Abstract: A method for extracting only a portion stained with a specific dye from a pathology slide stained with a mixed dye in which various types of dyes are mixed, training an artificial neural network, and determining a pathology image stained by using various staining techniques; and a computing system performing same. A neural network learning system generates and learns a learning data set including M pieces of individual learning data (where M is a natural number greater than or equal to 2).

Abstract: An artificial neural network is trained using pathology slides, obtained by staining serial sections of a single pathological specimen with a variety of different staining reagents, such that a disease can be determined with a high degree of accuracy. A method for training an artificial neural network, the method includes: generating a training data set including individual training data; and training the artificial neural network on the basis of the training data. A step of generating the training data set includes generating, for all m where 1<=m<=M, m-th training data to be included in the training data set. Generating the m-th training data includes acquiring first to Nth pathology slide images, the slide images being pathology slide images obtained by staining serial sections of a single pathological specimen with different staining reagents; and generating the m-th training data on the basis of the first to Nth pathology slide images.

Abstract: An allocation search device includes: a first sample output unit that outputs, on the basis of past event occurrence data, a first main sample that may occur under a predetermined condition and a first auxiliary sample that may occur under a similar condition; an allocation plan creation unit that creates a plurality of allocation plans of resources for an occurring event; a first allocation evaluation unit that evaluates whether or not each of the plurality of allocation plans satisfies a predetermined evaluation criterion in a case where the first main sample and the first auxiliary sample are applied to each of the plurality of allocation plans; a second sample output unit that outputs, on the basis of latest event occurrence data, a future second main sample that may occur under the predetermined condition and a future second auxiliary sample that may occur under the similar condition; and a second allocation evaluation unit that, in a case where the second main sample and the second auxiliary sample are

Abstract: A display control device controls a display unit to display position information of an emergency vehicle, a predictive distribution of an occurrence point, and a risk level, the occurrence point representing a point at which a call for the emergency vehicle occurs, the risk level being determined depending on a time required for the emergency vehicle to arrive at the occurrence point after the call for the emergency vehicle occurs or a distance between the emergency vehicle and the occurrence point.

Abstract: A comparison unit 68 compares target data to be inferred with a learning data group that is data used for learning of the inference model, and determines that an inference result is uncertain when a comparison result does not satisfy a fixed criterion. A notification unit 70 notifies a user that the inference result is uncertain in addition to the inference result when the inference result is determined to be uncertain.

Abstract: A business support device acquires at least business information including order reception/loss information relating to each commercial material for a company that is a business target of the commercial material, area information of a company, a business type of the company, and a company scale of the company, generates data used for prediction of an order reception score for each company and for each commercial material, predicts the order reception score for each commercial material and for each company using the generated data, and displays the predicted order reception score.

Abstract: A waypoint setting device sets a waypoint on the basis of a predictive distribution representing a demand prediction of occurrence points indicating positions from which a mobile object is called, position information indicating a position of a target mobile object that is a mobile object for which the waypoint is to be set, and destination information indicating information regarding a destination to which the target mobile object is to be moved. Then, the waypoint setting device outputs the set waypoint.

Abstract: A hospital that has a high possibility of accepting a user can be predicted with good precision. An estimating method is an estimating method of estimating a prospect of a hospital accepting a user at a first time. In the estimating method, a prospect of accepting the user at the first time is estimated by correlating a reason of the hospital rejecting acceptance of a user at a second time, and an amount of time from the second time to the first time.

Abstract: Features serving as a basis for prediction can be explained. In an explanation creating method, with regard to a prospect of each of hospitals accepting a user, prediction results are found in advance using each of features including information relating to a reason of each of the hospitals rejecting acceptance of the user. Processing prescribed in advance is performed regarding each of features to be taken as an object, out of each of the features, and finds prediction results following modification. On the basis of a difference between prediction results using each of the features and prediction results following modification found regarding the features to be taken as an object, in a case in which there is a difference that satisfies a standard set in advance regarding each of the features to be taken as an object, creating an explanation regarding prediction results relating to the reason of rejection regarding this feature.

Abstract: Provided is a technique for efficiently predicting the number of occurrences of emergency medical service requests in a target area. At least patient attributes being attributes of patients, date/time information indicating dates and times of occurrences of emergency medical service requests, position information indicating places of the occurrences of the emergency medical service requests, and illness/injury information indicating illnesses and injuries that caused the emergency medical service requests are obtained. The illnesses and injuries are categorized into illness/injury groups, on a basis of a likelihood of occurrences of the illnesses and injuries in the illness/injury information obtained by bringing the patient attributes, the position information, and the date/time information into association with one another.