invention relates generally to a system and methodology for learning, and in particular for practicing for tests. The system and method employs algorithms for performing various analytics. A computerized system analyzes Meta-Data associated with the Tests, Questions and the learner / student response to those and provides information which helps the learner / student learn faster, get a deeper understanding of the subject area and helps them focus and practice for the subject area of needs. As the data for a student is collected the system can analyze the core strength and weakness of student for the subject, topic or subtopic etc. and in various other dimensions. Additionally as data from multiple students is aggregated the methodology defines the mechanism to analyze and quantify the outcome from three areas of learning namely, the learner effort, an instructor effort and the quality of learning contents. Such a meta-data based learning methodology greatly reduces the potential for guess and subjective analysis and improves the learning of an individual based on the quantifiable data, either at the user choice or being driven by the system recommendation.

The invention discloses an intelligent studying platform based on multimodal interaction and an interaction method of the intelligent studying platform and particularly discloses an intelligent studying platform based on multimodal interaction operations such as touch screen input, voice control and gesture control and an interaction method of the intelligent studying platform. The intelligent studying platform comprises an embedded microprocessor module, a light-controlled module, a voice processing module, an imaging processing module, a network communication module and a sitting posture correction module. The intelligent studying platform integrates functions of real-time video and voice communication, intelligent machine answering, real-time student state monitoring, real-time push and the like, and accordingly electronic learning product operation complexity is reduced for users, the intelligent studying platform adapts to study habits of students taking a 'desk top' as a major studying area, timely interaction between parents or teachers and the students is facilitated, and studying efficiency of the users is improved greatly.

The invention relates to a method and a system for network protocol recognition based on tri-classifier cooperative training learning. The method comprises the following steps: carrying out IP (Internet Protocol) regrouping and TCP (Transmission Control Protocol) traffic reduction on network original traffic, and stipulating the unit of network data from original packets to flow; extracting each message of unidirectional flow feature information and vectoring to build a feature matrix; building a tri-classifier cooperative training classifier with few identified samples; judging whether a classifying model of an analyzed protocol exists or not, and utilizing a tri-classifier cooperative training learning method to build a protocol classifier if the classifying model does not exist, otherwise, judging the protocol attributes of data packets; training by a tri-classifier cooperative training learning algorithm based on J48 and obtaining the classifying model of the analyzed protocol; carrying out protocol type judgment on network data packets not identified, and outputting two classes of results: one class refers to the network data packets belonging to the target protocol, and the other class refers to network data packets not belonging to the target protocol. High recognition accuracy and high recalling rate are ensured by the method.

The invention discloses a nutrient meal-following food and a producing method thereof. The nutrient meal-following food is characterized by comprising the following components in parts by weight: 0.5-1.5 parts of polished round-grained rice, 0.5-1.5 parts of white rice, 0.5-1.5 parts of black rice, 0.5-1.5 parts of barley, 1.5-2.5 parts of buckwheat, 0.5-1.5 parts of wheat, 1.5-2.5 parts of oats, 1.5-2.5 parts of coix seeds, 1.5-2.5 parts of millets, 1.5-2.5 parts of corns, 1.5-2.5 parts of broomcorn and 1.6-2.4 parts of sesames. The producing steps are as follows: (1) mixing and cleaning the raw materials, and then soaking the mixture for 8-12 hours (2) steaming the raw materials thoroughly and then airing the raw materials to 30 DEG C; (3) adding fermentation strains and the raw material soaking water in the step (1) into the raw materials in the former step and mixing uniformly; (4) fermenting at a constant temperature of 30 DEG C for 30-35 hours; (5) repeating the step (3); (6) fermenting at a constant temperature of 30 DEG C for 24-30 hours; (7) grinding the well-fermented raw materials into paste; (8) drying the paste into powder and packaging the powder in a sealing manner. The nutrient meal-following food has rational allocation, results in no digestion discomfortableness to the intestines and stomach as the surfaces of the coarse grains are softened through biological fermentation, and is better for the absorption of an organism as the plant cell walls are destroyed by enzymes under the action of microorganisms.

The invention discloses a knowledge-based electronic medical record quality control method. The method includes: by a closed loop type design structure, designing and making quality control semantic judgment rules and inputting time limit rules in an initial stage; allowing middle-stage data to enter a mobile terminal taking Raspberry Pi as a key design, wherein an analysis module combined with the NLP (natural language processing) natural language learning technology is embedded in the terminal; after quality control is finished, uploading synchronous data to a data center for storage and management; by the data center, acquiring common templates in medical record data by means of big data analysis, establishing an error correction knowledge base, and designing and constructing a database and an integrated environment to realize data sharing with internal modules of an error detection quality control terminal. By adoption of the knowledge-based electronic medical record quality control method, data of the data center are sampled and inspected regularly to further guarantee data correctness. Therefore, error detection, integration and analysis of structural medical record data can be realized, mass data and artificial intelligence processing technologies under an intelligent medical environment are utilized completely, medical record quality control cost is reduced, construction of the error correction knowledge base is realized, and quality control algorithm and rule quality is improved.