With the recent development of agriculture, the growing area and utilization rate of facilities are increasing, but it is necessary to control and prevent pests, and if the disease is detected at an early stage, appropriate treatment is possible. To this end, researches on control systems using artificial intelligence are being expanded recently, therefore we propose a pest diagnosis system using data acquisition and deep learning through collective intelligence. This study modeled the diagnostic system based on deep learning using the collective intelligence that the user group participates in the prediction of pests arising from the plant cultivation and the data registered by experts in the field. Diagnostic data were collected information on pest diagnosis registered on the Internet and used; the collected data were constructed as a data set that is easy to analyze, through preprocessing, types of crops were classified, pests data were studied through TensorFlow. Most of the researches for the control and prevention of pests are based on web-based expert system. In this paper, we collect data through the collective intelligence and the general public. Especially, when a user uses input question and answers data without a formalized format, it gives wrong prediction; therefore, the preprocessing process was performed for data analysis because it could adversely affect the reliability of the system. After the data collection and preprocessing process is completed, a prediction model is created using TensorFlow, an artificial intelligence open source framework, using the generated data set. The user was allowed to

In this paper, Make use of Finite state machine by way to control intelligence style NPC's action in game. It has many states innumerably that is UT game which is using to experiment environment. This state variable sea bream nation point, other part, me by standard state express and this states because do grouping mode make out and expressed this by hierarchic Finite state machine. Action did, and did so that achieve little more soft action giving precedence than outside action so that interior action allows purpose and check state that is consisted of outside state and pair of interior state and select mode in mode conversion allowed interior action and outside action, and is part that achieve actual action in the case of outside action. Also, Auction type that is supporting in most on-line auction system present is not supporting superior multiplex auction the speed and efficiency and auction newcomers' single auction. Paper that see to solve these problems designs and embodied HFSM.

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When observing behaviors of special-needs students, on average, typical behaviors of common propensity are observed along with unspecific behaviors. Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It is a model that provides data to a user by collecting, classifying and managing the behavior data by letting the machine learn the data regarding the unspecific behaviors of special-needs students that are not typical and well known. Since it requires data regarding various behaviors of special-needs students, the data set shall be organized using the behavior pattern data of special-needs students that currently exists, and the data shall be learned by the proposed model. Web-based machine learning model that collects behaviors of special-needs students in real-time is proposed in order to collect the behavior pattern data regarding the unspecific behaviors of special-needs students. The data can be input using the proposed model, and the user can use it through web-browser. The data can be easily input and revised using the proposed model in any environment that the internet can be accessed. The utilization of data analysis result can be enhanced through the use of data list and tools such as a graph through a web-browser, and the accuracy can be enhanced by learning the result that has been acquired by comparing it with previous data by connecting to the database. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process. In future, the research of real-time data collection and tailored education index data: the common items need to be organized as a data by recording a class time of special schools or special classes and analyzing the behavior patterns of multiple special-needs students in real-time.