• Knowledge Management Research
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• v.18 no.4
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• pp.63-96
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• 2017

In a competitive organizational environment, members view completing tasks to perfection without mistakes as the requirement for success and good evaluation from others. However, unrealistically strong perfectionism can have negative effects on members' psychological states and organizational performance. This study focuses on evaluative concerns perfectionism, the negative aspect of perfectionism, based on positive psychology theory to explore the positive mechanism that comes into place in the process of strengthening organization members' resilience. For this purpose, we performed a moderated moderation analysis to investigate whether the moderating effect of leaders' social support (primary moderator) is moderated by their emotional intelligence (secondary moderator). To verify our research model, we conducted a two-part survey of 252 employees in various industries with a time interval to minimize the common method bias. Job rank and positive affectivity were set as control variables to identify the clear causal relationship among variables. Our findings showed that first, evaluative concerns perfectionism reduced resilience; second, leaders' social support positively moderated the relationship between evaluative concerns perfectionism and resilience; and third, the leaders' emotional intelligence positively moderated the two-way interaction between evaluative concerns perfectionism and the leaders' social support, confirming a moderated moderation. Our findings suggest the need for leaders to assume multidimensional roles to enhance the positive traits of organization members, especially in today's complex business environment. The implications of our findings are further discussed in relation to knowledge management and the development of organization members who exhibit evaluative concerns perfectionism, along with suggestions for future research.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.481-486
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• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using differential haar-like feature, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: horizontal haar-like feature and vertical haar-like feature. For the next, we calculate the proposed feature vector using differential haar-like method. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using the differential area of horizontal and vertical haar-like. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method for the pedestrian and non-pedestrian.

• Information Systems Review
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• v.21 no.4
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• pp.143-156
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• 2019

Types of innovation can be categorized into simplification, information, automation, and intelligence. Intelligence is the highest level of innovation, and RPA can be seen as one of intelligence. Robotic Process Automation(RPA), a software robot with artificial intelligence, is an example of intelligence that is suited for simple, repetitive, large-scale transaction processing tasks. The RPA, which is already in operation in many companies in Korea, shows what needs to be done to naturally focus on the core tasks in a situation where the need for a strong organizational culture is increasing and the emphasis is on voluntary leadership, strong teamwork and execution, and a professional working culture. The introduction was considered naturally according to the need to find. Robotic Process Automation, or RPA, is a technology that replaces human tasks with the goal of quickly and efficiently handling structural tasks. RPA is implemented through software robots that mimic humans using software such as ERP systems or productivity tools. RPA robots are software installed on a computer and are called robots by the principle of operation. RPA is integrated throughout the IT system through the front end, unlike traditional software that communicates with other IT systems through the back end. In practice, this means that software robots use IT systems in the same way as humans, repeat the correct steps, and respond to events on the computer screen instead of communicating with the system's application programming interface(API). Designing software that mimics humans to communicate with other software can be less intuitive, but there are many advantages to this approach. First, you can integrate RPA with virtually any software you use, regardless of your openness to third-party applications. Many enterprise IT systems are proprietary because they do not have many common APIs, and their ability to communicate with other systems is severely limited, but RPA solves this problem. Second, RPA can be implemented in a very short time. Traditional software development methods, such as enterprise software integration, are relatively time consuming, but RPAs can be implemented in a relatively short period of two to four weeks. Third, automated processes through software robots can be easily modified by system users. While traditional approaches require advanced coding techniques to drastically modify how they work, RPA can be instructed by modifying relatively simple logical statements, or by modifying screen captures or graphical process charts of human-run processes. This makes RPA very versatile and flexible. This RPA is a good example of the application of digital to intelligence(D2I).

• Journal of Digital Convergence
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• v.15 no.12
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• pp.349-358
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• 2017

This study was done to identify the relationship between emotional intelligence, critical thinking disposition professional self-concept and problem solving skills of nursing students. Subjects were 212 associate nursing students associate nursing students(3rd and 4th grade) in Korea. The data were collected using self -report questionnaire from February 20 to March 30, 2017. Data were analyzed by frequencies, t-test, ANOVA, Pearson's correlation using SPSS Win 18.0. The score for emotional intelligence was 3.59, critical thinking disposition scoring 3.47, professional Self-Concept scoring 3.45 and problem solving skills scoring 3.45. Problem solving skills were significantly strong positive correlation with emotional intelligence(r=.68, p<.001), critical thinking disposition(r=.77, p<.001), Professional Self-Concept(r=.66, p<.001) in nursing students. Emotional intelligence, critical thinking disposition and professional self-concept explained 65.1% of total variance of problem solving skills of nursing students. Therefore, To increase problem solving skills of nursing students, it is necessary to develop and test the program for increase emotional intelligence, critical thinking disposition and professional self-concept of nursing students.

• The Journal of Information Systems
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• v.17 no.3
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• pp.255-287
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• 2008

This study examined the relationship among constructs that impact on strategic use of IS(Information Systems). Independent variables were OIQ(Organizational Intelligence Quotient) and role of CIO(Chief of Information Officer) as a top manager, and dependent variable are strategic use of IS. The dependent variable has three-sub constructs: 1) IT infrastructure flexibility; 2) operation-orientation; and 3) market-orientation. Seven research hypotheses derived from the research model, and were empirically tested using the PLS (Partial Least Squares) method. The research results confirmed that both OIQ and CIO's roles have strong impact on organizations' strategic use of IS. Communication and business network among the sub-constructs of OIQ have effect on strategic use of IS. CIO's role as a top manager was found to be significant. CIO's role as a resource allocator and innovator among the CIO's roles showed significant influence on strategic use of IS. OIQ was also significantly related to CIO's role as a top manager. This study suggests practical implications and insights to the enterprises which aim to apply IT strategically.

• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Clinical and Experimental Pediatrics
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• v.57 no.8
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• pp.363-369
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• 2014

Purpose: This study examines changes in developmental profiles of children with language delay over time and the clinical significance of assessment conducted at age 2-3 years. Methods: We retrospectively reviewed the medical records of 70 children (62 male, 8 female), who had visited the hospital because of delayed language development at 2-3 years, and were reassessed at ages 5-6. Language and cognitive abilities were assessed using multiple scales at the initial and follow-up visits. Results: At the initial test, 62 of the 70 children had mental development index (MDI) below 70 of Bayley Scales of Infant Development Test II. Of the 62 children in the follow-up assessment, 30 children (48.4%) remained within the same cognitive range (full-scale intelligence quotient, FSIQ<70 of Wechsler preschool and primary scale of intelligence), 12 had borderline intellectual functioning (FSIQ, 70-85), 6 improved to average intellectual functioning (FSIQ>85), and 5 had specific language impairment, 9 had autism spectrum disorders. At the initial test, 38 of the 70 children had cognitive developmental quotients (C-DQ) below 70. Of the 38 children in the follow-up assessment, 23 children (60.5%) remained within the same cognitive range (FSIQ<70). The correlation coefficient for MDI and FSIQ was 0.530 (P<0.0001) and that for C-DQ and FSIQ was 0.727 (P<0.0001). There was a strong correlation between C-DQ and FSIQ, and a moderate correlation between MDI and FSIQ. Conclusion: Low MDI scores reflect a specific delay in cognitive abilities, communication skills, or both. The C-DQ, receptive language development quotient, and social maturity quotient also help to distinguish between children with isolated language delay and children with cooccurring cognitive impairment. Moreover, changes in the developmental profile during preschool years are not unusual in children with language delay. Follow-up reassessments prior to the start of school are required for a more accurate diagnosis and intervention.

• Journal of Science and Technology Studies
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• v.1 no.2 s.2
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• pp.399-422
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• 2001

Models for human cognition currently discussed in cognitive science cannot be appropriate ones. The symbolic model of the traditional artificial intelligence works for reasoning and problem-solving tasks, but doesn't fit for pattern recognition such as letter/sound cognition. Connectionism shows the contrary phenomena to those of the traditional artificial intelligence. Connectionist systems has been shown to be very strong in the tasks of pattern recognition but weak in most of logical tasks. Brooks' situated action theory denies the. notion of representation which is presupposed in both the traditional artificial intelligence and connectionism and suggests a subsumption model which is based on perceptions coming from real world. However, situated action theory hasn't also been well applied to human cognition so far. In emphasizing those characteristics of models I refer those models 'left-brain model', 'right-brain model', and 'robot model' respectively. After I examine those models in terms of substantial items of cognitions- mental state, mental procedure, basic element of cognition, rule of cognition, appropriate level of analysis, architecture of cognition, I draw three arguments of embodiment. I suggest a way of unifying those existing models by examining their theoretical compatability which is found in those arguments.