• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.21-30
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• 2024

Several anti-inflammatory small molecules have been found in the process of the inflammatory response, and these small molecules have been used to treat some inflammatory and autoimmune diseases. Numerous tools for predicting anti-inflammatory peptides (AIPs) have emerged in recent years. However, conducting experimental validations in the lab is both resource-intensive and time-consuming. Current therapies for inflammatory and autoimmune disorders often involve nonspecific anti-inflammatory drugs and immunosuppressants, often with potential side effects. AIPs have been used in treating inflammatory illnesses like Alzheimer's disease and can limit the expression of inflammatory promoters. Recent advances in adverse incident predictions (AIPs) have been made, but it is crucial to acknowledge limitations and imperfections in existing methodologies.

• Journal of Integrative Natural Science
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• v.16 no.4
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• pp.111-122
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• 2023

Invasive species are organisms that are introduced into places outside of their natural distribution range. The global pet trade is facilitating the introduction of invasive species into new countries and areas. Among the introduced alien species, turtles are one of the most common animal groups whether lives in wetland ecosystems, such as wetlands or reservoirs. Like other countries around the world, exotic turtles is becoming a growing concern for the wetland ecosystem in South Korea. In this study, we report new reports of subspecies of Painted turtle (Chrysemys spp.): Chrysemys picta marginata, C. p. bellii and C. dorsalis, from the reservoirs in downtown Cheongju and Gwangju, South Korea. We used morphological features, such as the characteristics of the legs, plastron, and carapace, to identify the turtles. It is assumed that all turtles were artificially released into nature. Considering the increasing number of reports on the introduction of alien invasive turtles in Korean wetlands, we recommend the formulation of an immediate and systematic management plan for pet trades and organized continuous monitoring programs.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Journal of Korean Elementary Science Education
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• v.25 no.2
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• pp.167-178
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• 2006

The purpose of this study was to explore characteristics of sixth grade students' opistemological beliefs in science and the relationship to learner's characteristics: learning motivation, learning strategies, and logical thinking. The subjects were 265 sixth graders and data was collected through two types of questionnaires, translated and modified by researchers: opistemological beliefs regarding science, learning motivation & strategies. The results of this study were as follows. The students believed that the goals of science were related to activations such as 'Science is experiment', or 'Science is invention: These beliefs were connected with the emphasis of science classes or the focus of the science curriculum. However, the students' beliefs related to the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge were oriented to modern opistemological views. Moreover, the beliefs were meaningfully related to students' characteristics: learning motivation, learning strategies, and logical thinking. Among the students' characteristics, logical thinking was especially related to all of the factors of students' beliefs: the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge. However, the students who believed that scientific knowledge came from scientists, science teachers, or science textbooks had high levels of self-efficacy. Therefore, the belief that scientific knowledge is formed by self-discovery, in order to generate high self-efficacy, needs to be encouraged. From the results, it is possible to check the orientation of current science education based on the students' opistemological beliefs. In addition, the resources can be accumulated for persevering in our efforts to achieve a positive orientation for science education.

• Communications of Mathematical Education
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• v.24 no.1
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• pp.259-282
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• 2010

In this paper, we introduce a modified Moore Method designed for the multivariable calculus course, and discuss about the effective teaching and learning method by observing the changes in the understanding of students' learning and the effects on students' learning in the class implemented by this modified Moore Method. This teaching experiment research was conducted with the 15 students who took the multivariable calculus course offered as a 3 week summer session in 2008 at H University. To guide the students' active preparation, stepwise course materials structured in the form of questions on the important mathematical notions were provided to the students in advance. We observed the process of the students' small-group collaborative learning activities and their presentations in the class, and analysed the students' class journals collected at the end of every lecture and the survey carried out at the end of the course. The analysis of these results show that the students have come to recognize that a deeper understanding of the subjects are possible through their active process of search and discovery, and the discussion among the peers and teaching each other allowed a variety of learning experiences and reflective thinking.

• Journal of Gifted/Talented Education
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• v.11 no.3
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• pp.23-44
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• 2001

In the current classrooms a teacher has been merely able to inculcate the procedural knowledge of how-and-what. In doing so, however, we lose sight of the essence of "doing science."Though desire of the gifted children is qualitatively different from that of normal children, it is an undesirable reality that we have not developed sufficient researches and programs in conformity with the necessary desire and demand of the gifted children. Curriculum for gifted children in the domain of science necessitates markedly the specializations for the specific areas of the contents, the processes, and the products of studies. In an effort to provide the optimum learning experience for the gifted, this paper deals with the development of project-and-discovery-based science program, its method of application to the real field of education, and its effect, however limited and partial that effect may be. What this study has found are the following: on the one hand, the students acquired and developed the higher levels of thinking when they were under the influence of project-and-discovery-based science program that dealt with concrete real-world problems and issues; on the other, the students were capable of solving creatively the complex and real problems through small group activities. This study also suggests the possible implications of project-and-discovery-based science program: the students can not only learn the contents of study but also apply them creatively; the students can cultivate critical thinking skills that can be a fundamental base for a life-time leaner; the students can naturally acquire the abilities of communication and coordination. Project-and-discovery-based program is currently used in the various disciplines. However, the field of gifted education does not yet implement this type of program. So the overall contribution of this study is to show the successful implementation of project-and-discovery-based science program in developing optimal teaming experience for gifted children in the domain of science, since this type of study is most compatible with the characteristic of the gifted children. children.

• School Mathematics
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• v.18 no.4
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• pp.839-856
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• 2016

In 2009 revision and 2015 revision mathematics national curriculum, 'proof' was moved to high school from middle school in consideration of the cognitive development level of students, and 'proof by contradiction' was stated in the "success criteria of learning contents" of the first year high school subject while it had been not officially introduced in $7^{th}$ and 2007 revision national curriculum. Proof by contradiction is known that it induces a cognitive conflict due to the unique nature of rather assuming the opposite of the statement for proving it. In this article, based on the logical, mathematical and historical analysis of Proof by contradiction, we looked about the introductions and the applications of the current textbooks which had been revised recently, and searched for improvement measures from the viewpoint of discovery, explanation, and consilience. We suggested introducing Proof by contradiction after describing the discovery process earlier, separately but organically describing parts necessary to assume the opposite and parts not necessary, disclosing the relationships with proof by contrapositive, and using the viewpoint of consilience.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.