• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Fisheries and Aquatic Sciences
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• v.25 no.8
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• pp.450-461
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• 2022

A randomized, double-blind, and placebo-controlled clinical study was used to determine the cognitive functions related to working memory (WM) and antioxidant properties of fermented Laminaria japonica (FLJ) on healthy volunteers. Eighty participants were divided into a placebo group (n = 40) and FLJ group (n = 40) that received FLJ (1.5 g/day) for 6 weeks. Memory-related blood indices (brain-derived neurotrophic factor, BDNF; angiotensin-converting enzyme; human growth hormone, HGH; insulin-like growth factor-1, IGF-1) and antioxidant function-related indices (catalase, CAT; malondialdehyde, MDA; 8-oxo-2'-deoxyguanosine, 8-oxo-dG; thiobarbituric acid reactive substances, TBARS) were determined before and after the trial. In addition, standardized cognitive tests were conducted using the Cambridge Neuropsychological Test Automated Batteries. Furthermore, the Korean Wechsler Adult Intelligence Scale (K-WAIS)-IV, and the Korean version of the Montreal Cognitive Assessment (MoCA-K) were used to assess the pre and post intake changes on WM-related properties. According to the results, FLJ significantly increased the level of CAT, BDNF, HGH, and IGF-1. FLJ reduced the level of TBARS, MDA, and 8-oxo-dG in serum. Furthermore, FLJ improved physical activities related to cognitive functions such as K-WAIS-IV, MoCA-K, Paired Associates Learning, and Spatial Working Memory compared to the placebo group. Our results suggest that FLJ is a potential candidate to develop functional materials reflecting its capability to induce antioxidant mechanisms together with WM-related indices.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Communications of Mathematical Education
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• v.37 no.2
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• pp.183-197
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• 2023

Mathematical listening ability(MLA) refers to the capability to listen to speech languages that contain mathematical principles and concepts and understand their meanings, distinguishing it from daily life and listening in other subject classes. In this study, I investigated 834 elementary school students' MLA adapting a MLA survey items. Through the statistical analysis results of the survey, I confirmed that students' MLA had a significant correlation with gender, grade, and school location. Female students' MLA was statistically significantly higher than that of male students. MLA increased with grade and then decreased again in 6th grade. In addition, students' MLA was statistically significant differences according to the location of the school. The results of this study might be used as the basis for follow-up research and development of teaching and learning materials related to MLA.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.3-18
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• 2023

The purpose of this study is to identify the difference between the current retention level and the required level of engineering students' career competency that they think they need based on their perceptions. Ultimately, the results of this study are used as basic data when designing the major/general education program and the curricular/extra-curricular career program. The task of this study is to identify the difference between the current retention level and the required level of engineering students' career competency. And based on this, it is to confirm the educational needs of engineering students for the career competency. For this purpose, literature research on career competency education in universities was reviewed in the theoretical background. Next, previous studies on career competency and sub-competence derived from career competency-related studies and detailed questions were analyzed. Based on this, an initial evaluation tool for career competency of engineering students was developed. Finally, through expert review, a career competency evaluation tool with a total of 43 items in 10 competency groups was developed. A career competency evaluation questionnaire was conducted for 197 engineering students who participated in the 2022 Engineering Education Festa, and as a result of the IPA analysis, 'global competency' was found to be the competency with the largest difference between importance and execution. Next, 'major job competency' and 'career development competency' appeared in order. Reflecting the results of this study, it is expected that mutually organic design of competency-based liberal arts curriculum and major curriculum that can cultivate global competency, major job competency, and career development capability will be carried out through learning activities and field practice.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.80-80
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• 2002

The main thesis of this article is that the decisive point of creativity education is the cultivation of critical thinking capability. Although the narrow conception of creativity as divergent thinking is not subsumed under that of critical thinking, the role of divergent thinking is not so crucial in the science context of creative problem-solving. On the contrary, the broad conception of creativity as focusing on the reference to utility and the third conception of creativity as a process based on the variation and combination of existing pieces of information are crucial in creative problem-solving context, which are yet subsumed under that of critical thinking. The emphasis on critical thinking education is connected with the characteristics of contemporary knowledge-based society. This rapidly changing society requires situation-adaptive or situation-sensitive cognitive ability, whose core is critical thinking capability. Hence, the education of critical thinking is to be centered on the learning of blowing-how and procedural knowledge but not of knowing-that and declarative knowledge. Accordingly, the learning of critical thinking is to be headed towards the cultivation of competence but not just of performance. In conclusion, when a rational problem-solving through critical and logical thinking turns out consequently to be novel, we call it creative thinking. So, creativity is an emergent property based on critical and logical thinking.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.81-90
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• 2002

The main thesis of this article is that the decisive point of creativity education is the cultivation of critical thinking capability. Although the narrow conception of creativity as divergent thinking is not subsumed under that of critical thinking, the role of divergent thinking is not so crucial in the science context of creative problem-solving. On the contrary, the broad conception of creativity as focusing on the reference to utility and the third conception of creativity as a process based on the variation and combination of existing pieces of information are crucial in creative problem-solving context, which are yet subsumed under that of critical thinking. The emphasis on critical thinking education is connected with the characteristics of contemporary knowledge-based society. This rapidly changing society requires situation-adaptive or situation-sensitive cognitive ability, whose core is critical thinking capability. Hence, the education of critical thinking is to be centered on the learning of blowing-how and procedural knowledge but not of knowing-that and declarative knowledge. Accordingly, the learning of critical thinking is to be headed towards the cultivation of competence but not just of performance. In conclusion, when a rational problem-solving through critical and logical thinking turns out consequently to be novel, we call it creative thinking. So, creativity is an emergent property based on critical and logical thinking.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.61-78
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• 2019

This study is about the effect of the founder's self-efficacy on the sales of the founding company by focusing on the factors that are currently emphasized in the founding education. In particular, this paper starts from the consciousness of the problem that the education that is being implemented to achieve the purpose of successful start-up among various government-based start-up support projects is failing to produce many start-up failures. Entrepreneurs cannot be assessed by objective financial data, but there is a high degree of uncertainty that should be determined based on their personal and learning abilities. In addition, many previous studies, which are likely to be successful when there is a high self-efficacy in a specific field due to the influence of factors such as personal experience or learning, will answer the direction of support for start-up companies. This study focuses on the impact of the founder's self-efficacy on the sales of the founding firms, especially the sales that are the key to the survival of the founding firms. This study has six major studies. First, to analyze whether the self-efficacy of entrepreneurs with respect to entrepreneurship affects the sales of entrepreneurs. Second, to analyze whether the self-efficacy of entrepreneurs with respect to market orientation affects the sales of entrepreneurs. Analysis of whether the founder's self-efficacy affects the sales of the founding firms. Fourth, analysis of whether the founder's self-efficiency affects the sales of the founding firms' understanding of management environment changes. An analysis of whether efficacy affects the sales of a start-up company, and sixth, an analysis of whether the founder's self-efficacy of business model building ability affects the sales of a start-up company. As a result of the empirical analysis, this study found that the self-efficacy of entrepreneurs on product differentiation capability and business model building capacity had a positive influence on the sales of entrepreneurs. The self-efficacy had a positive effect on self-efficacy, and the customer orientation had a positive effect on self-efficacy on business model building capacity. Also, it was confirmed that a path exists between the components of self-efficacy and that self-efficacy through the path has a positive effect on the sales of the start-up company. Therefore, the results of this study suggest the implications of establishing such a path and strengthening self-efficacy to create the survival and start-up performance of a start-up company if the goal of the start-up company is to survive when implementing various support projects for the start-up company.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.