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This neuro -fuzzy system enables the algorithm to identify performing and non-performing employees as organizations currently use several traditional employee evaluation performance methods that utilizes different approaches that are inaccurate and subjective by nature and usually deficient in approximating the accurate capability and nature of employee performance. Results revealed that this artificial intelligence technique utilizing the neuro-fuzzy profiling system, optimizes the objective function in the employee quality evaluation and determines the most distinctive employees deserving career advancement or those who further need appropriate training and development in the achievement, leadership and behavior categories. Since the coefficients of Neural Network can be tuned to the manager's evaluation results, the logic of the overall judgment can be adjusted to the characteristics of the department. The evaluation of this system is also performed with the same evaluation logic of the objective input values thus, the objectivity and transparency of the evaluation are extremely high. This enables HR and decision makers in the organization to truly understand employee strengths and weaknesses that is also an essential part in promoting a positive company culture unlike the traditional employee performance evaluation methods still being adopted by many organizations at present that is impaired with unreliability and rating errors.

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A user friendly shape design system which fits an user's sense has been developed. The system has dials as operation devices. The dials can be defined by words which express user's ideas. The system uses a neural network to convert user's ideas which are input by the dials to geometric parameters. Using the neural network, relations between user's ideas and geometric parameters can be defined while the relations are difficult to define mathematically or nonlinear. Learning data to define each word's transformation function can be generated by an user's selection of appropriate sets of source and destination shape samples for an idea expressed by the word. This learning procedure enables an user to make neural network parameters reflected the user's sense. The system was evaluated by toy plane design experiments.