• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.29-48
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• 1996

Previous studies on user training have largely focused on assessing models which describe the determinants of information technology usage or examined the effects of training on user satisfaction, productivity, performance, and so on. Scant research efforts have been made, however, to examine those effects of training by using theoretical models. This study presented a conceptual model to predict intention to use information technology and conducted an experiment to understand how training for computer skill acquisition affects primary variables of the model. The data were obtained from 32 student subjects of an experimental group and 31 students of a control group, and the information technology employed for this study was a university's electronic mail system. The study results revealed that attitude toward usage and perceived behavioral control helped to predict user intentions; outcome expectations were positively related to attitude toward usage; and self - efficacy and perceived behavioral control. The changes in those variables suggest more causal effects of user training than other survey studies.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• 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.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.57-67
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• 2024

This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.

• Journal of Korean Neurosurgical Society
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• v.64 no.6
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• pp.882-890
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• 2021

Objective : The aim of this study to investigate the benefits of patient-based 3-dimensional (3D) cerebral arteriovenous malformation (AVM) models for preoperative surgical planning and education. Methods : Fifteen patients were operated on for AVMs between 2015 and 2019 with patient-based 3D models. Ten patients' preoperative cranial angiogram screenings were evaluated preoperatively or perioperatively via patient-based 3D models. Two patients needed emergent surgical intervention; their models were solely designed based on their AVMs and used during the operation. However, the other patients who underwent elective surgery had the modeling starting from the skull base. These models were used both preoperatively and perioperatively. The benefits of patients arising from treatment with these models were evaluated via patient files and radiological data. Results : Fifteen patients (10 males and five females) between 16 and 66 years underwent surgery. The mean age of the patients was 40.0±14.72. The most frequent symptom patients observed were headaches. Four patients had intracranial bleeding; the symptom of admission was a loss of consciousness. Two patients (13.3%) belonged to Spetzler-Martin (SM) grade I, four (26.7%) belonged to SM grade II, eight (53.3%) belonged to SM grade III, and one (6.7%) belonged to SM grade IV. The mean operation duration was 3.44±0.47 hours. Three patients (20%) developed transient neurologic deficits postoperatively, whereas three other patients died (20%). Conclusion : Several technological innovations have emerged in recent years to reduce undesired outcomes and support the surgical team. For example, 3D models have been employed in various surgical procedures in the last decade. The routine usage of patient-based 3D models will not only support better surgical planning and practice, but it will also be useful in educating assistants and explaining the situation to the patient as well.

• Archives of Plastic Surgery
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• v.45 no.5
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• pp.395-402
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• 2018

Increased emphasis on competency-based learning modules and widespread departure from traditional models of Halstedian apprenticeship have made surgical simulation an increasingly appealing component of medical education. Surgical simulators are available in numerous modalities, including virtual, synthetic, animal, and non-living models. The ideal surgical simulator would facilitate the acquisition and refinement of surgical skills prior to clinical application, by mimicking the size, color, texture, recoil, and environment of the operating room. Simulation training has proven helpful for advancing specific surgical skills and techniques, aiding in early and late resident learning curves. In this review, the current applications and potential benefits of incorporating simulation-based surgical training into residency curriculum are explored in depth, specifically in the context of plastic surgery. Despite the prevalence of simulation-based training models, there is a paucity of research on integration into resident programs. Current curriculums emphasize the ability to identify anatomical landmarks and procedural steps through virtual simulation. Although transfer of these skills to the operating room is promising, careful attention must be paid to mastery versus memorization. In the authors' opinions, curriculums should involve step-wise employment of diverse models in different stages of training to assess milestones. To date, the simulation of tactile experience that is reminiscent of real-time clinical scenarios remains challenging, and a sophisticated model has yet to be established.

• English Language & Literature Teaching
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• v.16 no.3
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• pp.269-289
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• 2010

The present study reports on a study of the development of customized intensive in-service English teachers training programs (IIETTP) reflecting on the demands of elementary/secondary school English teachers. For the purpose of study, a survey was conducted with 1,033 English teachers at elementary/secondary schools across the country. The results showed by and large no significant differences by school level, albeit some slight differences were revealed such as in training times, training methods, the percentages of teaching English in English (TEE), and other things. Since the two IIETTP models are presented as basic formats, they can be modified and applied according to the contexts of schools and the demands of trainees.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• International Journal of Quality Innovation
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• v.7 no.1
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• pp.1-19
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• 2006

This study sought to examine the effect of three (distributive, procedural, and interactional) justice perceptions in predicting employees' motivation to participate in training activities. On the basis of theoretical linkages between the constructs, full mediation and partial mediation models by perceived benefits of training were developed. The models were tested using SEM (Structural Equation Modeling) on responses from 302 employees of three wireless operators in the Republic of Korea. The results showed the partial-mediation model is a dominant model. It also confirmed that interactional justice directly influence motivation to participate in training, whereas procedural justice influence the variable through perceived benefits of training. Furthermore, limitations and implications of the current study and directions for future work are discussed.

• Archives of Plastic Surgery
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• v.40 no.4
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• pp.312-319
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• 2013

Over the past decade, driven by advances in educational theory and pressures for efficiency in the clinical environment, there has been a shift in surgical education and training towards enhanced simulation training. Microsurgery is a technical skill with a steep competency learning curve on which the clinical outcome greatly depends. This paper investigates the evidence for educational and training interventions of traditional microsurgical skills courses in order to establish the best evidence practice in education and training and curriculum design. A systematic review of MEDLINE, EMBASE, and PubMed databases was performed to identify randomized control trials looking at educational and training interventions that objectively improved microsurgical skill acquisition, and these were critically appraised using the BestBETs group methodology. The databases search yielded 1,148, 1,460, and 2,277 citations respectively. These were then further limited to randomized controlled trials from which abstract reviews reduced the number to 5 relevant randomised controlled clinical trials. The best evidence supported a laboratory based low fidelity model microsurgical skills curriculum. There was strong evidence that technical skills acquired on low fidelity models transfers to improved performance on higher fidelity human cadaver models and that self directed practice leads to improved technical performance. Although there is significant paucity in the literature to support current microsurgical education and training practices, simulated training on low fidelity models in microsurgery is an effective intervention that leads to acquisition of transferable skills and improved technical performance. Further research to identify educational interventions associated with accelerated skill acquisition is required.