• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.729-755
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• 2012

As an effort to understand alienated gifted students, we investigated learning characteristics and learning tactics of a scientifically gifted student with economic difficulty and physical disadvantage. The student we studied is attending the Saturday Physics Class which is an after school science activity offered by our university. We adopted techniques of qualitative case study. Participant observation was carried out at the field and the interview was done with the participant, his mother, and his teacher of 5th grade. Field documents and self-reports were used to understand the student synthetically. As a result, learning characteristics of the participant could be summarized as a spontaneous learning which originated from the internal motivation and struggle for learning to overcome the sense of inferiority and isolation from the peers. The participant adopted a strategic method for learning to satisfy his learning desire given the circumstance of socioeconomic and physical disadvantage: the three tactics we found were various learning routes, meta-cognitive ability and fervent response.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.79-85
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• 2006

This paper presents a strategy and tactic for robot soccer using furry logic mediator that determines robot action depending on the positions and the roles of adjacent two robots. Conventional Q-learning algorithm, where the number of states increases exponentially with the number of robots, is not suitable for a robot soccer system, because it needs so much calculation that processing cannot be accomplished in real time. A modular Q-teaming algorithm reduces a number of states by partitioning the concerned area, where mediator algorithm for cooperation of robots is used additionally. The proposed scheme implements the mediator algorithm among robots by fuzzy logic system, where simple fuzzy rules make the calculation easy and hence proper for robot soccer system. The simulation of MiroSot shows the feasibility of the proposed scheme.