• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.145-151
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• 2023

This study was attempted to provide basic data for the development of the nursing major curriculum and the admission strategy for attracting new students by identifying the perception of the nursing major among freshmen in the nursing department. Participants in this study were 40 freshmen who agreed to participate in the study among freshmen in the department of nursing who completed self-exploration, a required liberal arts course for freshmen opened in the first semester of 2021. Data collection was a study diary written by participants after 15 weeks of class, and the traditional content analysis method suggested by Heieh and Shannon was applied to data analysis. As a result of the study, three themes were derived: 'motivation for entering the nursing major', 'value of the nursing major', and 'obstacles to the nursing major'. Therefore, colleges and departments need to strengthen their entrance examination strategies to develop and conduct field trip programs for experiential departments linked to middle and high schools and It is necessary to solve the difficulties in taking major courses by providing subject and extracurricular programs targeting students who lack basic learning ability.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.159-166
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• 2023

Humanities are as crucial as the technology itself in the intelligent information society. Human-centered convergence information technology (IT), which reflects emotional and human nature, can be considered a unique technology with an optimistic outlook in the unpredictable future. Based on this research background, this paper proposed an education model that can improve the IT humanities capabilities of various learners, including elementary and secondary students, prospective teachers, incumbent teachers, school managers, and the general public, through analysis of previous studies on convergence education models. Furthermore, the practical aspects of the proposed model were closely examined so that the proposed education model could be stably incorporated and utilized in the educational field. There are seven strategies for implementing the education model proposed in this paper, including research on textbooks, teaching and learning materials, activation of research results, maker space creation, global joint research, online education operation, developing living lab governance, and diversification of self-sustaining platforms for sustainable and practical education. In the future, validity verification through expert Delphi is required as a follow-up study.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.289-296
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• 2023

The purpose of this study aims to identify the key factors that contribute to successful online learning experiences for college students in the pre-course, during course, and post-course phases. A survey was conducted college students, and a total of 95 questionnaires were used for statistical analysis. The main findings revealed that in the pre-course phase, task value, academic self-efficacy, and control beliefs were significant factors. During course, interaction emerged as a crucial factor. Notably, students' satisfaction in the post-course phase is significantly influenced by academic self-efficacy and interaction. Understanding these factors will help inform the design and operation of effective college-level online courses to improve student experience and satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.563-571
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• 2023

The purpose of the study is to understand the impact of nursing college students' awareness and self-esteem on nursing professionals. The research design of this study is a descriptive investigative study using convenient samples. The data collection collected structured questionnaires and Google's online survey methods for first- to fourth-year nursing college students at three universities in North Chungcheong Province. The collected data were analyzed using the SPSS window 25.0 program as frequency, percentage, mean and standard deviation, t-test and one-way ANOVA, and post-test as Scheffétest, Pearson correlation coefficient, and multiple regression. The study found that 21.7% (==-.181, p<.001), 2.8% major satisfaction, and 24.5% (β=.420, p<.001), so it is recommended to use it as basic data to establish a curriculum and teaching learning strategy to improve major satisfaction.

• Korean Journal of Veterinary Service
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• v.46 no.4
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• pp.293-302
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• 2023

This study provides a comprehensive analysis of the epidemiological trends and challenges in managing tuberculosis (TB) in livestock in Korea from 2013 to 2022. Tuberculosis, caused by the Mycobacterium tuberculosis complex, is a significant zoonotic disease affecting cattle, deer, and other domesticated animals. Despite the initiation of a test-and-slaughter eradication policy in 1964, TB has continued to persist in Korean livestock, particularly in cattle and deer. This study used data from the Korea Animal Health Integrated System and provincial animal health laboratories to analyze TB incidence in various livestock including different cattle breeds and deer species. The results from 2013 to 2022 showed a peak in TB cases in 2019 with a subsequent decline by 2022. The study highlighted a significant incidence of TB in Korean native cattle and the need for amore inclusive approach towards TB testing and control in different cattle breeds. Additionally, the study underscored the importance of addressing TB in other animals such as goats, wildlife, and companion animals for a holistic approach to TB eradication in Korea. The findings suggest that while the test-and-slaughter strategy has been historically effective, there is a need for adaptation to the current challenges, and learning from successful eradiation stories on other countries like Australia. A collaborative effort involving an expanded surveillance system, active private sector participation, and robust government support essential for the efficient eradication of TB in livestock in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.177-188
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• 2024

In this research, a cohort of two children, aged 7-8 years, was selected to participate in a specialized three-week training program aimed at enhancing their working memory. The program consisted of three sessions, each lasting approximately 30 minutes. The primary goal was to investigate the impact and developmental trajectory of working memory in school-aged children. Working memory plays a significant role in young children's learning and daily activities. To address the needs of this demographic, products should offer both educational and enjoyable activities that engage working memory. Digital educational tools, known for their flexibility, are suitable for both older individuals and young children. By updating software or modifying content, these tools can be effectively repurposed for young learners without extensive hardware changes, making them both cost-effective and practical. For example, memory training games initially designed for older adults can be adapted for young children by altering images, music, or storylines. Furthermore, incorporating elements familiar to children, like animals, toys, or fairy tales, can increase their engagement in these activities. Historically, working memory capabilities have been assessed predominantly through traditional intelligence tests. However, recent research questions the adequacy of these behavioral measures in accurately detecting changes in working memory. To bridge this gap, the current study utilized electroencephalography (EEG) as a more sophisticated and precise tool for monitoring potential changes in working memory after the training. The research findings were revealing. Participants showed marked improvement in their performance on n-back tasks, a standard measure for evaluating working memory. This improvement post-training strongly supports the effectiveness of the training program. The results indicate that such targeted and structured training programs can significantly enhance the working memory abilities of children in this age group, providing promising implications for educational strategies and cognitive development interventions.