• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.162-176
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• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.17-22
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• 2001

All of the students must trace to the predetermined course of curriculum in traditional education system. There are some absurdity that nearly the same subjects, which are different each other, are treating partially the same contents. Therefore This paper proposes the cyber-educational system which constructs curriculum divided to modular parts for efficient self-directed learning in performing internet-based remote education. The preliminary experiment for two subjects shows that the proposed system gives about 9.4％ of time reduction than traditional system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.155-156
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• 2019

Traditional machine learning based emotion analysis methods such as Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) are less accurate. In this paper, we propose an improved kNN classification method. Improved methods and data normalization achieve the goal of improving accuracy. Then, three classification algorithms and an improved algorithm were compared based on experimental data.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• Journal of Educational Research in Mathematics
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• v.10 no.2
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• pp.229-246
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• 2000

The purpose of this paper is to compare and analyze the two different teaching methods of elementary mathematics in the traditional method and in the constructive view. To do so, the actual class in the constructive view has been made for about four months using a class of 45 students in the second grade of an elementary school. After the class was finished, we collected diverse data from the class, such as the responses from the children(self-evaluation, mathematics diary, observation by the investigator, daily report), class evaluation report by other teacher and so on. The results of this research are as follows: First, the traditional class reaches at the goal of learning in a unit time because the class is guided by the teacher but the class in the constructive view is a little flexible because it is contextual. Second, in the constructive process of mathematical knowledge we knew that small group activities or discussion without intervention of teacher was often ended in exhaustive argument without arriving at valid social consensus. Third, the attitude in mathematics was changed from the passive one to the self-regulated ones. Fourth, the class in the constructive view could extend not only the ability of mathematical communication but also the ability of self-directed learning of children. Fifth, it was a considerable change the role of teacher, that is, guide of instruction instead of unique specialist in the classroom. Sixth, finally, the evaluation was made after finishing a unit class in the traditional instruction but it was integrated in a class in a constructive view.

• The Korean Journal of Physiology and Pharmacology
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• v.23 no.5
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• pp.305-310
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• 2019

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.1-9
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• 2022

The notion of outcome-based educational paradigm and its adaptability for higher education has become a recent growing and quite stirring trend. In the year 2017-18, this educational philosophy has been embraced by some of the higher educational institutions in Pakistan as well. This research attempts to investigate OBE and non-OBE systems in the context of students learning outcomes and academic attainment levels in engineering education in Pakistan. The study has been conducted on undergraduate students of MUET, Jamshoro, Sindh Pakistan. The students of the software engineering department are taken as the sample. Student cohorts are formed i.e., OBE and non-OBE (traditional/teacher-centered approach) cohorts. The summative assessments of semester exams are used for data analysis descriptive statistics and independent samples t-test is performed to set up the group statistic. The findings of this study show that, in terms of students' performance, the OBE system outperforms the traditional system and this transition in engineering institutions might be beneficial in the future.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.125-134
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• 2015

This paper designs a peer tutoring in Flipped Learning environments for effective game programming(TGF), suggests a result of survey and a lesson learned from game programming in terms of students' and professors' perspectives in hands-on program training using Snake game programming as an applied example. The TGF is more effective than the traditional classroom to achieve the learning goals of game programming course.

• Journal of Fisheries and Marine Sciences Education
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• v.13 no.2
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• pp.194-211
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• 2001

This study was conducted to investigate the effects of cooperative learning applying Jigsaw II on learner's self-regulated learning ability, achievements, self-esteem & cooperation. 12 graders were assigned to experimental group(applying Jigsaw II treatment) & control group(applying traditional instructional treatment). Experimental group was trained to ask comprehension & thought-provoking questions on the material when in tutor role & to explain material to group members when acting as tutee. Tutorial sessions followed over 8-week treatment. As a results, Experimental group outperformed control group on ability to construct learner's self-regulated learning ability, achievements, self-esteem & cooperation both during their tutorial interaction & on written measures.

• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.95-102
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• 2008

In this paper through the implementation of cooperative learning using SDG, education puzzle actually applies to computer training. SDG(Single Display Groupware) which one computer display have a multi-input devices can work as a collaborative system. Learners are performing together through SDG-based cooperative learning system. SDG cooperative learning with a multi-input device is superior to traditional learning with individual. We have implementation of the puzzle game with this fact. This system through effective education and raising their children's education participation rate will be able to do.