• The Journal of Korean Academic Society of Nursing Education
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• v.3 no.2
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• pp.207-213
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• 1997

Basic science teaching and clinical education should be integrated whenever appropriate, and the development of skills, values, and attitudes which are emphasized to the same extent as the acquisition of knowledge in nursing. Problem-based learning provides a students-centered learning environment and encourages an inquisitive style of learning. The purpose of this paper is to review and comment the role of tutors and students on problem-based learning. The use of problem-based learning places a high demand on faculty members' time and support. The role of tutors in Problem-based learning focuses primarily on issues of developing and teaching the curriculum and on organizational implementation and institutionalization. Tutors are an integral part of course planning. Tutors serve as a constant source of feedback on student needs and concerns to the course director and constitute an informal steering committee while the course is in progress. Tutors write cases, develop student evaluation methods, recommend resources, suggest modifications in lectures and laboratories. Students have a limited amount of time available to study what is traditionally defined as the core content of nursing. But, the role of students in Problem-based learning would be active, independent learners and problem-solvers rather than passive recipients of information. Students using a deep level approach attempt to integrate what they learn with what they already know, to understand the meaning underlying the material to be learned, and to look for explanations rather than facts. Students are encouraged, with appropriate guidance, to define their own learning goals, to select appropriate experiences to achieve these goals, and to be responsible for assessing their own learning progress. Problem-based learning is more flexible and meaningful, by encouraging student interaction, and by having a better emotional climate than the conventional learning.

• Korean Journal of Clinical Pharmacy
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• v.33 no.3
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• pp.202-209
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• 2023

Background: With the emergent transition of online learning during the COVID-19 pandemic, the need for online/offline blended learning that can effectively be utilized in a team-based learning (TBL) course has emerged. Methods: We used the online metaverse platforms, Gather and Zoom, along with face-to-face teaching methods in a team-based Introductory Pharmacy Practice Experience (IPPE) course and examined students' learning satisfaction and achievement, as well as their preferences to the learning platforms. A survey questionnaire was distributed to the students after the IPPE course completion. All data were analyzed using Excel and SPSS. Results: Students had high levels of course satisfaction (4.61±0.57 out of 5) and achievement of course learning objectives (4.49±0.70 out of 5), and these were positively correlated with self-directed learning ability. While students believed that the face-to-face platform was the most effective method for many of the class activities, they responded that Gather was the most effective platform for team presentations. The majority of students (64.3%) indicated that blended learning was the most preferred method for a TBL course. Conclusion: Students in a blended TBL IPPE course had high satisfaction and achievements with the use of various online/offline platforms, and indicated that blended learning was the most preferred learning method. In the post-COVID-19 era, it is important to utilize the blended learning approach in a TBL setting that effectively applies online/offline platforms according to the learning contents and activities to maximize students' learning satisfaction and achievement.

• Annual Conference of KIPS
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• 2024.05a
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• pp.711-714
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• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2121-2142
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• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.84-88
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• 2011

In this paper, a new competition strategy for learning vector quantization is proposed. The simple competitive strategy used for learning vector quantization moves the winning prototype which is the closest to the newly given data pattern. We propose a new learning strategy based on k-nearest neighbor prototypes as the winning prototypes. The selection of several prototypes as the winning prototypes guarantees that the updating process occurs more frequently. The design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the proposed learning strategy.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.19-25
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• 2016

There are many free applications that need users to sign up before they can use the applications nowadays. It is difficult to choose a suitable password for your account. If the password is too complicated, then it is hard to remember it. However, it is easy to be intruded by other users if we use a very simple password. Therefore, biometric-based approach is one of the solutions to solve the issue. The biometric-based approach includes keystroke dynamics on keyboard, mice, or mobile devices, gait analysis and many more. The approach can integrate with any appropriate machine learning algorithm to learn a user typing behavior for authentication system. Preprocessing phase is one the important role to increase the performance of the algorithm. In this paper, we have proposed ensemble-by-session (EBS) method which to operate the preprocessing phase before the training phase. EBS distributes the dataset into multiple sub-datasets based on the session. In other words, we split the dataset into session by session instead of assemble them all into one dataset. If a session is considered as one day, then the sub-dataset has all the information on the particular day. Each sub-dataset will have different information for different day. The sub-datasets are then trained by a machine learning algorithm. From the experimental result, we have shown the improvement of the performance for each base algorithm after the preprocessing phase.

• Smart Structures and Systems
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• v.24 no.2
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• pp.233-242
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• 2019

In this paper, an inverse approach based on uniform load surface (ULS) is presented for structural damage localization and quantification. The ULS is excellent approximation for deformed configuration of a structure under distributed unit force applied on all degrees of freedom. The ULS make use of natural frequencies and mode shapes of structure and in mathematical point of view is a weighted average of mode shapes. An objective function presented to damage detection is discrepancy between the ULS of monitored structure and numerical model of structure. Solving this objective function to find minimum value yields damage's parameters detection. The teaching-learning based optimization algorithm has been employed to solve inverse problem. The efficiency of present damage detection method is demonstrated through three numerical examples. By comparison between proposed objective function and another objective function which make use of natural frequencies and mode shapes, it is revealed present objective function have faster convergence and is more sensitive to damage. The method has good robustness against measurement noise and could detect damage by using the first few mode shapes. The results indicate that the proposed method is reliable technique to damage detection in structures.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.223-228
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• 2009

We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.