A real-time detection method and apparatus for DGA domain name. An original domain name is translated into a multi-dimensional numeric vector, the multi-dimensional numeric vector is input into a deep learning model pre-trained based on an ImageNet data set, to generate a domain name feature, a domain name classifier is trained based on the generated domain name feature, and a DGA domain name is classified and predicted based on the domain name classifier obtained by training. The method firstly uses a deep learning model pre-trained based on an ImageNet data set, from the field of visual image classification and detection, for real-time detection of a DGA domain name, avoiding the process of high-intensity training and parameter weight adjustment for the deep learning model in DGA domain name detection. The detection rate is higher, and detection speed is faster.

A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. The method includes receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient, extracting a first region from the image, extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression, correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient, and generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

An image processing method includes the steps of performing processing of selecting a learning model from a plurality of learning models that have learned a reference used to record an image generated by an image sensor; performing, using the selected learning model, determination processing of determining whether the image generated by the image sensor satisfies the reference; and recording the image in a case in which it is determined that the image generated by the image sensor satisfies the reference, wherein the processing of selecting the learning model is performed based on at least one of an image capturing instruction by a user, an evaluation result of the image by the user, an environment when the image is generated, and a score of each of the learning models for the image generated by the image sensor.

A method for at least one of detecting and classifying a wanted signal in an electromagnetic signal is described. The method includes the following steps: the electromagnetic signal is received; a spectrogram of the electromagnetic signal is determined; at least one correlation parameter, for example a correlation multi-dimensional algebraic object including several correlation parameters, is determined based on the determined spectrogram; the at least one correlation parameter is used as an input for a machine learning module; the wanted signal in the electromagnetic signal is detected and/or classified via the machine learning module based at least partially on the at least one correlation parameter. Further, a signal detection and/or classification system and a computer program are described.

A data storage unit collects and stores, for each user, data related to a “room-and-image” combination preferred by the user. A generation unit generates information which indicates a “room-and-image” relationship and in which a preference of the user is reflected by using the data collected for each user. In a case where a room image (second image) captured by the user is acquired, an image selection unit selects an image (third image) in which the preference of the user is reflected, from an image group registered in a content DB or an image group registered in a user image DB, based on the acquired second image and the information which indicates the “room-and-image” relationship, and suggests the selected image to the user. Thereby, it is possible to suggest an image which matches with the user's room and in which the preference of the user is reflected, as an image for decorating the user's room.

A managed notification system compares image(s) and/or indicia relating to the image(s) and where there is a match selectively provides a notification of the same.

Disclosed herein is an image preprocessing/analysis apparatus using machine learning-based artificial intelligence. The image preprocessing apparatus includes a computing system, and the computing system includes: a processor; a communication interface configured to receive an input image; and an artificial neural network configured to generate first and second preprocessing conditions through inference on the input image. The processor includes a first preprocessing module configured to generate a first preprocessed image and a second preprocessing module configured to generate a second preprocessed image. The processor is configured to control the first preprocessing module, the second preprocessing module, the artificial neural network, and the communication interface so that the first preprocessed image and the second preprocessed image are transferred to an image analysis module configured to perform image analysis on the input image based on the first preprocessed image and the second preprocessed image.

A system for verifying medication doses in a filled medication package comprises an imaging unit to produce at least one image of a filled medication package and a verification unit for receiving the image of the filled medication package. The verification unit comprises a dose locator to determine from the image a location of any dose in the filled medication package, and associate a time period to the location. It also comprises a dose verifier to verify an identity of any dose from the visual characteristics of the image as a function of dose reference profiles. The verification unit compares an identity and time period of the doses of the filled medication package to a prescription and has an interface for producing verification output based on the comparison of the verification unit. A method for verifying medication doses in a filled medication package is also provided.

A vibration reliability calculation apparatus 10 is provided with an image acquisition unit 11 that acquires time-series images of an object that are output by an image capturing apparatus that shoots the object, and a reliability calculation unit 12 that calculates, for a vibration waveform of the object that is derived from a result of comparing one image and another image that constitute the acquired time-series images, a reliability level indicating a reliability of the vibration waveform.

A method of processing a multimedia signal includes the operations of obtaining the multimedia signal, determining at least one kernel to be used for processing the obtained multimedia signal, approximating the determined at least one kernel according to a structure of the at least one kernel, and processing the obtained multimedia signal by using the approximated at least one kernel.