• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.7-13
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• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1297-1304
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• 2018

Online education, commonly referred to as distance education, has developed rapidly. However, it is questionable whether such distance education has been applied to various educational fields and has achieved satisfactory results in terms of learning effect. One of the reasons for not maximizing the benefits of distance education is non-dynamicity in the production and application of educational content. Educational contents production is made up of collaborative work between the instructor who is the contents expert and the developer who is the production expert. For this reason, existing researches have also concentrated on the improvement of each educational effect. In this paper, we propose to replace a production expert from a developer to an instructor. At this time, the important point is that the educational contents produced by the instructor, who is a development non-expert, should still be able to be maintained with high-quality contents utilizing the characteristics of the web. For this purpose, the production system was developed based on open source to maintain the quality similar to the educational contents developed by the production expert. This will increase the effectiveness of education by applying the developed Smart-Blended Learning System to various educational sites.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.58-61
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• 2003

This paper proposes e-Learning environments far digital circuit experiment. The e-Learning environments are implemented as a WBT system that includes the circuits monitoring system and the students management system. In the WBT client-server system, the instructor represents the server and students represent clients. The client computers are equipped with a digital circuit training board and connected to the server on the World Wide Web. The training board consists of a Programmable Logic Device (PLD) and measuring instruments. The instructor can reconfigure the PLD with various circuit designs from the server so that students can investigate signals from the training board. The instructor can monitor the progress of the students using Joint Test Action Grouo(JTAG) technology. We implement the WBT system and a courseware fo digital circuits and evaluation the environments.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of Engineering Education Research
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• v.27 no.3
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• pp.3-13
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• 2024

Since the launch of ChatGPT, many college students used it extensively in various ways in their curricular learning activities. This study investigates the utilization of ChatGPT in the curriculum of first and second-year engineering students, aiming to examine its influence from a learner perspective. We explored how ChatGPT is used in each subject and learning activity to understand how learners perceive the use of ChatGPT. From the survey data on engineering college students at E university, we examined students' perception on 'shortening time to perform tasks' through ChatGPT, 'dependence on ChatGPT', 'their contribution to individual capacity building', and 'their influence on academic grade'. The majority of students reported extensive use of ChatGPT for learning activities, particularly showing high dependency in liberal arts subjects and coding-related activities. While the use of ChatGPT in liberal arts was seen as not contributing to the enhancement of individual capacity, its use in coding was positively evaluated. Furthermore, the contribution of ChatGPT to the creativity in report writing tasks was highly rated. These findings offer several important implications for the use of AI tools like ChatGPT in engineering education. Firstly, the positive impact of ChatGPT's high usability and individual-capacity enhancement in coding should be expanded to other areas of learning. Secondly, as AI technology progresses, the contribution of AI tools compared to learners is expected to increase, suggesting that students should be encouraged to effectively use AI tools to achieve their learning objectives while maintaining a balanced approach to avoid overreliance on AI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.670-673
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• 2020

Recent advances in artificial intelligence have allowed for easier sentiment analysis (e.g. positive or negative forecast) of documents such as a finance reports. In this paper, we investigate a method to apply text mining techniques to extract in the financial report using deep learning, and propose an accounting model for the effects of sentiment values in financial information. For sentiment analysis with keyword detection in the financial report, we suggest the input layer with extracted keywords, hidden layers by learned weights, and the output layer in terms of sentiment scores. Our approaches can help more effective strategy for potential investors as a professional guideline using sentiment values.

• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.761-764
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• 1993

In this paper we consider techniques for reducing the generated number of rules in learning fuzzy controllers of the state-space action-reinforcement type that can be simply implemented and that behave well in the presence of process noise. Fewer rules lead to better performance, less contradiction in controller action estimation, smaller required execution-time and make it easier for a human to comprehend the generated rules and possibly intervene.