• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.1-12
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• 2024

Predicting path loss is one of the important factors for wireless network design, such as selecting the installation location of base stations in cellular networks. In the past, path loss values were measured through numerous field tests to determine the optimal installation location of the base station, which has the disadvantage of taking a lot of time to measure. To solve this problem, in this study, we propose a path loss prediction method based on machine learning (ML). In particular, an ensemble learning approach is applied to improve the path loss prediction performance. Bootstrap dataset was utilized to obtain models with different hyperparameter configurations, and the final model was built by ensembling these models. We evaluated and compared the performance of the proposed ensemble-based path loss prediction method with various ML-based methods using publicly available path loss datasets. The experimental results show that the proposed method outperforms the existing methods and can predict the path loss values accurately.

• Educational Technology International
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• v.20 no.2
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• pp.195-221
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• 2019

This study explored middle and high school students' learning experiences using e-textbooks in online learning courses. Data were collected from in-depth interviews. The interviewees for this study were 19 students who enrolled voluntarily in an online mathematics and science inquiry program, actively participated in the online learning. The students generally have high academic achievement and motivation for learning in science and mathematics. Data were analyzed based on a grounded theory approach. As a result, the characteristics of the online learning environment using e-textbooks were conceptualized via three different categories including temporal, spatial, and technical. Such characteristics of the learning environment were able to provoke self-directed learning, extended learning, interactive learning, in-depth learning, improved ICT literacy, and formation of positive emotions and learning habits. Most of the learners showed positive feedback towards the use of e-textbooks, while some mentioned the technical limitations compared to conventional paper-based learning. This study suggested that e-textbooks are likely to induce positive experiences for learners in the context of online learning, so it is necessary to design contents that utilize various functions and advantages of electronic teaching materials in order to use e-textbooks effectively.

• The Journal of Information Systems
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• v.27 no.3
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• Journal of The Korean Association of Information Education
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• v.21 no.4
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• pp.393-401
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• 2017

The purpose of this study is to develop a deep learning based learning system for improving learner's data analytical thinking ability. The contents of the study are as follows. First, deep learning was applied to the discovery learning model to improve data analytical thinking ability. This is a learning method that can generate a model showing the relationship of given data by using the deep learning method, then apply the model to new data to obtain the result. Second, we developed a deep learning based system for DBD learning model. Specifically, we developed a system to generate a model of data using the deep learning method and to apply this model. The research of deep learning based learning system will be a new approach to improve learner's data analytical thinking ability in future society where data becomes more important.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.169-186
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• 2023

The research aimed to identify the effectiveness of an educational program using 3D glasses as a technological innovation on academic achievement and attitude towards elearning in science in the preparatory stage. The research relied on the analytical descriptive approach and the semi-experimental approach. The research tools were the achievement test and the scale of attitude towards e-learning. An educational program was designed and produced using 3D glasses. The study sample consisted of 60 students from the second grade in the preparatory stage at the Rural Jeddah School. The research concluded to the following results: There was a satistically sigificant difference at the level of sig. (0.05) among the -mean scores of the experiemtal and control group students in the post assessment atthe level of achievement in favor of the experiemental group and therewas a satistically sigificant difference at the level of sig. (0.05) among mean scores of the experiemtal and control group students in the post assessment at the level of attitude towards e-learning in favor of the experiemental group. And it was found that the positive effect of the 3D educational program for improving the level of achievement and the attitude towards e-learning for the students. The program allowed the experimental group students to practice self-learning, interaction, and achievement according to the individual differences among them.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 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.