To identify the relationships between autonomous judgment and the quality of nurses, with the aim of elucidating the lifestyle restructuring support of elderly persons. In order to identify the relationship between nurse performance, authorization qualification acquisition, years of clinical experience, sex difference, a test was given to 651 nurses at stroke medical institutions based on the Scale for Measuring Nurse' Autonomy as Specialists, developed by Dr. Kikuchi in 1997. A result, analyzing issues affecting nurses' autonomy as specialists, there were significant difference of autonomous judgment ability and Nursing Experience of stroke.

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There are osteoporosis and rheumatoid arthritis in the main disease of the bones. Currently, to diagnose that diseases CR, CT and MR images are used in medical fields. Major feature of the diagnostic criteria for rheumatoid arthritis (RA) are still performed manually and there is no method to detect the osteoporosis quantitatively using radiographs until now. To overcome the lack of accuracy and overestimation problem, automatic extraction and quantitative assessment of the region of interest based on image processing technique is required from medical fields In this paper, to analysis the osteoporosis such as quantitative and checking the temporal changes, we proposed a method for segmentation of each phalange regions and registration of same subject which is obtained previous and current of radiograph. We describes some experimental results by using the previous and current image from the same subject.

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Recently, image fusion technique is used on medical field, which is obtained various different modalities. To make a fusion image, registration is important task. However, because many registration techniques need manual operation, increased burden to the doctors and the difference of result becomes a problem. In this paper, we propose a method for image fusion on head CT and MR image to assist surgical plan for the Cyber Knife. Our registration technique is performed two main steps; 1) global registration by using center of gravity, and outline information of image, 2) final registration by use of the mutual information which is selected some VOIs. In this paper, we report some experimental results with registration accuracy and computational processing time.

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In this research we use hidden Markov model in order to represent changes of tourist purposes. We propose an estimation method for tourist purposes based on statistical decision theory. The proposed estimation method minimizes the estimation error rate with reference to the Bayes criterion. We propose a recommendation method for visiting tourist facilities based on statistical decision theory. The proposed recommendation method minimizes the recommendation error rate with reference to the Bayes criterion. We show the effectiveness of the proposed model and methods by some numerical calculation examples.

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This research sets speech scenario as the tension situation for the purpose of grasping the relevant physiological and psychological condition when being tensed. Male university students were made to present about their own stage fright experience as a speech topic. Regarding the relevant condition when tensed, since the inverted U theory has been proposed, the fuzzy reasoning (Product-Sum-Gravity Method) will be applied. The fuzzy rating value calculated by applying fuzzy reasoning from the physiological (blood pressure) and psychological index (state anxiety) of the presenter before the speech and the relationship between self-assessment and peer assessment to the speech was investigated. As a result, compared to presenters who have low fuzzy rating value, those who have high value dominantly shows high tendency for both self-assessment and peer assessment. From this result, the fuzzy rating value from the blood pressure and state anxiety before the speech was considered to be an effective clue to know the relationship between the condition and the performance immediately before the speech.

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In order to predict performance or reliability of a system including a medical information system, prediction methods using a knowledge base is often used, and various methods have been proposed. However, the accuracy of prediction depends on the characteristics of a system, so finding the best predictor is difficult. In software engineering domain, for example, in error-prone module prediction, this situation is the same, so it is necessary to choose the optimal method for each target system. Zhimin has proposed a selection technique of suitable predictors. In this paper, we modified Zhimin's technique in order to perform mining more appropriately, and we proposed the usage of the features of system characteristics. We performed three experiments based on original data and PROMISE data, and showed the effectiveness our proposal. This technique can be applied to various optimal predictor selection problems.

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There is much previous research on maintenance. Markov decision processes with unknown states are used in order to represent the maintenance problem. In the previous research a fixed profit is considered. In general the profit depends on economic states. In this research we propose a new maintenance method based on statistical decision theory. The proposed maintenance method maximizes the total reward of the maintenance problem considering profits and economic states with reference to a Bayes criterion. We show the effectiveness of the method by some numerical calculation examples. In the examples appropriate repairs are selected depending on economic states.

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