• Journal of Smart Tourism
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• 제1권4호
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• pp.3-7
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• 2021

Although Geographic Information Systems (GISs) have commonly been employed as powerful tools for manipulating and displaying spatial data in community-based tourism, a variety of GIS functions still lack the capabilities required to assist multiple decision makers to come to consensual decisions. In this study, I propose an alternative approach: spatial multicriteria decision analysis (SMCDA) that could reflect diverse decision makers' preferences by integrating GISs and multicriteria decision analysis (MCDA). I review the small number of case studies that have employed SMCDA, with a focus on the roles of GISs and MCDA. The methodological integration of GISs and MCDA into multi-spatial decision support systems offers the potential to implement participatory decision-making to solve complex spatial problems in community-based tourism planning, development, and management.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• International journal of advanced smart convergence
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• 제12권1호
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• pp.9-17
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• 2023

Recently, IoT technology has been widely used in many industries. Also research on integrating IoT technology with IoT sensors is actively underway. One of the important challenges in IoT is to support low-latency communication. With the development of communication networks and protocols, a variety of protocols are being used, and their performance is improving. In this paper, we compare the performance and analyze the characteristics of some of the major communication protocols in IoT application, namely MQTT, HTTP, and HTTPS. IoT sensors acquired data by connecting an Arduino equipped with ESP8266 and a temperature and humidity sensor (DHT11). The server measured the performance by building servers for each protocol using AWS EC2. We analyzed the packets transmitted between the Arduino and the server during the data transmission. We measured the amount of data and transfer time. The measurement results showed that MQTT had the lowest data transmission time and data amount among the three protocols.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.1422-1425
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• 2011

These days' mobile phones and their improved multimedia limits making it powerful enough to handle complicated tasks. Image processing related support for mobile devices is extremely comprehensive in mobile cyber museum. A key technical challenge is how to achieve the best-perceived image quality and transmitting data between client and server with given the limited screen size and display bit-depth of the mobile devices. This paper targets image processing features such as capturing rendering zooming, panning and image rotation for 360o view and customized algorithm related image processing with variety of search method i.e. alphabetical, visual search.

• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.22-30
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• 2023

The portable mobile devices with high performance and high speed 5G network activate and explode the demands for ubiquitous information services that remove the limitations of time for the communication and places to request for the information. NN (Nearest Neighbor) search is one of the most important types of queries to be processed efficiently in the information services. Various indexes have been proposed to support efficient NN search in the wireless broadcast system. The indexes adopting Hilbert curve, grid partition or Voronoi diagram enable the clients to search for NN quickly in the wireless broadcast channel. It is necessary that an efficient means to evaluate the performances of various indexes. In this paper, we propose an open framework that can adopt a variety of indexing schemes and evaluate and compare the performances of them. The proposed framework is organized with open and flexible structure that can adopt hybrid indexing schemes extensible to Voronoi diagram as well as simple indexing schemes. With the implemented framework, we demonstrate the efficiency and scalability and flexibility of the proposed framework by evaluating various indexing schemes for NN query.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제26권1호
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• pp.19-30
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• 2023

This study analyzes the tasks selected and implemented by pre-service mathematics teachers to support students' development of epistemic actions. Data was collected from 20 students who participated in a mathematics education curriculum theory course during one semester, and multiple data sources were used to gather information about the microteaching sessions. The study focused on the tasks selected and demonstrated during microteaching by pre-service teachers. The results suggest that providing students with a variety of learning opportunities that engage them in different combinations of abductive and deductive epistemic actions is important. The tasks selected by pre-service teachers primarily focused on understanding concepts, calculation, and reasoning. However, the use of engineering tools may present challenges as it requires students to engage in two epistemic actions simultaneously. The study's findings can inform the development of more effective approaches to mathematics education and can guide the development of teacher training programs.

• Educational Technology International
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• 제9권2호
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• pp.123-142
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• 2008

The purpose of this study was to examine how computer conferencing might facilitate collaborative learning for students to engage in meaningful discussion. The participants in this study consisted of the instructor and the students in a graduate level course. Different sources of evidence were used to triangulate the data: in-depth interviews, content analysis of transcripts of discussion, and other archival data including course syllabus, presentation materials, and lecture notes. Participants perceived web-based learning as collaborative process, providing opportunities to share the idea, respect and evaluate different perspectives, and co-construct new insights. Analysis of the data revealed several challenges related collaboration in a web-based learning environment: absence of a sense of community, technical problems, adaptability to different types of learner, and managing the discussion. The data also indicated that a variety of strategies were used to facilitate learning: building a sense of community, technical support, developing instructional methodologies, class size, and design of the content.

• 센서학회지
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• 제32권6호
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• pp.378-385
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• 2023

Human biosignals provide essential information for diagnosing diseases such as dementia and Parkinson's disease. Owing to the shortcomings of current clinical assessments, noninvasive solutions are required. Machine learning (ML) on wearable sensor data is a promising method for the real-time monitoring and early detection of abnormalities. ML facilitates disease identification, severity measurement, and remote rehabilitation by providing continuous feedback. In the context of wearable sensor technology, ML involves training on observed data for tasks such as classification and regression with applications in clinical metrics. Although supervised ML presents challenges in clinical settings, unsupervised learning, which focuses on tasks such as cluster identification and anomaly detection, has emerged as a useful alternative. This review examines and discusses a variety of ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Neural Networks (NN), and Deep Learning for the analysis of complex clinical data.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.260-267
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• 2023

Due to the development of science and technology in the 4th Industrial Revolution, a variety of content is being developed and utilized through educational courses linked to digital textbooks. Students use smart devices to engage in realistic virtual learning experiences, interacting with the content in digital textbooks. However, while many realistic contents offer visual and auditory effects like 3D VR, AR, and holograms, olfactory content that evokes actual sensations has not yet been introduced. Therefore, in this paper, we designed and implemented 4D educational content by adding the sense of smell to existing content. This implemented content was tested in classrooms through a curriculum-based evaluation. Classes taught with olfactory-enhanced content showed a higher percentage of correct answers compared to those using traditional audio-visual materials, indicating improved understanding.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.107-118
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• 2024

With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.