• International Journal of Contents
• /
• v.15 no.4
• /
• pp.1-7
• /
• 2019

As the recent developments of artificial intelligence, particularly machine-learning, impact every aspect of society, they are also increasingly influencing creative fields manifested as new artistic tools and inspirational sources. However, as more artists integrate the technology into their creative works, the issues of diversity and fairness are also emerging in the AI-based creative practice. The data dependency of machine-learning algorithms can amplify the social injustice existing in the real world. In this paper, we present an interactive visualization system for raising the awareness of the diversity and fairness issues. Rather than resorting to education, campaign, or laws on those issues, we have developed a web & ML-based interactive data visualization system. By providing the interactive visual experience on the issues in interesting ways as the form of web content which anyone can access from anywhere, we strive to raise the public awareness of the issues and alleviate the important ethical problems. In this paper, we present the process of developing the ML-based interactive visualization system and discuss the results of this project. The proposed approach can be applied to other areas requiring attention to the issues.

• School Mathematics
• /
• v.12 no.1
• /
• pp.17-26
• /
• 2010

The purposes of this study were to understand mathematics teachers' difficulties under the context of community and to contribute to the research on professional development using a partnership between a high school and a university. I examined what struggles mathematics teachers had in building a learning community. I used data from a project in South East area in U.S.A. Three student teachers, three mentor teachers, and a university teacher participated in this study. Data sources included cluster meeting observations, interviews, and documents (such as open-ended surveys and e-mail responses). Data were analyzed using case study and narrative analysis methods. The results showed that the participants had power issues, issues about selecting topics to discuss, criticizing others, sharing goals, and managing time and the number of members.

• Journal of Information Science Theory and Practice
• /
• v.6 no.1
• /
• pp.45-64
• /
• 2018

The global information economy is transforming the way people connect with each other, learn new things, and contribute to the knowledge society. With the online platform, library services have also expanded beyond face to face interaction. Although studies of virtual reference services have been made in different parts of the world, a case study discussing various forms of online reference engagement in Kazakhstan has not been written. While most of the theories on connectivism emphasize the context of instruction, the researchers of this paper discussed the tenets as they relate to online engagement. Applying the theory of connectivism, this paper explores through a mixed method the use of various online platforms to enhance engagement connecting library users to information. Findings revealed that differences in patterns of interactions as to platforms, types of queries, and users reveal that students, faculty, and other members of the academic community served by the library have various preferences for communication. The case study further showed that respondents have not maximized the use of VLS but interest in using both synchronous and asynchronous services is clear. Finding connections between sources of information, creating useful information patterns, is essential in learning. Amplifying awareness on the use of VLS giving emphasis to the unique features of each service is useful in order to enable students to see how this platform facilitates learning.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.1023-1025
• /
• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.

• Korean Journal of Remote Sensing
• /
• v.35 no.1
• /
• pp.93-115
• /
• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.237-245
• /
• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Journal of Astronomy and Space Sciences
• /
• v.41 no.3
• /
• pp.149-158
• /
• 2024

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hol-eneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

• Structural Engineering and Mechanics
• /
• v.91 no.6
• /
• pp.583-589
• /
• 2024

Accurately predicting the strength of concrete is vital for ensuring the safety and durability of structures, thereby contributing to time and cost savings throughout the design and construction phases. The compressive strength of concrete is determined by various material factors, including the type of cement, composition ratios of concrete mixtures, curing time, and environmental conditions. While mix design establishes the proportions of each material for concrete, predicting strength before experimental measurement remains a challenging task. In this study, Abrams's law was chosen as a representative investigative approach to estimating concrete compressive strength. Abrams asserted that concrete compressive strength depends solely on the water-cement ratio and proposed a logarithmic linear relationship. However, Abrams's law is only applicable to concrete using cement as the sole binding material and may not be suitable for modern concrete mixtures. Therefore, this research aims to predict concrete compressive strength by applying various conventional regression analyses and machine learning methods. Six models were selected based on performance experiment data collected from various literature sources on different concrete mixtures. The models were assessed using Root Mean Squared Error (RMSE) and coefficient of determination (R²) to identify the optimal model.

• Journal of the Korean Society of Earth Science Education
• /
• v.13 no.2
• /
• pp.186-195
• /
• 2020

The purpose of this study is to investigate the effect of jigsaw cooperative learning strategy on elementary students' task commitment and creative personality. This study utilized experimental method of comparing the effect of jigsaw strategy between treatment and comparative group. Twenty four 6^th grade students in treatment group experienced the jigsaw cooperative learning for twelve-weeks during 2019 school year whereas students in comparetive group did not. Task commitment and creative personality test result were collected from both groups as main data sources and mixed-method was used to analyze the data. The results of the study were as follows. First, the treatment group students' task commitment score was significantly higher than comparative group of students' score(p<.05). Second, the treatment group students' creative personality efficacy score was significantly higher than comparative group students' (p<.05). Third, students who experienced the jigsaw cooperative learning-based science lesson had positive perceptions about the lesson.

• Journal of the Korean earth science society
• /
• v.38 no.5
• /
• pp.357-366
• /
• 2017

Urban planning and engineering undergraduate students need to understand the earth physical systems and that how human beings interact with the earth systems to planning and engineering urban area. The eco-friendly or geo-friendly design and planning of an urban area is a critical issue not only for economic benefits but more importantly for the sustainable future of urban life. However, little study has been done dealing with the urban engineering students' understanding of the earth as a system and what pedagogical approach is appropriate to improve their understanding of the earth as a system. This study is to investigate the impact of a purposely designed ESS course on urban engineering students' understanding of the earth as a system and their perceptions about the instructional approaches of the course on their learning competency. This study utilized a mixed-methodology with three main data sources: concept maps, student's perception survey about their learning competency, and course contents. Both the survey and concept maps were analyzed quantitatively as well as qualitatively. The result of this study showed that the urban engineering students' experience of team-based research about the topic they chose based on their own interest had a positive impact on their understanding of the earth as a system and their learning competency. The results of this study suggest that structuring and presenting the earth system contents in the context of engineering students' understanding and their future career be effective not only for the improvement of students' content knowledge but also for the enhancement of their learning competency such as creativity and problem-solving skills in everyday life situation.